Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population.

Code ↔ Paper

13 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 13 matches

[1] § METHODS › Clinical risk scores › Cognitive Health and Dementia Risk Reduction ↔ workflow/scripts/imputation.qmd, lines 55–114 · score 0.78 · atrial fibrillation, cognitive engagement, social support, insomnia, SD, obesity
[2] § METHODS › Clinical risk scores › Cognitive Health and Dementia Risk Reduction ↔ workflow/scripts/crs_analysis.qmd, lines 77–136 · score 0.77 · atrial fibrillation, cognitive engagement, social support, insomnia, obesity, stroke
[3] § METHODS › Statistical analysis › AD Endophenotypes ↔ workflow/scripts/crs_analysis.qmd, lines 1797–1943 · score 0.69 · cognitive function, plasma biomarkers, CogDrisk, ratio, hippocampal, executive
[4] § RESULTS › Higher clinical risk burden was associated with worsening AD endophenotypes › CogDRisk ↔ workflow/scripts/crs_analysis.qmd, lines 1758–1795 · score 0.68 · cortical thickness, hippocampal volume, verbal ability, executive function, CogDRisk
[5] § METHODS › Health and Aging Brain Study–Health Disparities › Endophenotype standardization ↔ workflow/scripts/crs_standardization.qmd, lines 199–252 · score 0.66 · composite scores, delayed, WMS, Animal, Digit, FAS
[6] § RESULTS › Higher clinical risk burden was associated with worsening AD endophenotypes › mCAIDE ↔ workflow/scripts/crs_analysis.qmd, lines 1758–1795 · score 0.58 · cortical thickness, hippocampal volumes, verbal ability, mCAIDE, race
[7] § METHODS › Health and Aging Brain Study–Health Disparities › Endophenotype standardization ↔ workflow/scripts/crs_standardization.qmd, lines 254–371 · score 0.57 · log transformed, raw, outliers, HABS, Plasma, NfL
[8] § METHODS › Statistical analysis › Prediction of pTau217/Aβ42 amyloid positivity by CRS ↔ workflow/scripts/cutoff_analysis.qmd, lines 25–44 · score 0.57 · optimal cutpoint, Youden, bootstrapping, pTau217, threshold
[9] § METHODS › Clinical risk scores › Lifestyle for Brain Health ↔ workflow/scripts/crs_analysis.qmd, lines 186–256 · score 0.56 · heart diseases, AUDIT, alcohol, renal, physical, RAPA
[10] § METHODS › Clinical risk scores › Lifestyle for Brain Health ↔ workflow/scripts/imputation.qmd, lines 164–233 · score 0.56 · heart diseases, AUDIT, alcohol, renal, physical, RAPA
[11] § METHODS › Statistical analysis › Cognitive impairment ↔ workflow/scripts/crs_analysis.qmd, lines 2227–2312 · score 0.54 · Logistic regression, CogDRisk, squared, Nagelkerke, mCAIDE, MCI
[12] § METHODS › Health and Aging Brain Study–Health Disparities › Endophenotype standardization ↔ workflow/scripts/crs_standardization.qmd, lines 376–438 · score 0.54 · Cortical thickness, entorhinal, fusiform, inferior, Hippocampal, AD
[13] § RESULTS › Higher clinical risk burden was associated with worsening AD endophenotypes › Lifestyle for Brain Health ↔ workflow/scripts/crs_analysis.qmd, lines 1797–1943 · score 0.53 · plasma biomarkers, hyperintensity, population, hippocampal, executive, neuroimaging

Paper

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The authors' code

Quarto · 2,407 lines · 113 KB · MIT · 7 matches

---
title: "CRS analysis"
author: "Meri Okorie"
date: "2025-12-10"
output: html_document
---
### Setup
```{r}
library(ggplot2)
library(dplyr)
library(tidyr)
library(pROC)
library(PRROC)
library(readr)
library(Metrics)
library(r2redux)
library(ggalluvial)
library(glue)
library(broom)
library(purrr)
library(tidyverse)
library(scales)
library(rcompanion)
library(plotgardener)
library(cowplot)
library(parameters)
library(mice)
library(missForest)
library(rcompanion)
library(forcats)
```
### Imputation
```{r}
#former smoker
data_org$smoke_former = ifelse(data_org$substance_use_smoke_end_age < data_org$age, 1, 0)
data_org$smoke_former[is.na(data_org$smoke_former)] = 0
new_data_cols = c("age", "sex", "id_education", "id_income", "id_language_primary", "bmi", "imh_diabetes", "imh_stroke", "imh_tbi",
"cdx_hypertension", "cdx_depression", "rapa_1_1", "rapa_1_2", "rapa_1_6", "rapa_1_7", "smoke_ever", "smoke_former", "smoke_currently",
"social_support_total", "audit_1", "eGFR", "imh_high_cholesterol", "bw_hdl_chol", "bw_chol_total", "om_bp1_sys", "om_bp2_sys",
"rapa_1_total", "rapa_2_total", "race", "cdr", "hc_insurance_no_insurance", "health_status", "prs_z_prscsx", "PC1", "PC2", "PC3", "PC4")
vars = c("bmi", "imh_diabetes", "imh_tbi", "rapa_1_1", "rapa_1_2", "rapa_1_6", "rapa_1_total", "rapa_2_total",
"rapa_1_7", "smoke_ever", "smoke_currently", "social_support_total", "audit_1",
"eGFR", "bw_hdl_chol", "om_bp1_sys", "om_bp2_sys", "bw_chol_total")
data_for_imp_fixed = data_org %>%
select(all_of(new_data_cols)) %>%
mutate(across(where(is.character), as.factor)) %>%
mutate(across(where(~ is.numeric(.) && n_distinct(., na.rm = TRUE) == 2), ~ as.factor(.))) %>%
as.data.frame()
set.seed(123)
imp_mf = missForest(data_for_imp_fixed, maxiter = 10, ntree = 100)
data_imp = imp_mf$ximp
data_imp_values = imp_mf$ximp
data_imputed = data_org
data_imputed[vars] = data_imp_values[vars]
plot(density(data_org$rapa_2_total, na.rm = T), xlab = "BMI", lwd = 2, col = "black")
lines(density(data_imputed$rapa_2_total), col = "red", lty = 3, lwd = 2)
# CogD
threshold_ss = quantile(data_imputed$social_support_total, 0.15, na.rm = TRUE)
habs_imp = data_imputed %>%
mutate(
bw_chol_total_mmoll = bw_chol_total / 38.67
)
data.cogd_imp = habs_imp %>% # insomnia, fish consumption, atrial fibrillation, and cognitive engagement not measured
mutate(
cogd_age = case_when(
sex == 0 & age < 60 ~ 0,
sex == 0 & age >= 60 & age <= 64 ~ 0,
sex == 0 & age >= 65 & age <= 69 ~ 5,
sex == 0 & age >= 70 & age <= 74 ~ 8,
sex == 0 & age >= 75 & age <= 79 ~ 12,
sex == 0 & age >= 80 & age <= 84 ~ 17,
sex == 0 & age >= 85 & age <= 89 ~ 20,
sex == 0 & age >= 90 ~ 22,
sex == 1 & age < 60 ~ 0,
sex == 1 & age >= 60 & age <= 64 ~ 0,
sex == 1 & age >= 65 & age <= 69 ~ 5,
sex == 1 & age >= 70 & age <= 74 ~ 7,
sex == 1 & age >= 75 & age <= 79 ~ 13,
sex == 1 & age >= 80 & age <= 84 ~ 16,
sex == 1 & age >= 85 & age <= 89 ~ 19,
sex == 1 & age >= 90 ~ 23,
TRUE ~ NA_real_
),
cogd_edu = case_when(
id_education > 11 ~ 0,
id_education >= 8 & id_education <= 11 ~ 2,
id_education < 8 ~ 4,
TRUE ~ NA_real_
),
cogd_obesity = case_when( # only assigned for patients under the age of 65
bmi >= 18.5 & bmi <= 25 ~ 0, # normal
bmi > 25 & bmi <= 30 ~ 1, # overweight
bmi < 18.5 ~ 3, # underweight
bmi > 30 ~ 2, # obese
TRUE ~ NA_real_
),
cogd_chol = case_when(
bw_chol_total_mmoll < 6.5 ~ 0,
bw_chol_total_mmoll >= 6.5 ~ 3,
TRUE ~ NA_real_
),
cogd_diabetes = case_when(
imh_diabetes == 0 ~ 0,
sex == 0 & imh_diabetes == 1 ~ 2,
sex == 1 & imh_diabetes == 1 ~ 3,
TRUE ~ NA_real_
),
cogd_stroke = case_when(
imh_stroke == 0 ~ 0,
imh_stroke == 1 ~ 2,
TRUE ~ NA_real_
),
cogd_tbi = case_when(
imh_tbi == 0 ~ 0,
imh_tbi == 1 ~ 1,
TRUE ~ NA_real_
),
cogd_hypertension = case_when(
cdx_hypertension == 0 ~ 0,
cdx_hypertension == 1 ~ 1,
TRUE ~ NA_real_
),
cogd_depression = case_when(
cdx_depression == 0 ~ 0,
cdx_depression == 1 ~ 4,
TRUE ~ NA_real_
),
cogd_physical = case_when(
rapa_1_6 == 1 | rapa_1_7 == 1 ~ -3,
is.na(rapa_1_6) & is.na(rapa_1_7) ~ NA_real_,
TRUE ~ 0
),
cogd_smoke = case_when(
smoke_ever == 0 ~ 0,
smoke_former == 0 ~ 0,
smoke_former == 1 ~ 0.2,
smoke_currently == 0 ~ 0,
smoke_currently == 1 ~ 2,
TRUE ~ NA_real_
),
cogd_social = case_when(
social_support_total >= threshold_ss ~ 2,
social_support_total < threshold_ss ~ 0,
TRUE ~ NA_real_
)
) %>%
rowwise() %>%
mutate(
# Compute cogd score (else condition) and set it to NA if any variable is NA (if condition)
cogd = if(any(is.na(c(cogd_age, cogd_edu, cogd_obesity, cogd_chol, cogd_diabetes, cogd_stroke, cogd_tbi,
cogd_hypertension, cogd_depression, cogd_physical, cogd_smoke, cogd_social)))) {
NA_real_
} else {
sum(c(cogd_age, cogd_edu, cogd_obesity, cogd_chol, cogd_diabetes, cogd_stroke, cogd_tbi,
cogd_hypertension, cogd_depression, cogd_physical, cogd_smoke, cogd_social), na.rm = TRUE)
}
) %>%
ungroup()
# mutate(cogd_noedu = ifelse(!is.na(cogd) & !is.na(cogd_edu), cogd - cogd_edu, NA_real_))
# Libra
data.libra_imp = data.cogd_imp %>%
mutate(
libra_obesity = case_when( # only assigned for patients under the age of 65
bmi < 18.5 ~ 0,
bmi >= 18.5 & bmi < 25 ~ 0, # normal
bmi >= 25 & bmi < 30 ~ 1.6, # overweight
bmi >= 30 ~ 1.6, # obese
TRUE ~ NA_real_
),
libra_chol = case_when(
bw_chol_total_mmoll < 6.5 ~ 0,
bw_chol_total_mmoll >= 6.5 ~ 1.4,
TRUE ~ NA_real_
),
libra_diabetes = case_when(
imh_diabetes == 0 ~ 0,
imh_diabetes == 1 ~ 1.3,
TRUE ~ NA_real_
),
libra_hypertension = case_when(
cdx_hypertension == 0 ~ 0,
cdx_hypertension == 1 ~ 1.6,
TRUE ~ NA_real_
),
libra_depression = case_when(
cdx_depression == 0 ~ 0,
cdx_depression == 1 ~ 2.1,
TRUE ~ NA_real_
),
libra_physical = case_when( # 0.27% missigness
rapa_1_1 == 1 | rapa_1_2 == 1 ~ 1.1,
is.na(rapa_1_1) & is.na(rapa_1_2) ~ NA_real_,
TRUE ~ 0
),
libra_smoke = case_when(
smoke_currently == 1 ~ 1.5,
smoke_currently == 0 ~ 0,
TRUE ~ NA_real_
),
libra_alcohol = case_when(
audit_1 == 3 ~ -1.0,
!is.na(audit_1) ~ 0, # for all other non-missing values
TRUE ~ NA_real_
),
libra_renal = case_when(
eGFR < 60 ~ 1.1,
eGFR >= 60 ~ 0,
TRUE ~ NA_real_
),
libra_heart = case_when(
cdp_heart_disease == 1 | imh_stroke == 1 | cdp_myocardial == 1 ~ 1.0,
is.na(cdp_heart_disease) & is.na(imh_stroke) & is.na(cdp_myocardial) ~ NA_real_,
TRUE ~ 0.0
)) %>%
rowwise() %>%
mutate(
libra = if (any(is.na(c_across(c(
libra_obesity, libra_chol, libra_diabetes, libra_hypertension, libra_depression,
libra_physical, libra_smoke, libra_alcohol, libra_renal, libra_heart
))))) {
NA_real_
} else {
sum(c_across(c(
libra_obesity, libra_chol, libra_diabetes, libra_hypertension, libra_depression,
libra_physical, libra_smoke, libra_alcohol, libra_renal, libra_heart
)))
}
) %>%
ungroup()
#mCaide
data.libra_imp = data.libra_imp %>%
mutate(rapa_total = rapa_1_total + rapa_2_total)
threshold_rapa = quantile(data.libra_imp$rapa_total, probs = 1/3, na.rm = TRUE)
data.libra_imp$om_bp_sys_avg = (data.libra_imp$om_bp1_sys + data.libra_imp$om_bp2_sys) / 2
data.caide_imp = data.libra_imp %>%
mutate(
mcaide_age = case_when(
age <= 64 ~ 0,
age >= 65 & age < 73 ~ 1,
age >= 73 ~ 2,
TRUE ~ NA_real_
),
mcaide_educ = case_when(
id_education < 12 ~ 2,
id_education >= 12 & id_education <= 16 ~ 1,
id_education > 16 ~ 0,
TRUE ~ NA_real_
),
mcaide_sex = case_when(
sex == 0 ~ 1,
sex == 1 ~ 0,
TRUE ~ NA_real_
),
mcaide_bmi = case_when(
bmi <= 30 ~ 0,
bmi > 30 ~ 2,
TRUE ~ NA_real_
),
mcaide_sbp = case_when(
om_bp_sys_avg < 140 ~ 0,
om_bp_sys_avg >= 140 ~ 2,
TRUE ~ NA_real_
),
mcaide_chol = case_when(
imh_high_cholesterol == 0 ~ 0,
imh_high_cholesterol == 1 ~ 2,
TRUE ~ NA_real_
),
mcaide_phy = case_when(
rapa_total < threshold_rapa ~ 2,
rapa_total >= threshold_rapa ~ 0,
TRUE ~ NA_real_
),
) %>%
rowwise() %>%
mutate(
mcaide = if(any(is.na(c(mcaide_age, mcaide_educ, mcaide_sex, mcaide_bmi, mcaide_sbp, mcaide_chol, mcaide_phy)))) {
NA_real_
} else {
sum(c(mcaide_age, mcaide_educ, mcaide_sex, mcaide_bmi, mcaide_sbp, mcaide_chol, mcaide_phy), na.rm = TRUE)
}
) %>%
ungroup()
# WHICAP
data_all_imp = data.caide_imp %>%
mutate(
whicap_sex = case_when(
sex == 0 ~ 0,
sex == 1 ~ 1,
TRUE ~ NA_real_
),
whicap_age = case_when(
age <= 70 ~ 0,
age > 70 & age <= 75 ~ 6,
age > 75 & age <= 80 ~ 8,
age > 80 & age <= 85 ~ 13,
age > 85 ~ 21,
TRUE ~ NA_real_
),
whichap_diabetes = case_when(
cdx_diabetes == 0 ~ 0,
cdx_diabetes ==1 ~ 3,
TRUE ~ NA_real_
),
whicap_hypertension = case_when(
cdx_hypertension == 0 ~ 0,
cdx_hypertension == 1 ~ 1,
TRUE ~ NA_real_
),
whicap_smoker = case_when(
smoke_currently == 0 ~ 0,
smoke_currently == 1 ~ 5,
TRUE ~ NA_real_
),
whicap_cholesterol = case_when(
sex == 1 & bw_hdl_chol < 50 ~ 3,
sex == 1 & bw_hdl_chol >= 50 ~ 0,
sex == 0 & bw_hdl_chol < 40 ~ 3,
sex == 0 & bw_hdl_chol >= 40 ~ 0,
TRUE ~ NA_real_
),
whicap_bmi = case_when(
bmi > 25 ~ 7,
bmi <= 25 ~ 0,
TRUE ~ NA_real_
),
whicap_edu = case_when(
id_education > 9 ~ 0,
id_education >= 7 & id_education <= 9 ~ 8,
id_education <= 6 ~ 11,
TRUE ~ NA_real_
),
whicap_race = case_when(
race == "NHW" ~ 0,
race == "Hispanic" ~ 4,
race == "Black" ~ 5,
TRUE ~ NA_real_
)) %>%
rowwise() %>%
mutate(
whicap = if(any(is.na(c(whicap_sex, whicap_age, whichap_diabetes, whicap_hypertension, whicap_smoker,
whicap_cholesterol, whicap_bmi, whicap_edu, whicap_race)))) {
NA_real_
} else {
sum(c(whicap_sex, whicap_age, whichap_diabetes, whicap_hypertension, whicap_smoker,
whicap_cholesterol, whicap_bmi, whicap_edu, whicap_race), na.rm = TRUE)
}
) %>%
ungroup()
```
```{r}
dat = data_all_imp %>%
as.data.frame() %>%
mutate(
cdx_cn_vs_ci = case_when(
cdx_cog == 0 ~ 1,
cdx_cog %in% c(1, 2) ~ 0
),
cdx_mci_vs_cn = case_when(
cdx_cog == 0 ~ 0,
cdx_cog == 1 ~ 1,
TRUE ~ NA # drop dementia
),
cdx_dem_vs_cn = case_when(
cdx_cog == 0 ~ 0,
cdx_cog == 2 ~ 1,
TRUE ~ NA # drop MCI
),
cdx_ci_vs_cn = case_when(
cdx_cog == 0 ~ 0,
cdx_cog %in% c(1, 2) ~ 1, # MCI + dementia
TRUE ~ NA
)
)
data = dat %>%
rowwise() %>%
mutate(
cogd_sva = if (any(is.na(c_across(c(
cogd_obesity, cogd_chol, cogd_diabetes, cogd_stroke, cogd_tbi, cogd_hypertension, cogd_depression, cogd_physical, cogd_smoke, cogd_social
))))) {
NA_real_
} else {
sum(c_across(c(
cogd_obesity, cogd_chol, cogd_diabetes, cogd_stroke, cogd_tbi, cogd_hypertension, cogd_depression, cogd_physical, cogd_smoke, cogd_social
)))
},
mcaide_sva = if (any(is.na(c_across(c(mcaide_bmi, mcaide_sbp, mcaide_chol, mcaide_phy))))) {
NA_real_
} else {
sum(c_across(c(mcaide_bmi, mcaide_sbp, mcaide_chol, mcaide_phy)))
},
whicap_sva = if (any(is.na(c_across(c(
whichap_diabetes, whicap_hypertension, whicap_smoker, whicap_cholesterol, whicap_bmi, whicap_race
))))) {
NA_real_
} else {
sum(c_across(c(
whichap_diabetes, whicap_hypertension, whicap_smoker, whicap_cholesterol, whicap_bmi, whicap_race
)))
}
) %>%
mutate(
libra_age_sva = case_when(
sex == 0 & age < 65 ~ 0,
sex == 0 & age >= 65 & age < 69 ~ 0.13,
sex == 0 & age >= 70 & age <= 74 ~ 1.57,
sex == 0 & age >= 75 & age <= 79 ~ 2.04,
sex == 0 & age >= 80 & age <= 84 ~ 3.37,
sex == 0 & age >= 85 & age <= 89 ~ 4.24,
sex == 0 & age >= 90 ~ 4.93,
sex == 1 & age < 65 ~ 0,
sex == 1 & age >= 65 & age <= 69 ~ 0.64,
sex == 1 & age >= 70 & age <= 74 ~ 1.87,
sex == 1 & age >= 75 & age <= 79 ~ 2.75,
sex == 1 & age >= 80 & age <= 84 ~ 3.71,
sex == 1 & age >= 85 & age <= 89 ~ 4.58,
sex == 1 & age >= 90 ~ 5.28,
TRUE ~ NA_real_
),
libra_edu_sva = case_when(
id_education > 11 ~ 0,
id_education >= 8 & id_education >= 11 ~ 0.42,
id_education < 8 ~ 0.80,
TRUE ~ NA_real_
)) %>%
rowwise() %>%
mutate(
libra_sva = if (any(is.na(c_across(c(
libra_age_sva, libra_edu_sva, libra_obesity, libra_chol, libra_diabetes, libra_hypertension,
libra_depression, libra_physical, libra_smoke, libra_alcohol, libra_renal, libra_heart
))))) {
NA_real_
} else {
sum(c_across(c(
libra_age_sva, libra_edu_sva, libra_obesity, libra_chol, libra_diabetes, libra_hypertension,
libra_depression, libra_physical, libra_smoke, libra_alcohol, libra_renal, libra_heart
)))
}
) %>%
ungroup() %>%
rename_with(~ paste0(.x, "_org"),
.cols = c(cogd, mcaide, libra, whicap,
cogd_sva, mcaide_sva, libra_sva, whicap_sva)) %>%
mutate(across(ends_with("_org"),
~ as.numeric(scale(.)),
.names = "{sub('_org$', '', .col)}"))
```
## Regression
### Linear - All
```{r}
all_num_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'ef', 'all', 'base', 'interview_language + age + sex + id_education + apoe',
'em', 'all', 'base', 'interview_language + age + sex + id_education + apoe',
'va', 'all', 'base', 'interview_language + age + sex + id_education + apoe',
'mmse_total', 'all', 'base', 'apoe + age + sex + id_education + interview_language',
'cdr', 'all', 'base', 'apoe + age + sex + id_education + interview_language',
'ab40', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42_ab40', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'tau', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'ptau', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'nfl', 'all', 'base', 'apoe + age + sex + bmi + eGFR',
'meta_roi', 'all', 'base', 'apoe + age + sex + icv',
'z_hippcampul_vol', 'all', 'base', 'apoe + age + sex + icv',
'wmh_volume_log', 'all', 'base', 'apoe + age + sex + icv',
#Demographics - apoe
'ef', 'all', 'base_noapoe', 'interview_language + age + sex + id_education',
'em', 'all', 'base_noapoe', 'interview_language + age + sex + id_education',
'va', 'all', 'base_noapoe', 'interview_language + age + sex + id_education',
'mmse_total', 'all', 'base_noapoe', 'age + sex + id_education + interview_language',
'cdr', 'all', 'base_noapoe', 'age + sex + id_education + interview_language',
'ab40', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'ab42', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'ab42_ab40', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'tau', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'ptau', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'nfl', 'all', 'base_noapoe', 'age + sex + bmi + eGFR',
'meta_roi', 'all', 'base_noapoe', 'age + sex + icv',
'z_hippcampul_vol', 'all', 'base_noapoe', 'age + sex + icv',
'wmh_volume_log', 'all', 'base_noapoe', 'age + sex + icv',
#mCAIDE
'ef', 'all', 'mcaide', 'interview_language + apoe + mcaide',
'em', 'all', 'mcaide', 'interview_language + apoe + mcaide',
'va', 'all', 'mcaide', 'interview_language + apoe + mcaide',
'mmse_total', 'all', 'mcaide', 'interview_language + apoe + mcaide',
'cdr', 'all', 'mcaide', 'interview_language + apoe + mcaide',
'ab40', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'ab42', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'ab42_ab40', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'tau', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'ptau', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'nfl', 'all', 'mcaide', 'apoe + eGFR + mcaide',
'meta_roi', 'all', 'mcaide', 'apoe + icv + mcaide',
'z_hippcampul_vol', 'all', 'mcaide', 'apoe + icv + mcaide',
'wmh_volume_log', 'all', 'mcaide', 'apoe + icv + mcaide',
# LIBRA
'ef', 'all', 'libra', 'interview_language + apoe + libra',
'em', 'all', 'libra', 'interview_language + apoe + libra',
'va', 'all', 'libra', 'interview_language + apoe + libra',
'mmse_total', 'all', 'libra', 'interview_language + apoe + libra',
'cdr', 'all', 'libra', 'interview_language + apoe + libra',
'ab40', 'all', 'libra', 'apoe + libra',
'ab42', 'all', 'libra', 'apoe + libra',
'ab42_ab40', 'all', 'libra', 'apoe + libra',
'tau', 'all', 'libra', 'apoe + libra',
'ptau', 'all', 'libra', 'apoe + libra',
'nfl', 'all', 'libra', 'apoe + libra',
'meta_roi', 'all', 'libra', 'apoe + icv + libra',
'z_hippcampul_vol', 'all', 'libra', 'apoe + icv + libra',
'wmh_volume_log', 'all', 'libra', 'apoe + icv + libra',
# WHICAP
'ef', 'all', 'whicap', 'interview_language + apoe + whicap',
'em', 'all', 'whicap', 'interview_language + apoe + whicap',
'va', 'all', 'whicap', 'interview_language + apoe + whicap',
'mmse_total', 'all', 'whicap', 'interview_language + apoe + whicap',
'cdr', 'all', 'whicap', 'interview_language + apoe + whicap',
'ab40', 'all', 'whicap', 'apoe + eGFR + whicap',
'ab42', 'all', 'whicap', 'apoe + eGFR + whicap',
'ab42_ab40', 'all', 'whicap', 'apoe + eGFR + whicap',
'tau', 'all', 'whicap', 'apoe + eGFR + whicap',
'ptau', 'all', 'whicap', 'apoe + eGFR + whicap',
'nfl', 'all', 'whicap', 'apoe + eGFR + whicap',
'meta_roi', 'all', 'whicap', 'apoe + icv + whicap',
'z_hippcampul_vol', 'all', 'whicap', 'apoe + icv + whicap',
'wmh_volume_log', 'all', 'whicap', 'apoe + icv + whicap',
# CogD
'ef', 'all', 'cogd', 'interview_language + apoe + cogd',
'em', 'all', 'cogd', 'interview_language + apoe + cogd',
'va', 'all', 'cogd', 'interview_language + apoe + cogd',
'mmse_total', 'all', 'cogd', 'interview_language + apoe + cogd',
'cdr', 'all', 'cogd', 'interview_language + apoe + cogd',
'ab40', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42_ab40', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'tau', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'ptau', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'nfl', 'all', 'cogd', 'apoe + bmi + eGFR + cogd',
'meta_roi', 'all', 'cogd', 'apoe + icv + cogd',
'z_hippcampul_vol', 'all', 'cogd', 'apoe + icv + cogd',
'wmh_volume_log', 'all', 'cogd', 'apoe + icv + cogd',
# Sensitivity analysis
#LIBRA
'ef', 'all', 'libra_sva', 'interview_language + apoe + libra_sva',
'em', 'all', 'libra_sva', 'interview_language + apoe + libra_sva',
'va', 'all', 'libra_sva', 'interview_language + apoe + libra_sva',
'mmse_total', 'all', 'libra_sva', 'interview_language + apoe + libra_sva',
'cdr', 'all', 'libra_sva', 'interview_language + apoe + libra_sva',
'ab40', 'all', 'libra_sva', 'apoe + libra_sva',
'ab42', 'all', 'libra_sva', 'apoe + libra_sva',
'ab42_ab40', 'all', 'libra_sva', 'apoe + libra_sva',
'tau', 'all', 'libra_sva', 'apoe + libra_sva',
'ptau', 'all', 'libra_sva', 'apoe + libra_sva',
'nfl', 'all', 'libra_sva', 'apoe + libra_sva',
'meta_roi', 'all', 'libra_sva', 'apoe + icv + libra_sva',
'z_hippcampul_vol', 'all', 'libra_sva', 'apoe + icv + libra_sva',
'wmh_volume_log', 'all', 'libra_sva', 'apoe + icv + libra_sva',
#mCAIDE
'ef', 'all', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'em', 'all', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'va', 'all', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'mmse_total', 'all', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'cdr', 'all', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'ab40', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42_ab40', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'tau', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ptau', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'nfl', 'all', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'meta_roi', 'all', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'z_hippcampul_vol', 'all', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'wmh_volume_log', 'all', 'mcaide_sva', 'apoe + icv + mcaide_sva',
#WHICAP
'ef', 'all', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'em', 'all', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'va', 'all', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'mmse_total', 'all', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'cdr', 'all', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'ab40', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42_ab40', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'tau', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ptau', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'nfl', 'all', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'meta_roi', 'all', 'whicap_sva', 'apoe + icv + whicap_sva',
'z_hippcampul_vol', 'all', 'whicap_sva', 'apoe + icv + whicap_sva',
'wmh_volume_log', 'all', 'whicap_sva', 'apoe + icv + whicap_sva',
#CogD
'ef', 'all', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'em', 'all', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'va', 'all', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'mmse_total', 'all', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'cdr', 'all', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'ab40', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42_ab40', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'tau', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ptau', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'nfl', 'all', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'meta_roi', 'all', 'cogd_sva', 'apoe + icv + cogd_sva',
'z_hippcampul_vol', 'all', 'cogd_sva', 'apoe + icv + cogd_sva',
'wmh_volume_log', 'all', 'cogd_sva', 'apoe + icv + cogd_sva'
) %>%
mutate(eq = glue('{outcome} ~ {predictors}'),
res = map(eq, lm, data = data),
dataf = map(res, tidy),
mod = map(res, glance),
r2 = map_dbl(mod, ~ .x$r.squared),
r2.adj = map_dbl(mod, ~ .x$adj.r.squared),
n = map_int(res, nobs))
```
### CDX - All
```{r}
all_cdx_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'cdx_cn_vs_ci', 'all', 'base', 'age + sex + apoe + id_education',
'cdx_mci_vs_cn', 'all', 'base', 'age + sex + apoe + id_education',
'cdx_dem_vs_cn', 'all', 'base', 'age + sex + apoe + id_education',
'cdx_ci_vs_cn', 'all', 'base', 'age + sex + apoe + id_education',
#Demographics
'cdx_cn_vs_ci', 'all', 'base_noapoe', 'age + sex + id_education',
'cdx_mci_vs_cn', 'all', 'base_noapoe', 'age + sex + id_education',
'cdx_dem_vs_cn', 'all', 'base_noapoe', 'age + sex + id_education',
'cdx_ci_vs_cn', 'all', 'base_noapoe', 'age + sex + id_education',
#LIBRA
'cdx_cn_vs_ci', 'all', 'libra', 'apoe + libra',
'cdx_mci_vs_cn', 'all', 'libra', 'apoe + libra',
'cdx_dem_vs_cn', 'all', 'libra', 'apoe + libra',
'cdx_ci_vs_cn', 'all', 'libra', 'apoe + libra',
#mCAIDE
'cdx_cn_vs_ci', 'all', 'mcaide', 'apoe + mcaide',
'cdx_mci_vs_cn', 'all', 'mcaide', 'apoe + mcaide',
'cdx_dem_vs_cn', 'all', 'mcaide', 'apoe + mcaide',
'cdx_ci_vs_cn', 'all', 'mcaide', 'apoe + mcaide',
#WHICAP
'cdx_cn_vs_ci', 'all', 'whicap', 'apoe + whicap',
'cdx_mci_vs_cn', 'all', 'whicap', 'apoe + whicap',
'cdx_dem_vs_cn', 'all', 'whicap', 'apoe + whicap',
'cdx_ci_vs_cn', 'all', 'whicap', 'apoe + whicap',
#CogD
'cdx_cn_vs_ci', 'all', 'cogd', 'apoe + cogd',
'cdx_mci_vs_cn', 'all', 'cogd', 'apoe + cogd',
'cdx_dem_vs_cn', 'all', 'cogd', 'apoe + cogd',
'cdx_ci_vs_cn', 'all', 'cogd', 'apoe + cogd',
# Sensitivity analysis
#LIBRA
'cdx_cn_vs_ci', 'all', 'libra_sva', 'apoe + libra_sva',
'cdx_mci_vs_cn', 'all', 'libra_sva', 'apoe + libra_sva',
'cdx_dem_vs_cn', 'all', 'libra_sva', 'apoe + libra_sva',
'cdx_ci_vs_cn', 'all', 'libra_sva', 'apoe + libra_sva',
#mCAIDE
'cdx_cn_vs_ci', 'all', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_mci_vs_cn', 'all', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_dem_vs_cn', 'all', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_ci_vs_cn', 'all', 'mcaide_sva', 'apoe + mcaide_sva',
#WHICAP
'cdx_cn_vs_ci', 'all', 'whicap_sva', 'apoe + whicap_sva',
'cdx_mci_vs_cn', 'all', 'whicap_sva', 'apoe + whicap_sva',
'cdx_dem_vs_cn', 'all', 'whicap_sva', 'apoe + whicap_sva',
'cdx_ci_vs_cn', 'all', 'whicap_sva', 'apoe + whicap_sva',
#CogD
'cdx_cn_vs_ci', 'all', 'cogd_sva', 'apoe + cogd_sva',
'cdx_mci_vs_cn', 'all', 'cogd_sva', 'apoe + cogd_sva',
'cdx_dem_vs_cn', 'all', 'cogd_sva', 'apoe + cogd_sva',
'cdx_ci_vs_cn', 'all', 'cogd_sva', 'apoe + cogd_sva'
) %>%
mutate(
eq = glue("{outcome} ~ {predictors}"),
res = map(eq, ~ glm(.x, data = data, family = "binomial")),
auc = map_dbl(res, ~ {
y = model.response(model.frame(.x)) # create model.frame(x) which is the exact same data as res
p = predict(.x, type = "response") # predict fitted probabilities for each outcome
if (length(unique(y)) > 1) { # if outcome is all 0s or 1s, skip
as.numeric(pROC::auc(pROC::roc(y, p))) # compute ROC curve then AUC and return numeric number (as.numeric)
} else {NA_real_}}), # if the outcome has only one class, return NA
nagelkerke_r2 = map_dbl(res, ~ {
out = tryCatch(performance::r2_nagelkerke(.x), error = function(e) NA_real_)
as.numeric(out)}),
dataf = map(res, tidy),
mod = map(res, glance),
n = map_int(res, nobs))
```
### Linear - Ancestry
```{r}
eur_num_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'ef', 'NHW', 'base', 'interview_language + age + sex + id_education + apoe',
'em', 'NHW', 'base', 'interview_language + age + sex + id_education + apoe',
'va', 'NHW', 'base', 'interview_language + age + sex + id_education + apoe',
'mmse_total', 'NHW', 'base', 'apoe + age + sex + id_education + interview_language',
'cdr', 'NHW', 'base', 'apoe + age + sex + id_education + interview_language',
'ab40', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42_ab40', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'tau', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'ptau', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'nfl', 'NHW', 'base', 'apoe + age + sex + bmi + eGFR',
'meta_roi', 'NHW', 'base', 'apoe + age + sex + icv',
'z_hippcampul_vol', 'NHW', 'base', 'apoe + age + sex + icv',
'wmh_volume_log', 'NHW', 'base', 'apoe + age + sex + icv',
#Demographics - apoe
'ef', 'NHW', 'base_noapoe', 'interview_language + age + sex + id_education',
'em', 'NHW', 'base_noapoe', 'interview_language + age + sex + id_education',
'va', 'NHW', 'base_noapoe', 'interview_language + age + sex + id_education',
'mmse_total', 'NHW', 'base_noapoe', 'age + sex + id_education + interview_language',
'cdr', 'NHW', 'base_noapoe', 'age + sex + id_education + interview_language',
'ab40', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'ab42', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'ab42_ab40', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'tau', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'ptau', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'nfl', 'NHW', 'base_noapoe', 'age + sex + bmi + eGFR',
'meta_roi', 'NHW', 'base_noapoe', 'age + sex + icv',
'z_hippcampul_vol', 'NHW', 'base_noapoe', 'age + sex + icv',
'wmh_volume_log', 'NHW', 'base_noapoe', 'age + sex + icv',
#mCAIDE
'ef', 'NHW', 'mcaide', 'interview_language + apoe + mcaide',
'em', 'NHW', 'mcaide', 'interview_language + apoe + mcaide',
'va', 'NHW', 'mcaide', 'interview_language + apoe + mcaide',
'mmse_total', 'NHW', 'mcaide', 'interview_language + apoe + mcaide',
'cdr', 'NHW', 'mcaide', 'interview_language + apoe + mcaide',
'ab40', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'ab42', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'ab42_ab40', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'tau', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'ptau', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'nfl', 'NHW', 'mcaide', 'apoe + eGFR + mcaide',
'meta_roi', 'NHW', 'mcaide', 'apoe + icv + mcaide',
'z_hippcampul_vol', 'NHW', 'mcaide', 'apoe + icv + mcaide',
'wmh_volume_log', 'NHW', 'mcaide', 'apoe + icv + mcaide',
# LIBRA
'ef', 'NHW', 'libra', 'interview_language + apoe + libra',
'em', 'NHW', 'libra', 'interview_language + apoe + libra',
'va', 'NHW', 'libra', 'interview_language + apoe + libra',
'mmse_total', 'NHW', 'libra', 'interview_language + apoe + libra',
'cdr', 'NHW', 'libra', 'interview_language + apoe + libra',
'ab40', 'NHW', 'libra', 'apoe + libra',
'ab42', 'NHW', 'libra', 'apoe + libra',
'ab42_ab40', 'NHW', 'libra', 'apoe + libra',
'tau', 'NHW', 'libra', 'apoe + libra',
'ptau', 'NHW', 'libra', 'apoe + libra',
'nfl', 'NHW', 'libra', 'apoe + libra',
'meta_roi', 'NHW', 'libra', 'apoe + icv + libra',
'z_hippcampul_vol', 'NHW', 'libra', 'apoe + icv + libra',
'wmh_volume_log', 'NHW', 'libra', 'apoe + icv + libra',
# WHICAP
'ef', 'NHW', 'whicap', 'interview_language + apoe + whicap',
'em', 'NHW', 'whicap', 'interview_language + apoe + whicap',
'va', 'NHW', 'whicap', 'interview_language + apoe + whicap',
'mmse_total', 'NHW', 'whicap', 'interview_language + apoe + whicap',
'cdr', 'NHW', 'whicap', 'interview_language + apoe + whicap',
'ab40', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'ab42', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'ab42_ab40', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'tau', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'ptau', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'nfl', 'NHW', 'whicap', 'apoe + eGFR + whicap',
'meta_roi', 'NHW', 'whicap', 'apoe + icv + whicap',
'z_hippcampul_vol', 'NHW', 'whicap', 'apoe + icv + whicap',
'wmh_volume_log', 'NHW', 'whicap', 'apoe + icv + whicap',
# CogD
'ef', 'NHW', 'cogd', 'interview_language + apoe + cogd',
'em', 'NHW', 'cogd', 'interview_language + apoe + cogd',
'va', 'NHW', 'cogd', 'interview_language + apoe + cogd',
'mmse_total', 'NHW', 'cogd', 'interview_language + apoe + cogd',
'cdr', 'NHW', 'cogd', 'interview_language + apoe + cogd',
'ab40', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42_ab40', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'tau', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'ptau', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'nfl', 'NHW', 'cogd', 'apoe + bmi + eGFR + cogd',
'meta_roi', 'NHW', 'cogd', 'apoe + icv + cogd',
'z_hippcampul_vol', 'NHW', 'cogd', 'apoe + icv + cogd',
'wmh_volume_log', 'NHW', 'cogd', 'apoe + icv + cogd',
# Sensitivity analysis
#LIBRA
'ef', 'NHW', 'libra_sva', 'interview_language + apoe + libra_sva',
'em', 'NHW', 'libra_sva', 'interview_language + apoe + libra_sva',
'va', 'NHW', 'libra_sva', 'interview_language + apoe + libra_sva',
'mmse_total', 'NHW', 'libra_sva', 'interview_language + apoe + libra_sva',
'cdr', 'NHW', 'libra_sva', 'interview_language + apoe + libra_sva',
'ab40', 'NHW', 'libra_sva', 'apoe + libra_sva',
'ab42', 'NHW', 'libra_sva', 'apoe + libra_sva',
'ab42_ab40', 'NHW', 'libra_sva', 'apoe + libra_sva',
'tau', 'NHW', 'libra_sva', 'apoe + libra_sva',
'ptau', 'NHW', 'libra_sva', 'apoe + libra_sva',
'nfl', 'NHW', 'libra_sva', 'apoe + libra_sva',
'meta_roi', 'NHW', 'libra_sva', 'apoe + icv + libra_sva',
'z_hippcampul_vol', 'NHW', 'libra_sva', 'apoe + icv + libra_sva',
'wmh_volume_log', 'NHW', 'libra_sva', 'apoe + icv + libra_sva',
#mCAIDE
'ef', 'NHW', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'em', 'NHW', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'va', 'NHW', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'mmse_total', 'NHW', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'cdr', 'NHW', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'ab40', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42_ab40', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'tau', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ptau', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'nfl', 'NHW', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'meta_roi', 'NHW', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'z_hippcampul_vol', 'NHW', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'wmh_volume_log', 'NHW', 'mcaide_sva', 'apoe + icv + mcaide_sva',
#WHICAP
'ef', 'NHW', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'em', 'NHW', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'va', 'NHW', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'mmse_total', 'NHW', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'cdr', 'NHW', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'ab40', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42_ab40', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'tau', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ptau', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'nfl', 'NHW', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'meta_roi', 'NHW', 'whicap_sva', 'apoe + icv + whicap_sva',
'z_hippcampul_vol', 'NHW', 'whicap_sva', 'apoe + icv + whicap_sva',
'wmh_volume_log', 'NHW', 'whicap_sva', 'apoe + icv + whicap_sva',
#CogD
'ef', 'NHW', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'em', 'NHW', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'va', 'NHW', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'mmse_total', 'NHW', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'cdr', 'NHW', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'ab40', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42_ab40', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'tau', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ptau', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'nfl', 'NHW', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'meta_roi', 'NHW', 'cogd_sva', 'apoe + icv + cogd_sva',
'z_hippcampul_vol', 'NHW', 'cogd_sva', 'apoe + icv + cogd_sva',
'wmh_volume_log', 'NHW', 'cogd_sva', 'apoe + icv + cogd_sva'
) %>%
mutate(eq = glue('{outcome} ~ {predictors}'),
res = map(eq, lm, data = filter(data, race == "NHW")),
dataf = map(res, tidy),
mod = map(res, glance),
r2 = map_dbl(mod, ~ .x$r.squared),
r2.adj = map_dbl(mod, ~ .x$adj.r.squared),
n = map_int(res, nobs))
amr_num_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'ef', 'Hispanic', 'base', 'interview_language + age + sex + id_education + apoe',
'em', 'Hispanic', 'base', 'interview_language + age + sex + id_education + apoe',
'va', 'Hispanic', 'base', 'interview_language + age + sex + id_education + apoe',
'mmse_total', 'Hispanic', 'base', 'apoe + age + sex + id_education + interview_language',
'cdr', 'Hispanic', 'base', 'apoe + age + sex + id_education + interview_language',
'ab40', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42_ab40', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'tau', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'ptau', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'nfl', 'Hispanic', 'base', 'apoe + age + sex + bmi + eGFR',
'meta_roi', 'Hispanic', 'base', 'apoe + age + sex + icv',
'z_hippcampul_vol', 'Hispanic', 'base', 'apoe + age + sex + icv',
'wmh_volume_log', 'Hispanic', 'base', 'apoe + age + sex + icv',
#Demographics - apoe
'ef', 'Hispanic', 'noapoe', 'interview_language + age + sex + id_education',
'em', 'Hispanic', 'noapoe', 'interview_language + age + sex + id_education',
'va', 'Hispanic', 'noapoe', 'interview_language + age + sex + id_education',
'mmse_total', 'Hispanic', 'noapoe', 'age + sex + id_education + interview_language',
'cdr', 'Hispanic', 'noapoe', 'age + sex + id_education + interview_language',
'ab40', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'ab42', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'ab42_ab40', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'tau', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'ptau', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'nfl', 'Hispanic', 'noapoe', 'age + sex + bmi + eGFR',
'meta_roi', 'Hispanic', 'noapoe', 'age + sex + icv',
'z_hippcampul_vol', 'Hispanic', 'noapoe', 'age + sex + icv',
'wmh_volume_log', 'Hispanic', 'noapoe', 'age + sex + icv',
#mCAIDE
'ef', 'Hispanic', 'mcaide', 'interview_language + apoe + mcaide',
'em', 'Hispanic', 'mcaide', 'interview_language + apoe + mcaide',
'va', 'Hispanic', 'mcaide', 'interview_language + apoe + mcaide',
'mmse_total', 'Hispanic', 'mcaide', 'interview_language + apoe + mcaide',
'cdr', 'Hispanic', 'mcaide', 'interview_language + apoe + mcaide',
'ab40', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'ab42', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'ab42_ab40', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'tau', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'ptau', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'nfl', 'Hispanic', 'mcaide', 'apoe + eGFR + mcaide',
'meta_roi', 'Hispanic', 'mcaide', 'apoe + icv + mcaide',
'z_hippcampul_vol', 'Hispanic', 'mcaide', 'apoe + icv + mcaide',
'wmh_volume_log', 'Hispanic', 'mcaide', 'apoe + icv + mcaide',
# LIBRA
'ef', 'Hispanic', 'libra', 'interview_language + apoe + libra',
'em', 'Hispanic', 'libra', 'interview_language + apoe + libra',
'va', 'Hispanic', 'libra', 'interview_language + apoe + libra',
'mmse_total', 'Hispanic', 'libra', 'interview_language + apoe + libra',
'cdr', 'Hispanic', 'libra', 'interview_language + apoe + libra',
'ab40', 'Hispanic', 'libra', 'apoe + libra',
'ab42', 'Hispanic', 'libra', 'apoe + libra',
'ab42_ab40', 'Hispanic', 'libra', 'apoe + libra',
'tau', 'Hispanic', 'libra', 'apoe + libra',
'ptau', 'Hispanic', 'libra', 'apoe + libra',
'nfl', 'Hispanic', 'libra', 'apoe + libra',
'meta_roi', 'Hispanic', 'libra', 'apoe + icv + libra',
'z_hippcampul_vol', 'Hispanic', 'libra', 'apoe + icv + libra',
'wmh_volume_log', 'Hispanic', 'libra', 'apoe + icv + libra',
# WHICAP
'ef', 'Hispanic', 'whicap', 'interview_language + apoe + whicap',
'em', 'Hispanic', 'whicap', 'interview_language + apoe + whicap',
'va', 'Hispanic', 'whicap', 'interview_language + apoe + whicap',
'mmse_total', 'Hispanic', 'whicap', 'interview_language + apoe + whicap',
'cdr', 'Hispanic', 'whicap', 'interview_language + apoe + whicap',
'ab40', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'ab42', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'ab42_ab40', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'tau', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'ptau', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'nfl', 'Hispanic', 'whicap', 'apoe + eGFR + whicap',
'meta_roi', 'Hispanic', 'whicap', 'apoe + icv + whicap',
'z_hippcampul_vol', 'Hispanic', 'whicap', 'apoe + icv + whicap',
'wmh_volume_log', 'Hispanic', 'whicap', 'apoe + icv + whicap',
# CogD
'ef', 'Hispanic', 'cogd', 'interview_language + apoe + cogd',
'em', 'Hispanic', 'cogd', 'interview_language + apoe + cogd',
'va', 'Hispanic', 'cogd', 'interview_language + apoe + cogd',
'mmse_total', 'Hispanic', 'cogd', 'interview_language + apoe + cogd',
'cdr', 'Hispanic', 'cogd', 'interview_language + apoe + cogd',
'ab40', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42_ab40', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'tau', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'ptau', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'nfl', 'Hispanic', 'cogd', 'apoe + bmi + eGFR + cogd',
'meta_roi', 'Hispanic', 'cogd', 'apoe + icv + cogd',
'z_hippcampul_vol', 'Hispanic', 'cogd', 'apoe + icv + cogd',
'wmh_volume_log', 'Hispanic', 'cogd', 'apoe + icv + cogd',
# Sensitivity analysis
#LIBRA
'ef', 'Hispanic', 'libra_sva', 'interview_language + apoe + libra_sva',
'em', 'Hispanic', 'libra_sva', 'interview_language + apoe + libra_sva',
'va', 'Hispanic', 'libra_sva', 'interview_language + apoe + libra_sva',
'mmse_total', 'Hispanic', 'libra_sva', 'interview_language + apoe + libra_sva',
'cdr', 'Hispanic', 'libra_sva', 'interview_language + apoe + libra_sva',
'ab40', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'ab42', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'ab42_ab40', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'tau', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'ptau', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'nfl', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'meta_roi', 'Hispanic', 'libra_sva', 'apoe + icv + libra_sva',
'z_hippcampul_vol', 'Hispanic', 'libra_sva', 'apoe + icv + libra_sva',
'wmh_volume_log', 'Hispanic', 'libra_sva', 'apoe + icv + libra_sva',
#mCAIDE
'ef', 'Hispanic', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'em', 'Hispanic', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'va', 'Hispanic', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'mmse_total', 'Hispanic', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'cdr', 'Hispanic', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'ab40', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42_ab40', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'tau', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ptau', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'nfl', 'Hispanic', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'meta_roi', 'Hispanic', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'z_hippcampul_vol', 'Hispanic', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'wmh_volume_log', 'Hispanic', 'mcaide_sva', 'apoe + icv + mcaide_sva',
#WHICAP
'ef', 'Hispanic', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'em', 'Hispanic', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'va', 'Hispanic', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'mmse_total', 'Hispanic', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'cdr', 'Hispanic', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'ab40', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42_ab40', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'tau', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ptau', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'nfl', 'Hispanic', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'meta_roi', 'Hispanic', 'whicap_sva', 'apoe + icv + whicap_sva',
'z_hippcampul_vol', 'Hispanic', 'whicap_sva', 'apoe + icv + whicap_sva',
'wmh_volume_log', 'Hispanic', 'whicap_sva', 'apoe + icv + whicap_sva',
#CogD
'ef', 'Hispanic', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'em', 'Hispanic', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'va', 'Hispanic', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'mmse_total', 'Hispanic', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'cdr', 'Hispanic', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'ab40', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42_ab40', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'tau', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ptau', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'nfl', 'Hispanic', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'meta_roi', 'Hispanic', 'cogd_sva', 'apoe + icv + cogd_sva',
'z_hippcampul_vol', 'Hispanic', 'cogd_sva', 'apoe + icv + cogd_sva',
'wmh_volume_log', 'Hispanic', 'cogd_sva', 'apoe + icv + cogd_sva'
) %>%
mutate(eq = glue('{outcome} ~ {predictors}'),
res = map(eq, lm, data = filter(data, race == "Hispanic")),
dataf = map(res, tidy),
mod = map(res, glance),
r2 = map_dbl(mod, ~ .x$r.squared),
r2.adj = map_dbl(mod, ~ .x$adj.r.squared),
n = map_int(res, nobs))
afr_num_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'ef', 'Black', 'base', 'interview_language + age + sex + id_education + apoe',
'em', 'Black', 'base', 'interview_language + age + sex + id_education + apoe',
'va', 'Black', 'base', 'interview_language + age + sex + id_education + apoe',
'mmse_total', 'Black', 'base', 'apoe + age + sex + id_education + interview_language',
'cdr', 'Black', 'base', 'apoe + age + sex + id_education + interview_language',
'ab40', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'ab42_ab40', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'tau', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'ptau', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'nfl', 'Black', 'base', 'apoe + age + sex + bmi + eGFR',
'meta_roi', 'Black', 'base', 'apoe + age + sex + icv',
'z_hippcampul_vol', 'Black', 'base', 'apoe + age + sex + icv',
'wmh_volume_log', 'Black', 'base', 'apoe + age + sex + icv',
#Demographics - apoe
'ef', 'Black', 'noapoe', 'interview_language + age + sex + id_education',
'em', 'Black', 'noapoe', 'interview_language + age + sex + id_education',
'va', 'Black', 'noapoe', 'interview_language + age + sex + id_education',
'mmse_total', 'Black', 'noapoe', 'age + sex + id_education + interview_language',
'cdr', 'Black', 'noapoe', 'age + sex + id_education + interview_language',
'ab40', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'ab42', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'ab42_ab40', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'tau', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'ptau', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'nfl', 'Black', 'noapoe', 'age + sex + bmi + eGFR',
'meta_roi', 'Black', 'noapoe', 'age + sex + icv',
'z_hippcampul_vol', 'Black', 'noapoe', 'age + sex + icv',
'wmh_volume_log', 'Black', 'noapoe', 'age + sex + icv',
#mCAIDE
'ef', 'Black', 'mcaide', 'interview_language + apoe + mcaide',
'em', 'Black', 'mcaide', 'interview_language + apoe + mcaide',
'va', 'Black', 'mcaide', 'interview_language + apoe + mcaide',
'mmse_total', 'Black', 'mcaide', 'interview_language + apoe + mcaide',
'cdr', 'Black', 'mcaide', 'interview_language + apoe + mcaide',
'ab40', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'ab42', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'ab42_ab40', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'tau', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'ptau', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'nfl', 'Black', 'mcaide', 'apoe + eGFR + mcaide',
'meta_roi', 'Black', 'mcaide', 'apoe + icv + mcaide',
'z_hippcampul_vol', 'Black', 'mcaide', 'apoe + icv + mcaide',
'wmh_volume_log', 'Black', 'mcaide', 'apoe + icv + mcaide',
# LIBRA
'ef', 'Black', 'libra', 'interview_language + apoe + libra',
'em', 'Black', 'libra', 'interview_language + apoe + libra',
'va', 'Black', 'libra', 'interview_language + apoe + libra',
'mmse_total', 'Black', 'libra', 'interview_language + apoe + libra',
'cdr', 'Black', 'libra', 'interview_language + apoe + libra',
'ab40', 'Black', 'libra', 'apoe + libra',
'ab42', 'Black', 'libra', 'apoe + libra',
'ab42_ab40', 'Black', 'libra', 'apoe + libra',
'tau', 'Black', 'libra', 'apoe + libra',
'ptau', 'Black', 'libra', 'apoe + libra',
'nfl', 'Black', 'libra', 'apoe + libra',
'meta_roi', 'Black', 'libra', 'apoe + icv + libra',
'z_hippcampul_vol', 'Black', 'libra', 'apoe + icv + libra',
'wmh_volume_log', 'Black', 'libra', 'apoe + icv + libra',
# WHICAP
'ef', 'Black', 'whicap', 'interview_language + apoe + whicap',
'em', 'Black', 'whicap', 'interview_language + apoe + whicap',
'va', 'Black', 'whicap', 'interview_language + apoe + whicap',
'mmse_total', 'Black', 'whicap', 'interview_language + apoe + whicap',
'cdr', 'Black', 'whicap', 'interview_language + apoe + whicap',
'ab40', 'Black', 'whicap', 'apoe + eGFR + whicap',
'ab42', 'Black', 'whicap', 'apoe + eGFR + whicap',
'ab42_ab40', 'Black', 'whicap', 'apoe + eGFR + whicap',
'tau', 'Black', 'whicap', 'apoe + eGFR + whicap',
'ptau', 'Black', 'whicap', 'apoe + eGFR + whicap',
'nfl', 'Black', 'whicap', 'apoe + eGFR + whicap',
'meta_roi', 'Black', 'whicap', 'apoe + icv + whicap',
'z_hippcampul_vol', 'Black', 'whicap', 'apoe + icv + whicap',
'wmh_volume_log', 'Black', 'whicap', 'apoe + icv + whicap',
# CogD
'ef', 'Black', 'cogd', 'interview_language + apoe + cogd',
'em', 'Black', 'cogd', 'interview_language + apoe + cogd',
'va', 'Black', 'cogd', 'interview_language + apoe + cogd',
'mmse_total', 'Black', 'cogd', 'interview_language + apoe + cogd',
'cdr', 'Black', 'cogd', 'interview_language + apoe + cogd',
'ab40', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'ab42_ab40', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'tau', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'ptau', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'nfl', 'Black', 'cogd', 'apoe + bmi + eGFR + cogd',
'meta_roi', 'Black', 'cogd', 'apoe + icv + cogd',
'z_hippcampul_vol', 'Black', 'cogd', 'apoe + icv + cogd',
'wmh_volume_log', 'Black', 'cogd', 'apoe + icv + cogd',
# Sensitivity analysis
#LIBRA
'ef', 'Black', 'libra_sva', 'interview_language + apoe + libra_sva',
'em', 'Black', 'libra_sva', 'interview_language + apoe + libra_sva',
'va', 'Black', 'libra_sva', 'interview_language + apoe + libra_sva',
'mmse_total', 'Black', 'libra_sva', 'interview_language + apoe + libra_sva',
'cdr', 'Black', 'libra_sva', 'interview_language + apoe + libra_sva',
'ab40', 'Black', 'libra_sva', 'apoe + libra_sva',
'ab42', 'Black', 'libra_sva', 'apoe + libra_sva',
'ab42_ab40', 'Black', 'libra_sva', 'apoe + libra_sva',
'tau', 'Black', 'libra_sva', 'apoe + libra_sva',
'ptau', 'Black', 'libra_sva', 'apoe + libra_sva',
'nfl', 'Black', 'libra_sva', 'apoe + libra_sva',
'meta_roi', 'Black', 'libra_sva', 'apoe + icv + libra_sva',
'z_hippcampul_vol', 'Black', 'libra_sva', 'apoe + icv + libra_sva',
'wmh_volume_log', 'Black', 'libra_sva', 'apoe + icv + libra_sva',
#mCAIDE
'ef', 'Black', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'em', 'Black', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'va', 'Black', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'mmse_total', 'Black', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'cdr', 'Black', 'mcaide_sva', 'interview_language + apoe + mcaide_sva',
'ab40', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ab42_ab40', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'tau', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'ptau', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'nfl', 'Black', 'mcaide_sva', 'apoe + eGFR + mcaide_sva',
'meta_roi', 'Black', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'z_hippcampul_vol', 'Black', 'mcaide_sva', 'apoe + icv + mcaide_sva',
'wmh_volume_log', 'Black', 'mcaide_sva', 'apoe + icv + mcaide_sva',
#WHICAP
'ef', 'Black', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'em', 'Black', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'va', 'Black', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'mmse_total', 'Black', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'cdr', 'Black', 'whicap_sva', 'interview_language + apoe + whicap_sva',
'ab40', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ab42_ab40', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'tau', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'ptau', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'nfl', 'Black', 'whicap_sva', 'apoe + eGFR + whicap_sva',
'meta_roi', 'Black', 'whicap_sva', 'apoe + icv + whicap_sva',
'z_hippcampul_vol', 'Black', 'whicap_sva', 'apoe + icv + whicap_sva',
'wmh_volume_log', 'Black', 'whicap_sva', 'apoe + icv + whicap_sva',
#CogD
'ef', 'Black', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'em', 'Black', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'va', 'Black', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'mmse_total', 'Black', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'cdr', 'Black', 'cogd_sva', 'interview_language + apoe + cogd_sva',
'ab40', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ab42_ab40', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'tau', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'ptau', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'nfl', 'Black', 'cogd_sva', 'apoe + eGFR + cogd_sva',
'meta_roi', 'Black', 'cogd_sva', 'apoe + icv + cogd_sva',
'z_hippcampul_vol', 'Black', 'cogd_sva', 'apoe + icv + cogd_sva',
'wmh_volume_log', 'Black', 'cogd_sva', 'apoe + icv + cogd_sva'
) %>%
mutate(eq = glue('{outcome} ~ {predictors}'),
res = map(eq, lm, data = filter(data, race == "Black")),
dataf = map(res, tidy),
mod = map(res, glance),
r2 = map_dbl(mod, ~ .x$r.squared),
r2.adj = map_dbl(mod, ~ .x$adj.r.squared),
n = map_int(res, nobs))
```
### CDX - Ancestry
```{r}
eur_cdx_prs = tribble( ~outcome, ~pop, ~model, ~predictors,
#Demographics
'cdx_cn_vs_ci', 'NHW', 'base', 'age + sex + apoe + id_education',
'cdx_mci_vs_cn', 'NHW', 'base', 'age + sex + apoe + id_education',
'cdx_dem_vs_cn', 'NHW', 'base', 'age + sex + apoe + id_education',
'cdx_ci_vs_cn', 'NHW', 'base', 'age + sex + apoe + id_education',
#Demographics
'cdx_cn_vs_ci', 'NHW', 'base_noapoe', 'age + sex + id_education',
'cdx_mci_vs_cn', 'NHW', 'base_noapoe', 'age + sex + id_education',
'cdx_dem_vs_cn', 'NHW', 'base_noapoe', 'age + sex + id_education',
'cdx_ci_vs_cn', 'NHW', 'base_noapoe', 'age + sex + id_education',
#LIBRA
'cdx_cn_vs_ci', 'NHW', 'libra', 'apoe + libra',
'cdx_mci_vs_cn', 'NHW', 'libra', 'apoe + libra',
'cdx_dem_vs_cn', 'NHW', 'libra', 'apoe + libra',
'cdx_ci_vs_cn', 'NHW', 'libra', 'apoe + libra',
#mCAIDE
'cdx_cn_vs_ci', 'NHW', 'mcaide', 'apoe + mcaide',
'cdx_mci_vs_cn', 'NHW', 'mcaide', 'apoe + mcaide',
'cdx_dem_vs_cn', 'NHW', 'mcaide', 'apoe + mcaide',
'cdx_ci_vs_cn', 'NHW', 'mcaide', 'apoe + mcaide',
#WHICAP
'cdx_cn_vs_ci', 'NHW', 'whicap', 'apoe + whicap',
'cdx_mci_vs_cn', 'NHW', 'whicap', 'apoe + whicap',
'cdx_dem_vs_cn', 'NHW', 'whicap', 'apoe + whicap',
'cdx_ci_vs_cn', 'NHW', 'whicap', 'apoe + whicap',
#CogD
'cdx_cn_vs_ci', 'NHW', 'cogd', 'apoe + cogd',
'cdx_mci_vs_cn', 'NHW', 'cogd', 'apoe + cogd',
'cdx_dem_vs_cn', 'NHW', 'cogd', 'apoe + cogd',
'cdx_ci_vs_cn', 'NHW', 'cogd', 'apoe + cogd',
# Sensitivity analysis
#LIBRA
'cdx_cn_vs_ci', 'NHW', 'libra_sva', 'apoe + libra_sva',
'cdx_mci_vs_cn', 'NHW', 'libra_sva', 'apoe + libra_sva',
'cdx_dem_vs_cn', 'NHW', 'libra_sva', 'apoe + libra_sva',
'cdx_ci_vs_cn', 'NHW', 'libra_sva', 'apoe + libra_sva',
#mCAIDE
'cdx_cn_vs_ci', 'NHW', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_mci_vs_cn', 'NHW', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_dem_vs_cn', 'NHW', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_ci_vs_cn', 'NHW', 'mcaide_sva', 'apoe + mcaide_sva',
#WHICAP
'cdx_cn_vs_ci', 'NHW', 'whicap_sva', 'apoe + whicap_sva',
'cdx_mci_vs_cn', 'NHW', 'whicap_sva', 'apoe + whicap_sva',
'cdx_dem_vs_cn', 'NHW', 'whicap_sva', 'apoe + whicap_sva',
'cdx_ci_vs_cn', 'NHW', 'whicap_sva', 'apoe + whicap_sva',
#CogD
'cdx_cn_vs_ci', 'NHW', 'cogd_sva', 'apoe + cogd_sva',
'cdx_mci_vs_cn', 'NHW', 'cogd_sva', 'apoe + cogd_sva',
'cdx_dem_vs_cn', 'NHW', 'cogd_sva', 'apoe + cogd_sva',
'cdx_ci_vs_cn', 'NHW', 'cogd_sva', 'apoe + cogd_sva'
) %>%
mutate(
eq = glue("{outcome} ~ {predictors}"),
res = map(eq, glm, data = filter(data, race == "NHW"), family = "binomial"),
auc = map_dbl(res, ~ {
y = model.response(model.frame(.x)) # create model.frame(x) which is the exact same data as res
p = predict(.x, type = "response") # predict fitted probabilities for each outcome
if (length(unique(y)) > 1) { # if outcome is all 0s or 1s, skip
as.numeric(pROC::auc(pROC::roc(y, p))) # compute ROC curve then AUC and return numeric number (as.numeric)
} else {NA_real_}}), # if the outcome has only one class, return NA
nagelkerke_r2 = map_dbl(res, ~ {
out = tryCatch(performance::r2_nagelkerke(.x), error = function(e) NA_real_)
as.numeric(out)}),
dataf = map(res, tidy),
mod = map(res, glance),
n = map_int(res, nobs))
amr_cdx_prs = tribble( ~outcome, ~pop, ~model, ~predictors,
#Demographics
'cdx_cn_vs_ci', 'Hispanic', 'base', 'age + sex + apoe + id_education',
'cdx_mci_vs_cn', 'Hispanic', 'base', 'age + sex + apoe + id_education',
'cdx_dem_vs_cn', 'Hispanic', 'base', 'age + sex + apoe + id_education',
'cdx_ci_vs_cn', 'Hispanic', 'base', 'age + sex + apoe + id_education',
#Demographics
'cdx_cn_vs_ci', 'Hispanic', 'base_noapoe', 'age + sex + id_education',
'cdx_mci_vs_cn', 'Hispanic', 'base_noapoe', 'age + sex + id_education',
'cdx_dem_vs_cn', 'Hispanic', 'base_noapoe', 'age + sex + id_education',
'cdx_ci_vs_cn', 'Hispanic', 'base_noapoe', 'age + sex + id_education',
#LIBRA
'cdx_cn_vs_ci', 'Hispanic', 'libra', 'apoe + libra',
'cdx_mci_vs_cn', 'Hispanic', 'libra', 'apoe + libra',
'cdx_dem_vs_cn', 'Hispanic', 'libra', 'apoe + libra',
'cdx_ci_vs_cn', 'Hispanic', 'libra', 'apoe + libra',
#mCAIDE
'cdx_cn_vs_ci', 'Hispanic', 'mcaide', 'apoe + mcaide',
'cdx_mci_vs_cn', 'Hispanic', 'mcaide', 'apoe + mcaide',
'cdx_dem_vs_cn', 'Hispanic', 'mcaide', 'apoe + mcaide',
'cdx_ci_vs_cn', 'Hispanic', 'mcaide', 'apoe + mcaide',
#WHICAP
'cdx_cn_vs_ci', 'Hispanic', 'whicap', 'apoe + whicap',
'cdx_mci_vs_cn', 'Hispanic', 'whicap', 'apoe + whicap',
'cdx_dem_vs_cn', 'Hispanic', 'whicap', 'apoe + whicap',
'cdx_ci_vs_cn', 'Hispanic', 'whicap', 'apoe + whicap',
#CogD
'cdx_cn_vs_ci', 'Hispanic', 'cogd', 'apoe + cogd',
'cdx_mci_vs_cn', 'Hispanic', 'cogd', 'apoe + cogd',
'cdx_dem_vs_cn', 'Hispanic', 'cogd', 'apoe + cogd',
'cdx_ci_vs_cn', 'Hispanic', 'cogd', 'apoe + cogd',
# Sensitivity analysis
#LIBRA
'cdx_cn_vs_ci', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'cdx_mci_vs_cn', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'cdx_dem_vs_cn', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
'cdx_ci_vs_cn', 'Hispanic', 'libra_sva', 'apoe + libra_sva',
#mCAIDE
'cdx_cn_vs_ci', 'Hispanic', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_mci_vs_cn', 'Hispanic', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_dem_vs_cn', 'Hispanic', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_ci_vs_cn', 'Hispanic', 'mcaide_sva', 'apoe + mcaide_sva',
#WHICAP
'cdx_cn_vs_ci', 'Hispanic', 'whicap_sva', 'apoe + whicap_sva',
'cdx_mci_vs_cn', 'Hispanic', 'whicap_sva', 'apoe + whicap_sva',
'cdx_dem_vs_cn', 'Hispanic', 'whicap_sva', 'apoe + whicap_sva',
'cdx_ci_vs_cn', 'Hispanic', 'whicap_sva', 'apoe + whicap_sva',
#CogD
'cdx_cn_vs_ci', 'Hispanic', 'cogd_sva', 'apoe + cogd_sva',
'cdx_mci_vs_cn', 'Hispanic', 'cogd_sva', 'apoe + cogd_sva',
'cdx_dem_vs_cn', 'Hispanic', 'cogd_sva', 'apoe + cogd_sva',
'cdx_ci_vs_cn', 'Hispanic', 'cogd_sva', 'apoe + cogd_sva'
) %>%
mutate(
eq = glue("{outcome} ~ {predictors}"),
res = map(eq, glm, data = filter(data, race == "Hispanic"), family = "binomial"),
auc = map_dbl(res, ~ {
y = model.response(model.frame(.x)) # create model.frame(x) which is the exact same data as res
p = predict(.x, type = "response") # predict fitted probabilities for each outcome
if (length(unique(y)) > 1) { # if outcome is all 0s or 1s, skip
as.numeric(pROC::auc(pROC::roc(y, p))) # compute ROC curve then AUC and return numeric number (as.numeric)
} else {NA_real_}}), # if the outcome has only one class, return NA
nagelkerke_r2 = map_dbl(res, ~ {
out = tryCatch(performance::r2_nagelkerke(.x), error = function(e) NA_real_)
as.numeric(out)}),
dataf = map(res, tidy),
mod = map(res, glance),
n = map_int(res, nobs))
afr_cdx_prs = tribble(~outcome, ~pop, ~model, ~predictors,
#Demographics
'cdx_cn_vs_ci', 'Black', 'base', 'age + sex + apoe + id_education',
'cdx_mci_vs_cn', 'Black', 'base', 'age + sex + apoe + id_education',
'cdx_dem_vs_cn', 'Black', 'base', 'age + sex + apoe + id_education',
'cdx_ci_vs_cn', 'Black', 'base', 'age + sex + apoe + id_education',
#Demographics
'cdx_cn_vs_ci', 'Black', 'base_noapoe', 'age + sex + id_education',
'cdx_mci_vs_cn', 'Black', 'base_noapoe', 'age + sex + id_education',
'cdx_dem_vs_cn', 'Black', 'base_noapoe', 'age + sex + id_education',
'cdx_ci_vs_cn', 'Black', 'base_noapoe', 'age + sex + id_education',
#LIBRA
'cdx_cn_vs_ci', 'Black', 'libra', 'apoe + libra',
'cdx_mci_vs_cn', 'Black', 'libra', 'apoe + libra',
'cdx_dem_vs_cn', 'Black', 'libra', 'apoe + libra',
'cdx_ci_vs_cn', 'Black', 'libra', 'apoe + libra',
#mCAIDE
'cdx_cn_vs_ci', 'Black', 'mcaide', 'apoe + mcaide',
'cdx_mci_vs_cn', 'Black', 'mcaide', 'apoe + mcaide',
'cdx_dem_vs_cn', 'Black', 'mcaide', 'apoe + mcaide',
'cdx_ci_vs_cn', 'Black', 'mcaide', 'apoe + mcaide',
#WHICAP
'cdx_cn_vs_ci', 'Black', 'whicap', 'apoe + whicap',
'cdx_mci_vs_cn', 'Black', 'whicap', 'apoe + whicap',
'cdx_dem_vs_cn', 'Black', 'whicap', 'apoe + whicap',
'cdx_ci_vs_cn', 'Black', 'whicap', 'apoe + whicap',
#CogD
'cdx_cn_vs_ci', 'Black', 'cogd', 'apoe + cogd',
'cdx_mci_vs_cn', 'Black', 'cogd', 'apoe + cogd',
'cdx_dem_vs_cn', 'Black', 'cogd', 'apoe + cogd',
'cdx_ci_vs_cn', 'Black', 'cogd', 'apoe + cogd',
# Sensitivity analysis
#LIBRA
'cdx_cn_vs_ci', 'Black', 'libra_sva', 'apoe + libra_sva',
'cdx_mci_vs_cn', 'Black', 'libra_sva', 'apoe + libra_sva',
'cdx_dem_vs_cn', 'Black', 'libra_sva', 'apoe + libra_sva',
'cdx_ci_vs_cn', 'Black', 'libra_sva', 'apoe + libra_sva',
#mCAIDE
'cdx_cn_vs_ci', 'Black', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_mci_vs_cn', 'Black', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_dem_vs_cn', 'Black', 'mcaide_sva', 'apoe + mcaide_sva',
'cdx_ci_vs_cn', 'Black', 'mcaide_sva', 'apoe + mcaide_sva',
#WHICAP
'cdx_cn_vs_ci', 'Black', 'whicap_sva', 'apoe + whicap_sva',
'cdx_mci_vs_cn', 'Black', 'whicap_sva', 'apoe + whicap_sva',
'cdx_dem_vs_cn', 'Black', 'whicap_sva', 'apoe + whicap_sva',
'cdx_ci_vs_cn', 'Black', 'whicap_sva', 'apoe + whicap_sva',
#CogD
'cdx_cn_vs_ci', 'Black', 'cogd_sva', 'apoe + cogd_sva',
'cdx_mci_vs_cn', 'Black', 'cogd_sva', 'apoe + cogd_sva',
'cdx_dem_vs_cn', 'Black', 'cogd_sva', 'apoe + cogd_sva',
'cdx_ci_vs_cn', 'Black', 'cogd_sva', 'apoe + cogd_sva'
) %>%
mutate(
eq = glue("{outcome} ~ {predictors}"),
res = map(eq, glm, data = filter(data, race == "Black"), family = "binomial"),
auc = map_dbl(res, ~ {
y = model.response(model.frame(.x)) # create model.frame(x) which is the exact same data as res
p = predict(.x, type = "response") # predict fitted probabilities for each outcome
if (length(unique(y)) > 1) { # if outcome is all 0s or 1s, skip
as.numeric(pROC::auc(pROC::roc(y, p))) # compute ROC curve then AUC and return numeric number (as.numeric)
} else {NA_real_}}), # if the outcome has only one class, return NA
nagelkerke_r2 = map_dbl(res, ~ {
out = tryCatch(performance::r2_nagelkerke(.x), error = function(e) NA_real_)
as.numeric(out)}),
dataf = map(res, tidy),
mod = map(res, glance),
n = map_int(res, nobs))
```
### Coefficients
```{r}
res_crs = bind_rows(
select(all_num_prs, model, pop, outcome, dataf, res),
select(all_cdx_prs, model, pop, outcome, dataf, res),
select(eur_num_prs, model, outcome, pop, dataf, res),
select(amr_num_prs, model, outcome, pop, dataf, res),
select(afr_num_prs, model, outcome, pop, dataf, res),
select(eur_cdx_prs, model, outcome, pop, dataf, res),
select(amr_cdx_prs, model, outcome, pop, dataf, res),
select(afr_cdx_prs, model, outcome, pop, dataf, res)
) %>%
unnest(dataf) %>%
mutate(std_params = map(res, ~ standardize_parameters(.x, method = "posthoc"))) %>%
unnest(std_params) %>%
select(-c(res, CI)) %>%
filter(term %in% c("cogd", "libra", "whicap", "mcaide")) %>%
filter(!outcome %in% c("cdx_hypertension", "cdx_dyslipidemia", "cdx_diabetes", "bmi", "cdx_anxiety", "cdx_depression", "cdx_cvd", "wmh_volume_log")) %>%
arrange(pop, term, Std_Coefficient) %>%
mutate(
p.fdr = p.adjust(p.value, method = "fdr"),
sig_fdr = ifelse(p.fdr < 0.05, TRUE, FALSE),
sig = ifelse(p.value < 0.05, TRUE, FALSE),
conf.low = estimate - (std.error * 1.96),
conf.high = estimate + (std.error * 1.96),
lci = exp(conf.low),
uci = exp(conf.high),
or = exp(estimate),
category = case_when(
outcome %in% c("va", "em", "ef", "mmse_total", "cdr") ~ "Cognitive \nFunction",
outcome %in% c("tau", "ptau", "nfl", "ab42", "ab40", "ab42_ab40") ~ "Plasma \nBiomarkers",
outcome %in% c("meta_roi", "z_hippcampul_vol") ~ "Neuroimaging",
outcome %in% c("cdx_cn_vs_ci", "cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn") ~ "Medical \nHistory"),
lab = fct_recode(
outcome,
MMSE = "mmse_total",
CDR = "cdr",
"Executive\n Function" = "ef",
"Verbal\n Ability" = "va",
Memory = "em",
"Cortical \nThickness" = "meta_roi",
"Hippo. \nVolume" = "z_hippcampul_vol",
"Aβ40" = "ab40",
"Aβ42" = "ab42",
"Aβ42/Aβ40" = "ab42_ab40",
"Total Tau" = "tau",
"pTau" = "ptau",
NfL = "nfl",
Healthy = "cdx_cn_vs_ci",
MCI = "cdx_mci_vs_cn",
Dementia = "cdx_dem_vs_cn",
"Cog. Impair." = "cdx_ci_vs_cn"
),
lab = fct_inorder(lab))
res_crs_sva = bind_rows(
select(all_num_prs, model, pop, outcome, dataf, res),
select(all_cdx_prs, model, pop, outcome, dataf, res),
select(eur_num_prs, model, outcome, pop, dataf, res),
select(amr_num_prs, model, outcome, pop, dataf, res),
select(afr_num_prs, model, outcome, pop, dataf, res),
select(eur_cdx_prs, model, outcome, pop, dataf, res),
select(amr_cdx_prs, model, outcome, pop, dataf, res),
select(afr_cdx_prs, model, outcome, pop, dataf, res)
) %>%
unnest(dataf) %>%
mutate(std_params = map(res, ~ standardize_parameters(.x, method = "posthoc"))) %>%
unnest(std_params) %>%
select(-c(res, CI)) %>%
filter(term %in% c("cogd_sva", "whicap_sva", "mcaide_sva")) %>%
filter(!outcome %in% c("cdx_hypertension", "cdx_dyslipidemia", "cdx_diabetes", "bmi", "cdx_anxiety", "cdx_depression", "cdx_cvd", "wmh_volume_log")) %>%
arrange(pop, term, estimate) %>%
mutate(
p.fdr = p.adjust(p.value, method = "fdr"),
sig_fdr = ifelse(p.fdr < 0.05, TRUE, FALSE),
sig = ifelse(p.value < 0.05, TRUE, FALSE),
conf.low = estimate - (std.error * 1.96),
conf.high = estimate + (std.error * 1.96),
lci = exp(conf.low),
uci = exp(conf.high),
or = exp(estimate),
category = case_when(
outcome %in% c("va", "em", "ef", "mmse_total", "cdr") ~ "Cognitive \nFunction",
outcome %in% c("tau", "ptau", "nfl", "ab42", "ab40", "ab42_ab40") ~ "Plasma \nBiomarkers",
outcome %in% c("meta_roi", "z_hippcampul_vol") ~ "Neuroimaging",
outcome %in% c("cdx_cn_vs_ci", "cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn") ~ "Medical \nHistory"),
outcome = factor(outcome, levels = c(
"cdx_hypertension", "cdx_dyslipidemia", "cdx_diabetes", "cdx_cvd",
"cdx_anxiety", "cdx_depression", "bmi",
"mmse_total", "cdr", "ef", "va", "em",
"meta_roi", "z_hippcampul_vol",
"ab40", "ab42", "ab42_ab40", "tau", "ptau", "nfl",
"cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn")),
lab = fct_recode(
outcome,
MMSE = "mmse_total",
CDR = "cdr",
"Executive\n Function" = "ef",
"Verbal\n Ability" = "va",
Memory = "em",
"Cortical \nThickness" = "meta_roi",
"Hippo. \nVolume" = "z_hippcampul_vol",
"Aβ40" = "ab40",
"Aβ42" = "ab42",
"Aβ42/Aβ40" = "ab42_ab40",
"Total Tau" = "tau",
"pTau" = "ptau",
NfL = "nfl",
Healthy = "cdx_cn_vs_ci",
MCI = "cdx_mci_vs_cn",
Dementia = "cdx_dem_vs_cn",
"Cog. Impair." = "cdx_ci_vs_cn"
),
lab = fct_inorder(lab))
```
### Z-test
```{r}
compare_coefs = function(b1, b2, se1, se2, return_pval = FALSE) {
z = (b1 - b2) / sqrt(se1^2 + se2^2)
if (return_pval) {
return(2 * (1 - pnorm(abs(z))))
} else {
return(z)
}
}
res_diff = res_crs %>%
filter(Parameter %in% c("mcaide", "cogd", "libra", "whicap")) %>%
select(term, lab, pop, Std_Coefficient, std.error, category) %>%
filter(!lab %in% c("Aβ42", "Aβ40")) %>%
group_by(term, lab, category) %>%
group_modify(~ {
pops = unique(.x$pop[.x$pop != "all"])
if(length(pops) < 2) return(tibble())
pairs = t(combn(pops, 2)) %>% as.data.frame()
colnames(pairs) = c("pop1", "pop2")
map_dfr(1:nrow(pairs), function(i) {
p1 = pairs$pop1[i]
p2 = pairs$pop2[i]
b1 = .x$Std_Coefficient[.x$pop == p1]
b2 = .x$Std_Coefficient[.x$pop == p2]
se1 = .x$std.error[.x$pop == p1]
se2 = .x$std.error[.x$pop == p2]
z = compare_coefs(b1, b2, se1, se2)
p = compare_coefs(b1, b2, se1, se2, return_pval = TRUE)
tibble(
pop1 = p1,
pop2 = p2,
z_score = z,
p_diff = p
)
})
}) %>%
ungroup() %>%
mutate(p_diff_fdr = p.adjust(p_diff, method = "fdr")) %>%
mutate(
term = recode(term, mcaide = "mCAIDE", whicap = "WHICAP", libra = "LIBRA", cogd = "CogDrisk"),
term = factor(term, levels = c("mCAIDE", "WHICAP", "LIBRA", "CogDrisk")),
pop1 = recode(pop1, "Black" = "AA", "Hispanic" = "LA", "NHW" = "NHW"),
pop2 = recode(pop2, "Black" = "AA", "Hispanic" = "LA", "NHW" = "NHW"),
pop1 = factor(pop1, levels = c("NHW", "LA", "AA")),
pop2 = factor(pop2, levels = c("NHW", "LA", "AA"))) %>%
droplevels()
res_diff = res_diff %>%
mutate(label = case_when(p_diff_fdr < 0.001 ~ "***", p_diff_fdr < 0.01 ~ "**", p_diff_fdr < 0.05 ~ "*")) %>%
rename(
Model = term,
Outcome = lab,
Category = category,
Pop1 = pop1,
Pop2 = pop2,
`Z-score` = z_score,
`P-Value` = p_diff,
`P-Value FDR. adj.` = p_diff_fdr,
Significance = label
)
res_sig = res_diff %>%
filter(p_diff_fdr < 0.05, (pop1 == "NHW" | pop2 == "NHW"))
res_diff_all = res_all %>%
select(term, outcome, pop, Std_Coefficient, std.error) %>%
filter(outcome != "cdx_cn_vs_ci") %>%
group_by(term, outcome) %>%
group_modify(~ {
pops = unique(.x$pop)
pairs = t(combn(pops, 2)) %>% as.data.frame()
colnames(pairs) = c("pop1", "pop2")
map_dfr(1:nrow(pairs), function(i) {
p1 = pairs$pop1[i]
p2 = pairs$pop2[i]
b1 = .x$Std_Coefficient[.x$pop == p1]
b2 = .x$Std_Coefficient[.x$pop == p2]
se1 = .x$std.error[.x$pop == p1]
se2 = .x$std.error[.x$pop == p2]
z = compare_coefs(b1, b2, se1, se2)
p = compare_coefs(b1, b2, se1, se2, return_pval = TRUE)
tibble(
pop1 = p1,
pop2 = p2,
z_score = z,
p_diff = p)
})
}) %>%
ungroup() %>%
mutate(p_diff_fdr = p.adjust(p_diff, method = "fdr"))
```
```{r}
# publication table - main + sensitivity analysis
coef_table_pred = bind_rows(res_crs, res_crs_sva) %>%
select(pop, term, category, lab, p.value, p.fdr, Parameter, Std_Coefficient, CI_low, CI_high) %>%
filter(Parameter %in% c("cogd", "libra", "whicap", "mcaide", "cogd_sva", "whicap_sva", "mcaide_sva")) %>%
filter(!lab %in% c("Healthy", "Aβ42", "Aβ40")) %>%
mutate(term = recode(term,
"cogd" = "CogDRisk",
"libra" = "LIBRA",
"whicap" = "WHICAP",
"mcaide" = "mCAIDE",
"cogd_sva" = "CogDRisk (sensitivity)",
"libra_sva" = "LIBRA (sensitivity)",
"whicap_sva" = "WHICAP (sensitivity)",
"mcaide_sva" = "mCAIDE (sensitivity)"),
lab = fct_recode(lab,
"Executive Function" = "Executive\n Function",
"Verbal Ability" = "Verbal\n Ability",
"Cortical Thickness" = "Cortical \nThickness",
"Hippocampal Volume" = "Hippo. \nVolume")
) %>%
rename(
Race = pop,
CRS = term,
Category = category,
Outcome = lab,
`P-value` = p.value,
`P-value, FDR adj.` = p.fdr,
β = Std_Coefficient,
`Lower Conf. Interval` = CI_low,
`Upper Conf. Interval` = CI_high
) %>%
select(-Parameter)
```
### OR
```{r}
# diagnosis only
color = c(AA = "#E41A1C", LA = "#377EB8", NHW = "#984EA3", all = "black")
diagnosis = ggplot(res_crs %>%
filter(
pop == "all",
outcome %in% c("cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn"),
term %in% c("mcaide", "whicap", "libra", "cogd")) %>%
mutate(
outcome = recode(outcome,
cdx_mci_vs_cn = "MCI",
cdx_dem_vs_cn = "Dementia",
cdx_ci_vs_cn = "MCI + Dementia"),
term = recode(term,
mcaide = "mCAIDE",
whicap = "WHICAP",
libra = "LIBRA",
cogd = "CogDrisk"),
outcome = factor(outcome, levels = c("MCI", "Dementia", "MCI + Dementia")),
term = factor(term, levels = c("mCAIDE", "WHICAP", "LIBRA", "CogDrisk")),
sig_fdr = factor(sig_fdr, levels = c(TRUE, FALSE))
),
aes(x = outcome, y = or, color = term, group = term, alpha = sig_fdr)) +
geom_point(position = position_dodge(width = 0.5), size = 1.5) +
geom_errorbar(aes(ymin = lci, ymax = uci),
position = position_dodge(width = 0.5),
width = 0, size = 0.8) +
geom_hline(yintercept = 1, linetype = 2) +
scale_alpha_manual(values = c(`TRUE` = 1, `FALSE` = 0.1)) +
scale_color_manual(values = c("mCAIDE" = "#003DE6",
"WHICAP" = "#d95f02",
"LIBRA" = "#F22424",
"CogDrisk" = "#005713")) +
ggtitle("All") +
labs(x = "", y = "Odds Ratio", color = "CRS", alpha = "FDR p < 0.05") +
theme_bw() +
theme(
axis.text.x = element_text(size = 8, angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 10, face = "bold", hjust = 0.5),
legend.title = element_text(size = 8),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 10)
)
diagnosis_race = ggplot(res_crs %>%
filter(outcome %in% c("cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn"),
term %in% c("mcaide", "whicap", "libra", "cogd")) %>%
mutate(
outcome = recode(outcome,
cdx_mci_vs_cn = "MCI",
cdx_dem_vs_cn = "Dementia",
cdx_ci_vs_cn = "MCI + Dementia"),
outcome = factor(outcome, levels = c("MCI", "Dementia", "MCI + Dementia")),
term = recode(term,
mcaide = "mCAIDE",
whicap = "WHICAP",
libra = "LIBRA",
cogd = "CogDrisk"),
term = factor(term, levels = c("mCAIDE", "WHICAP", "LIBRA", "CogDrisk")),
pop = recode(pop, "Black" = "AA", "Hispanic" = "LA", "NHW" = "NHW", "all" = "all"),
pop = factor(pop, levels = c("NHW", "LA", "AA", "all")),
sig_fdr = factor(sig_fdr, levels = c(TRUE, FALSE))
),
aes(x = outcome, y = or, color = pop, alpha = sig_fdr)) +
geom_point(aes(group = interaction(term, pop)),
position = position_dodge(width = 0.6), size = 1.5) +
geom_errorbar(aes(ymin = lci, ymax = uci, group = interaction(term, pop)),
position = position_dodge(width = 0.6), width = 0, size = 0.8) +
geom_hline(yintercept = 1, linetype = 2) +
scale_alpha_manual(values = c(`TRUE` = 1, `FALSE` = 0.1)) +
scale_color_manual(values = color) +
labs(x = "", y = "OR (95% CI)", color = "Population", alpha = "FDR p < 0.05") +
facet_wrap(~term, nrow = 1) +
theme_bw() +
theme(
axis.text.x = element_text(size = 6, angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8, hjust = 0.5),
legend.position = "bottom",
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 10)
)
plot_all = res_crs %>%
filter(Parameter %in% c("mcaide", "whicap", "libra", "cogd"),
category %in% c("Neuroimaging", "Plasma \nBiomarkers", "Cognitive \nFunction")) %>%
filter(lab != "Aβ42", lab != "Aβ40") %>%
mutate(
pop = recode(pop, "Black" = "AA", "Hispanic" = "LA", "NHW" = "NHW", "all" = "all"),
pop = factor(pop, levels = c("NHW", "LA", "AA", "all")),
sig_fdr = factor(sig_fdr, levels = c(TRUE, FALSE)),
Parameter = recode(Parameter,
"mcaide" = "mCAIDE",
"whicap" = "WHICAP",
"libra" = "LIBRA",
"cogd" = "CogDRisk"),
Parameter = factor(Parameter, levels = c(
"mCAIDE", "WHICAP", "LIBRA", "CogDRisk")),
category = recode(category,
"Neuroimaging" = "Neuroimaging",
"Plasma \nBiomarkers" = "Plasma Biomarkers",
"Cognitive \nFunction" = "Cognitive Function"),
lab = recode(lab, "Hippo. \nVolume" = "Hippocampal \nVolume",
"WMH" = "White Matter \nHyperintensity"),
lab = factor(lab, levels = c(
"MMSE", "CDR", "Memory", "Verbal\n Ability", "Executive\n Function",
"Hippocampal \nVolume", "Cortical \nThickness",
"NfL", "Total Tau", "pTau", "Aβ42/Aβ40"))) %>%
ggplot(aes(x = lab, y = Std_Coefficient, color = pop, alpha = sig_fdr)) +
geom_hline(yintercept = 0, linetype = 2) +
geom_point(position = position_dodge(width = 0.6), size = 1.5) +
geom_errorbar(aes(ymin = CI_low, ymax = CI_high),
position = position_dodge(width = 0.6), width = 0, size = 0.8) +
facet_grid(Parameter ~ category, scales = "free", space = "free_x") +
scale_color_manual(values = color) +
scale_alpha_manual(values = c(`TRUE` = 1, `FALSE` = 0.3)) +
#scale_y_continuous(limits = c(-0.52, 0.48), breaks = seq(-0.5, 0.5, 0.25)) +
labs(
x = "",
y = "Beta (95% CI)",
color = "Population",
alpha = "FDR p < 0.05"
) +
theme_bw() +
theme(
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 10),
legend.text = element_text(size = 10),
legend.title = element_text(size = 10),
legend.position = "bottom")
```
### AUC + R2
```{r}
adjust_crs = function(df_models, pop_label = NULL) {
df_data = if (!is.null(pop_label)) data %>% filter(pop == pop_label) else data
df_models %>%
mutate(
pop = ifelse(is.null(pop_label), "ALL", pop_label),
eq_crs_only = case_when(
model %in% c("base", "base_noapoe") ~ eq,
TRUE ~ str_replace(eq, "apoe \\+ ", "")
),
res_crs_only = map(eq_crs_only, ~ glm(.x, data = df_data, family = binomial)),
auc_crs_only = map_dbl(res_crs_only, ~ {
y = model.response(model.frame(.x))
p = predict(.x, type = "response")
if (length(unique(y)) > 1) pROC::auc(pROC::roc(y, p)) else NA_real_
}),
nagelkerker2_crs_only = map_dbl(res_crs_only, ~ {
if (length(unique(model.response(model.frame(.x)))) > 1)
pscl::pR2(.x)[["McFadden"]]
else NA_real_
})
)
}
afr_cdx_prs2 = adjust_crs(afr_cdx_prs, pop_label = "AFR") %>% mutate(pop = "Black")
amr_cdx_prs2 = adjust_crs(amr_cdx_prs, pop_label = "AMR") %>% mutate(pop = "Hispanic")
eur_cdx_prs2 = adjust_crs(eur_cdx_prs, pop_label = "EUR") %>% mutate(pop = "NHW")
all_cdx_prs2 = adjust_crs(all_cdx_prs) %>% mutate(pop = "all")
all_comb_cdx2 = bind_rows(all_cdx_prs2, afr_cdx_prs2, amr_cdx_prs2, eur_cdx_prs2)
plot_roc_outcome = function(df, outcome_name, crs_type = "CRS", pop_filter = NULL) {
crs_colors = c(
"Demographics+APOE" = "#7A3900",
"Demographics" = "#7A1100",
"mCAIDE" = "#003DE6",
"mCAIDE-" = "#003DE6",
"WHICAP" = "#d95f02",
"WHICAP-" = "#d95f02",
"LIBRA" = "#F22424",
"LIBRA-" = "#F22424",
"CogDRisk" = "#005713",
"CogDRisk-" = "#005713"
)
df_out = df %>% filter(outcome == outcome_name)
if (!is.null(pop_filter)) df_out = df_out %>% filter(pop == pop_filter)
df_out = df_out %>%
mutate(
outcome = recode(outcome,
"cdx_mci_vs_cn" = "MCI",
"cdx_dem_vs_cn" = "Dementia",
"cdx_ci_vs_cn" = "MCI + Dementia"),
model = recode(model,
"base" = "Demographics+APOE",
"base_noapoe" = "Demographics",
"libra" = "LIBRA",
"mcaide" = "mCAIDE",
"whicap" = "WHICAP",
"cogd" = "CogDRisk",
"libra_sva" = "LIBRA-",
"mcaide_sva" = "mCAIDE-",
"whicap_sva" = "WHICAP-",
"cogd_sva" = "CogDRisk-")) %>%
mutate(model = factor(model, levels = c(
"CogDRisk", "LIBRA", "WHICAP", "mCAIDE", "Demographics", "Demographics+APOE",
"mCAIDE-", "WHICAP-", "LIBRA-", "CogDRisk-")))
if (crs_type == "CRS") {
df_out = df_out %>% filter(!grepl("-$", model) | model %in% c("Demographics+APOE", "Demographics"))
} else if (crs_type == "CRS-") {
df_out = df_out %>% filter(grepl("-$", model) | model %in% c("Demographics+APOE", "Demographics"))
}
desired_order = c(
"CogDRisk", "LIBRA", "WHICAP", "mCAIDE", "Demographics", "Demographics+APOE")
df_out$model = factor(df_out$model, levels = desired_order)
# compute ROC curves
df_out = df_out %>%
mutate(
roc_obj = map(res_crs_only, ~ {
y = model.response(model.frame(.x))
p = predict(.x, type = "response")
if (length(unique(y)) > 1) pROC::roc(y, p) else NULL
}),
auc_val = map_dbl(roc_obj, ~ if (!is.null(.x)) as.numeric(pROC::auc(.x)) else NA_real_),
auc_label = paste0(model, ": AUC = ", round(auc_val, 2)),
roc_df = map(roc_obj, ~ if (!is.null(.x)) tibble(fpr = 1 - .x$specificities,
tpr = .x$sensitivities) else NULL)
)
roc_plot_df = df_out %>%
select(model, auc_val, auc_label, roc_df) %>%
tidyr::unnest(roc_df)
format_r2 = function(x) {
ifelse(x < 0.01, formatC(x, format = "f", digits = 4), formatC(x, format = "f", digits = 3))
}
auc_labels_df = df_out %>%
select(model, auc_val, nagelkerker2_crs_only) %>%
distinct(model, auc_val, nagelkerker2_crs_only) %>%
arrange(factor(model, levels = desired_order)) %>%
mutate(
auc_label = paste0(model, ": ", round(auc_val, 2),
"; ", format_r2(nagelkerker2_crs_only)),
fpr = 0.98, tpr = 0.02 + 0.05 * (row_number())
)
ggplot(roc_plot_df, aes(x = fpr, y = tpr, color = model)) +
geom_line(size = 0.5) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray50") +
geom_text(
data = auc_labels_df,
aes(x = fpr, y = tpr, label = auc_label),
inherit.aes = FALSE, hjust = 1, vjust = 0, size = 2
) +
scale_color_manual(values = crs_colors[levels(df_out$model)]) +
labs(x = "False Positive Rate",
y = "True Positive Rate",
color = NULL,
title = unique(df_out$outcome)) +
theme_bw() +
theme(
axis.title = element_text(size = 6),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 8, face = "bold")
)
}
#MCI
roc_mci_all = plot_roc_outcome(all_comb_cdx2, "cdx_mci_vs_cn", crs_type = "CRS", pop_filter = "all")
roc_mci_aa = plot_roc_outcome(all_comb_cdx2, "cdx_mci_vs_cn", crs_type = "CRS", pop_filter = "Black")
roc_mci_la = plot_roc_outcome(all_comb_cdx2, "cdx_mci_vs_cn", crs_type = "CRS", pop_filter = "Hispanic")
roc_mci_nhw = plot_roc_outcome(all_comb_cdx2, "cdx_mci_vs_cn", crs_type = "CRS", pop_filter = "NHW")
#Dementia
roc_dem_all = plot_roc_outcome(all_comb_cdx2, "cdx_dem_vs_cn", crs_type = "CRS", pop_filter = "all")
roc_dem_aa = plot_roc_outcome(all_comb_cdx2, "cdx_dem_vs_cn", crs_type = "CRS", pop_filter = "Black")
roc_dem_la = plot_roc_outcome(all_comb_cdx2, "cdx_dem_vs_cn", crs_type = "CRS", pop_filter = "Hispanic")
roc_dem_nhw = plot_roc_outcome(all_comb_cdx2, "cdx_dem_vs_cn", crs_type = "CRS", pop_filter = "NHW")
#MCI+Dementia
roc_ci_all = plot_roc_outcome(all_comb_cdx2, "cdx_ci_vs_cn", crs_type = "CRS", pop_filter = "all")
roc_ci_aa = plot_roc_outcome(all_comb_cdx2, "cdx_ci_vs_cn", crs_type = "CRS", pop_filter = "Black")
roc_ci_la = plot_roc_outcome(all_comb_cdx2, "cdx_ci_vs_cn", crs_type = "CRS", pop_filter = "Hispanic")
roc_ci_nhw = plot_roc_outcome(all_comb_cdx2, "cdx_ci_vs_cn", crs_type = "CRS", pop_filter = "NHW")
shared_legend = get_legend(
roc_mci_all +
theme(
legend.position = "bottom",
legend.title = element_text(size = 8, face = "bold"),
legend.text = element_text(size = 6)) +
guides(color = guide_legend(nrow = 1)))
pageCreate(width = 9, height = 12.5, default.units = "inches")
plotGG(
plot = roc_mci_all,
x = 0, y = 0, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotText(
label = "A)", x = 0.1, y = 0.1,
just = c("left", "top"), fontface = "bold",
fontsize = 12, default.units = "inches"
)
plotGG(
plot = roc_dem_all,
x = 3, y = 0, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_ci_all,
x = 6, y = 0, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_mci_aa,
x = 0, y = 3, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotText(
label = "B)", x = 0.1, y = 3.1,
just = c("left", "top"), fontface = "bold",
fontsize = 12, default.units = "inches"
)
plotGG(
plot = roc_dem_aa,
x = 3, y = 3, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_ci_aa,
x = 6, y = 3, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_mci_la,
x = 0, y = 6, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotText(
label = "C)", x = 0.1, y = 6.1,
just = c("left", "top"), fontface = "bold",
fontsize = 12, default.units = "inches"
)
plotGG(
plot = roc_dem_la,
x = 3, y = 6, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_ci_la,
x = 6, y = 6, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_mci_nhw,
x = 0, y = 9, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotText(
label = "D)", x = 0.1, y = 9.1,
just = c("left", "top"), fontface = "bold",
fontsize = 12, default.units = "inches"
)
plotGG(
plot = roc_dem_nhw,
x = 3, y = 9, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
plot = roc_ci_nhw,
x = 6, y = 9, width = 3, height = 3,
just = c("left", "top"), default.units = "inches"
)
plotGG(
shared_legend,
x = 2, y = 12, width = 6, height = 0.5,
just = c("left", "top")
)
pageGuideHide()
```
```{r}
# publication table for AUC & R2 for linear and logistic regression
adjust_crs_linear = function(df_models, pop_label = NULL) {
df_data = if (!is.null(pop_label)) data %>% filter(pop == pop_label) else data
df_models %>%
mutate(
pop = ifelse(is.null(pop_label), "ALL", pop_label),
eq_crs_only = case_when(
model %in% c("base", "base_noapoe") ~ eq,
TRUE ~ str_replace(eq, "apoe \\+ ", "")
),
res_crs_only = map(eq_crs_only, ~ lm(as.formula(.x), data = df_data)),
r2_crs_only = map_dbl(res_crs_only, ~ {
summ <- summary(.x)
if (!is.null(summ$r.squared)) summ$r.squared else NA_real_
})
)
}
afr_num_prs2 = adjust_crs_linear(afr_num_prs, pop_label = "AFR") %>% mutate(pop = "Black")
amr_num_prs2 = adjust_crs_linear(amr_num_prs, pop_label = "AMR") %>% mutate(pop = "Hispanic")
eur_num_prs2 = adjust_crs_linear(eur_num_prs, pop_label = "EUR") %>% mutate(pop = "NHW")
all_num_prs2 = adjust_crs_linear(all_num_prs) %>% mutate(pop = "all")
all_comb_num2 = bind_rows(all_num_prs2, afr_num_prs2, amr_num_prs2, eur_num_prs2)
res_pfm = bind_rows(
select(all_comb_cdx2, model, pop, outcome, auc_crs_only, nagelkerker2_crs_only),
select(all_comb_num2, model, pop, outcome, r2_crs_only)) %>%
filter(model %in% c("base", "base_noapoe", "cogd", "libra", "whicap", "mcaide", "cogd_sva", "libra_sva", "whicap_sva", "mcaide_sva")) %>%
filter(!outcome %in% c("cdx_hypertension", "cdx_dyslipidemia", "cdx_diabetes", "bmi", "cdx_anxiety", "cdx_depression", "cdx_cvd", "wmh_volume_log",
"ab40", "ab42", "cdx_cn_vs_ci")) %>%
arrange(pop, model) %>%
mutate(
model = recode(model,
"base_noapoe" = "Demographics",
"base" = "Demographics + APOE",
"cogd" = "CogDRisk",
"libra" = "LIBRA",
"whicap" = "WHICAP",
"mcaide" = "mCAIDE",
"cogd_sva" = "CogDRisk (sensitivity)",
"libra_sva" = "LIBRA (sensitivity)",
"whicap_sva" = "WHICAP (sensitivity)",
"mcaide_sva" = "mCAIDE (sensitivity)"),
category = case_when(
outcome %in% c("va", "em", "ef", "mmse_total", "cdr") ~ "Cognitive \nFunction",
outcome %in% c("tau", "ptau", "nfl", "ab42_ab40") ~ "Plasma \nBiomarkers",
outcome %in% c("meta_roi", "z_hippcampul_vol") ~ "Neuroimaging",
outcome %in% c("cdx_mci_vs_cn", "cdx_dem_vs_cn", "cdx_ci_vs_cn") ~ "Medical \nHistory"),
lab = fct_recode(
outcome,
MMSE = "mmse_total",
CDR = "cdr",
"Executive\n Function" = "ef",
"Verbal\n Ability" = "va",
Memory = "em",
"Cortical \nThickness" = "meta_roi",
"Hippo. \nVolume" = "z_hippcampul_vol",
"Aβ42/Aβ40" = "ab42_ab40",
"Total Tau" = "tau",
"pTau" = "ptau",
NfL = "nfl",
MCI = "cdx_mci_vs_cn",
Dementia = "cdx_dem_vs_cn",
"Cog. Impair." = "cdx_ci_vs_cn"),
lab = fct_inorder(lab)
) %>%
rename(
Model = model,
Race = pop,
Outcome = lab,
Category = category,
AUC = auc_crs_only,
`Nagelkerke R2` = nagelkerker2_crs_only,
R2 = r2_crs_only
) %>%
select(-outcome) %>%
select(Model, Race, Outcome, Category, AUC, `Nagelkerke R2`, R2)
```
### Demographics
```{r}
# table 1
summary_df = data %>%
filter(!is.na(cdx_cog)) %>%
group_by(cdx_cog) %>%
summarise(
Total = n(),
Age = sprintf("%.1f (± %.1f)", mean(age, na.rm = TRUE), sd(age, na.rm = TRUE)),
Female = sprintf("%d (%.1f%%)", sum(sex == 1, na.rm = TRUE), 100 * mean(sex == 1, na.rm = TRUE)),
Male = sprintf("%d (%.1f%%)", sum(sex == 0, na.rm = TRUE), 100 * mean(sex == 0, na.rm = TRUE)),
`ε4 Carrier` = sprintf("%d (%.1f%%)", sum(apoe4 == 1, na.rm = TRUE), 100 * mean(apoe4 == 1, na.rm = TRUE)),
`ε4 Noncarrier` = sprintf("%d (%.1f%%)", sum(apoe4 == 0, na.rm = TRUE), 100 * mean(apoe4 == 0, na.rm = TRUE)),
mCAIDE = sprintf("%.3f (± %.2f)", mean(mcaide, na.rm = TRUE), sd(mcaide, na.rm = TRUE)),
WHICAP = sprintf("%.3f (± %.2f)", mean(whicap, na.rm = TRUE), sd(whicap, na.rm = TRUE)),
LIBRA = sprintf("%.3f (± %.2f)", mean(libra, na.rm = TRUE), sd(libra, na.rm = TRUE)),
CogDRisk = sprintf("%.3f (± %.2f)", mean(cogd, na.rm = TRUE), sd(cogd, na.rm = TRUE)),
MMSE = sprintf("%.2f (± %.2f)", mean(mmse_total, na.rm = TRUE), sd(mmse_total, na.rm = TRUE)),
CDR = sprintf("%.4f (± %.2f)", mean(cdr, na.rm = TRUE), sd(cdr, na.rm = TRUE)),
`Executive\n Function` = sprintf("%.2f (± %.2f)", mean(ef, na.rm = TRUE), sd(ef, na.rm = TRUE)),
`Verbal\n Ability` = sprintf("%.2f (± %.2f)", mean(va, na.rm = TRUE), sd(va, na.rm = TRUE)),
Memory = sprintf("%.2f (± %.2f)", mean(em, na.rm = TRUE), sd(em, na.rm = TRUE)),
`Cortical \nThickness` = sprintf("%.2f (± %.2f)", mean(meta_roi, na.rm = TRUE), sd(meta_roi, na.rm = TRUE)),
`Hippo. \nVolume` = sprintf("%.2f (± %.2f)", mean(z_hippcampul_vol, na.rm = TRUE), sd(z_hippcampul_vol, na.rm = TRUE)),
`Aβ42/Aβ40` = sprintf("%.3f (± %.2f)", mean(ab42_ab40, na.rm = TRUE), sd(ab42_ab40, na.rm = TRUE)),
`Total Tau` = sprintf("%.3f (± %.2f)", mean(tau, na.rm = TRUE), sd(tau, na.rm = TRUE)),
pTau = sprintf("%.2f (± %.2f)", mean(ptau, na.rm = TRUE), sd(ptau, na.rm = TRUE)),
NfL = sprintf("%.3f (± %.2f)", mean(nfl, na.rm = TRUE), sd(nfl, na.rm = TRUE))
) %>%
tibble::column_to_rownames("cdx_cog") %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column("Variable")
# racial breakdown rows
race_summary = data %>%
filter(!is.na(cdx_cog), race %in% c("NHW", "Hispanic", "Black")) %>%
group_by(cdx_cog, race) %>%
summarise(n = n(), .groups = "drop_last") %>%
mutate(
perc = round(100 * n / sum(n), 1),
value = sprintf("%d (%.1f%%)", n, perc)
) %>%
select(cdx_cog, Variable = race, value) %>%
tidyr::pivot_wider(names_from = cdx_cog, values_from = value)
summary_df = bind_rows(summary_df, race_summary)
summary_df = summary_df %>%
mutate(
Group = case_when(
Variable %in% c("Total", "Case", "Control") ~ "AD",
Variable == "Age" ~ "Age",
Variable %in% c("Female", "Male") ~ "Sex",
Variable %in% c("NHW", "Hispanic", "Black") ~ "Race",
Variable %in% c("ε4 Carrier", "ε4 Noncarrier") ~ "APOE",
Variable %in% c("mCAIDE", "WHICAP", "LIBRA", "CogDRisk") ~ "CRS",
Variable %in% c("MMSE", "CDR", "Executive\n Function", "Verbal\n Ability", "Memory") ~ "Cognition",
Variable %in% c("Cortical \nThickness", "Hippo. \nVolume") ~ "Imaging",
Variable %in% c("Aβ40", "Aβ42", "Aβ42/Aβ40", "Total Tau", "pTau", "NfL") ~ "Biomarker",
TRUE ~ NA_character_
)) %>%
rename(
Characteristics = Variable,
`Cognitively Normal` = "0",
`Mild Cognitive Impairment` = "1",
Dementia = "2"
) %>%
select(-Group)
```

crs_analysis.qmd at commit aea67ca, under MIT · at the source

Overview

Authors: Meri Okorie^1,2, Xiaqing Jiang², Kristine Yaffe^1,2,3,4,5, Jennifer S Yokoyama^1,3,6, Shea J Andrews^1,2, for the Health and Aging Brain Study–Health Disparities

ORCID iDs: Meri Okorie, Jennifer S Yokoyama, Shea J Andrews

Edward and Pearl Fein Memory and Aging Center, University of California, San Francisco, San Francisco, California, USA
Department of Psychiatry and Behavioural Sciences, University of California, San Francisco, San Francisco, California, USA
Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
San Francisco Veterans Affairs Health Care System, San Francisco, California, USA
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA

Institutions: University of California, San Francisco (United States); San Francisco VA Health Care System (United States)

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 8, article e71567

Dates: received 4 February 2026; accepted 4 May 2026; published online 28 July 2026; in print August 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1002/alz.71567 · PMID 42522069 · PMCID PMC13415752 · OpenAlex W7171670744

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)

Methods: Statistics, Machine learning, Physiology & signal measures, Preprocessing

Keywords: Alzheimer's disease, biomarkers, clinical risk score, health disparities, modifiable risk factors

MeSH: Alzheimer Disease*, Dementia*, Endophenotypes*, Aged, Aged, 80 and over, Amyloid beta-Peptides, Female, Humans, Male, Peptide Fragments, Positron-Emission Tomography, Risk Factors, tau Proteins (* major topic)

Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: NIA NIH HHS (R01 AG057234, U24 AG072122, P30AG062422, U19 AG078109, R01 AG062588, U19 AG079774, U19AG079774, R01 AG054073, R01 AG058533, P01 AG019724, P01AG019724, R01AG057234, P30 AG062422, R01 AG070862); Alzheimer's Association; Genentech; NIBIB NIH HHS (P41 EB015922); Global Brain Health Institute; National Institute on Aging (R01AG057234, P30AG062422, U19AG079774, P01AG019724); Alzheimer’s Association; National Alzheimer's Coordinating Center (U24AG072122); National Alzheimer’s Coordinating Center (U24AG072122); NINDS NIH HHS (U54 NS123985, U54NS123985); French Foundation; National Institute of Neurological Disorders and Stroke (U54NS123985); Rainwater Charitable Foundation; Mary Oakley Foundation

Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

AndrewsLabUCSF/CRS-analysis

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: aea67caca4b114ff2aa32bb4bf5d244127574e7b, 25 March 2026

Languages: Quarto (4)

Size: 11 files, 4 scripts

Software Heritage: not archived

Found in: “CODE AVAILABILITY”

Holds: README, license file, documentation, 4 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration

Tools: tidyverse (4 files), broom (3 files), ggplot2 (2 files), pROC (2 files), cowplot (1 file), data.table (1 file), easystats (1 file), patchwork (1 file), randomForest (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

6 files

workflow/scripts/crs_analysis.qmd, Quarto, 2,407 lines, 7 matches
workflow/scripts/crs_standardization.qmd, Quarto, 471 lines, 3 matches
workflow/scripts/cutoff_analysis.qmd, Quarto, 401 lines, 1 match
workflow/scripts/imputation.qmd, Quarto, 472 lines, 2 matches
LICENSE, License, 21 lines
README.md, Text, 154 lines

At the source: github.com/AndrewsLabUCSF/CRS-analysis

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Read it in the paper: doi.org/10.1002/alz.71567.

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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 13 MeSH terms, 14 funders, 51 references.

Cite

This paper

Okorie, M., Jiang, X., Yaffe, K., Yokoyama, J. S., Andrews, S. J., & for the Health and Aging Brain Study–Health Disparities. (2026). Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(8), e71567. https://doi.org/10.1002/alz.71567

BibTeX

@article{okorie2026associations, author = {Okorie, Meri and Jiang, Xiaqing and Yaffe, Kristine and Yokoyama, Jennifer S and Andrews, Shea J and {for the Health and Aging Brain Study–Health Disparities}}, title = {{Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population}}, journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association}, year = {2026}, month = aug, volume = {22}, number = {8}, pages = {e71567}, publisher = {Wiley}, issn = {1552-5260}, doi = {10.1002/alz.71567}, url = {https://doi.org/10.1002/alz.71567}, pmid = {42522069}, pmcid = {PMC13415752} }

RIS

TY - JOUR AU - Okorie, Meri AU - Jiang, Xiaqing AU - Yaffe, Kristine AU - Yokoyama, Jennifer S AU - Andrews, Shea J AU - for the Health and Aging Brain Study–Health Disparities TI - Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association J2 - Alzheimers Dement PY - 2026 DA - 2026/08/01 VL - 22 IS - 8 SP - e71567 SN - 1552-5260 PB - Wiley DO - 10.1002/alz.71567 UR - https://doi.org/10.1002/alz.71567 LA - en ER -

CSL-JSON

{ "id": "10.1002/alz.71567", "type": "article-journal", "title": "Associations of dementia polyexposure scores to Alzheimer's disease endophenotypes in a diverse population", "container-title": "Alzheimer's & dementia : the journal of the Alzheimer's Association", "author": [ { "family": "Okorie", "given": "Meri" }, { "family": "Jiang", "given": "Xiaqing" }, { "family": "Yaffe", "given": "Kristine" }, { "family": "Yokoyama", "given": "Jennifer S" }, { "family": "Andrews", "given": "Shea J" }, { "literal": "for the Health and Aging Brain Study–Health Disparities" } ], "container-title-short": "Alzheimers Dement", "volume": "22", "issue": "8", "page": "e71567", "DOI": "10.1002/alz.71567", "PMID": "42522069", "PMCID": "PMC13415752", "ISSN": "1552-5260", "publisher": "Wiley", "URL": "https://doi.org/10.1002/alz.71567", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 1 ] ] } }

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