Sleep Disturbances and Cognition, Behavior, and Brain Structure in Children With mTBI.
The 3 matches
- [1] § Methods › Statistical Analysis › Main Analysis ↔ Sleep_Paper_3_Summary_Scores.R, lines 362–445 · score 0.66 · linear mixed, sleep trajectories, exploratory, models, onset, subscales
- [2] § Methods › Sample ↔ Sleep_Paper_1_SampleMeasures.R, lines 300–343 · score 0.61 · broken bones, OI controls, history, matched, income, mTBI
- [3] § Methods › Statistical Analysis › Main Analysis ↔ Sleep_Paper_2_Bootstrapping_Analysis.R, lines 411–455 · score 0.53 · linear mixed, sleep trajectories, models, mTBI, behavior, FA
Paper
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The authors' code
R · 445 lines · 51 KB · MIT · 1 match
- ################### ################### ################### ###################
- #Summary Scores: Means and 95% Confidence Intervals across Iterations
- ################### ################### ################### ###################
- df_groupcomp <- data.frame(groupcomp.mtrx)
- summary_groupcomp <- data.frame(
- Comparison = c("OI-mTBI", "TDC-mTBI", "TDC-OI"),
- mean = c(mean(df_groupcomp$X1), mean(df_groupcomp$X2), mean(df_groupcomp$X3)),
- lower = c(quantile(na.rm=TRUE,df_groupcomp$X1, prob=0.025), quantile(na.rm=TRUE,df_groupcomp$X2, prob=0.025), quantile(na.rm=TRUE,df_groupcomp$X3, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_groupcomp$X1, prob=0.975), quantile(na.rm=TRUE,df_groupcomp$X2, prob=0.975), quantile(na.rm=TRUE,df_groupcomp$X3, prob=0.975))
- )
- df_groupcomp_means <- data.frame(groupcomp.mtrx.means)
- summary_groupcomp_means <- data.frame(
- Group = c("mTBI", "OI", "TDC"),
- mean = c(mean(df_groupcomp_means$X1), mean(df_groupcomp_means$X2), mean(df_groupcomp_means$X3)),
- lower = c(quantile(na.rm=TRUE,df_groupcomp_means$X1, prob=0.025), quantile(na.rm=TRUE,df_groupcomp_means$X2, prob=0.025), quantile(na.rm=TRUE,df_groupcomp_means$X3, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_groupcomp_means$X1, prob=0.975), quantile(na.rm=TRUE,df_groupcomp_means$X2, prob=0.975), quantile(na.rm=TRUE,df_groupcomp_means$X3, prob=0.975))
- )
- df_traj <- data.frame(traj.mtrx)
- summary_traj <- data.frame(
- Comparison = c("Improving - Chronic", "New Onset - Chronic", "Normal - Chronic", "New Onset - Improving", "Normal - Improving", "Normal - New Onset"),
- mean = c(mean(df_traj$X1), mean(df_traj$X2), mean(df_traj$X3), mean(df_traj$X4), mean(df_traj$X5), mean(df_traj$X6)),
- lower = c(quantile(na.rm=TRUE,df_traj$X1, prob=0.025), quantile(na.rm=TRUE,df_traj$X2, prob=0.025), quantile(na.rm=TRUE,df_traj$X3, prob=0.025), quantile(na.rm=TRUE,df_traj$X4, prob=0.025), quantile(na.rm=TRUE,df_traj$X5, prob=0.025), quantile(na.rm=TRUE,df_traj$X6, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_traj$X1, prob=0.975), quantile(na.rm=TRUE,df_traj$X2, prob=0.975), quantile(na.rm=TRUE,df_traj$X3, prob=0.975), quantile(na.rm=TRUE,df_traj$X4, prob=0.975), quantile(na.rm=TRUE,df_traj$X5, prob=0.975), quantile(na.rm=TRUE,df_traj$X6, prob=0.975))
- )
- ###################
- #Summary Scores: differences in outcomes between the groups
- ###################
- df_thk <- data.frame(coef.mtrx.thk)
- summary_thk <- data.frame(
- variable = c("Thickness", "Thickness"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_thk$X1), mean(df_thk$X2)),
- lower = c(quantile(na.rm=TRUE,df_thk$X1, prob=0.025), quantile(na.rm=TRUE,df_thk$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_thk$X1, prob=0.975), quantile(na.rm=TRUE,df_thk$X2, prob=0.975))
- )
- df_vol <- data.frame(coef.mtrx.vol)
- summary_vol <- data.frame(
- variable = c("Volume", "Volume"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_vol$X1), mean(df_vol$X2)),
- lower = c(quantile(na.rm=TRUE,df_vol$X1, prob=0.025), quantile(na.rm=TRUE,df_vol$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_vol$X1, prob=0.975), quantile(na.rm=TRUE,df_vol$X2, prob=0.975))
- )
- df_fa <- data.frame(coef.mtrx.fa)
- summary_fa <- data.frame(
- variable = c("Fractional Anisotropy", "Fractional Anisotropy"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_fa$X1), mean(df_fa$X2)),
- lower = c(quantile(na.rm=TRUE,df_fa$X1, prob=0.025), quantile(na.rm=TRUE,df_fa$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_fa$X1, prob=0.975), quantile(na.rm=TRUE,df_fa$X2, prob=0.975))
- )
- summary_imaging <- bind_rows(summary_thk, summary_vol, summary_fa)
- df_thk_ES <- data.frame(coef.mtrx.thk.ES)
- summary_thk_ES <- data.frame(
- variable = c("Thickness", "Thickness"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_thk_ES$X1), mean(df_thk_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_thk_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_thk_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_thk_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_thk_ES$X2, prob=0.975))
- )
- df_vol_ES <- data.frame(coef.mtrx.vol.ES)
- summary_vol_ES <- data.frame(
- variable = c("Volume", "Volume"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_vol_ES$X1), mean(df_vol_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_vol_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_vol_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_vol_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_vol_ES$X2, prob=0.975))
- )
- df_fa_ES <- data.frame(coef.mtrx.fa.ES)
- summary_fa_ES <- data.frame(
- variable = c("Fractional Anisotropy", "Fractional Anisotropy"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_fa_ES$X1), mean(df_fa_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_fa_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_fa_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_fa_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_fa_ES$X2, prob=0.975))
- )
- df_sleep <- data.frame(coef.mtrx.sleep)
- summary_sleep <- data.frame(
- variable = c("Sleep", "Sleep"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep$X1), mean(df_sleep$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep$X2, prob=0.975))
- )
- df_cbcl <- data.frame(coef.mtrx.cbcl)
- summary_cbcl <- data.frame(
- variable = c("Behavior Problems", "Behavior Problems"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_cbcl$X1), mean(df_cbcl$X2)),
- lower = c(quantile(na.rm=TRUE,df_cbcl$X1, prob=0.025), quantile(na.rm=TRUE,df_cbcl$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_cbcl$X1, prob=0.975), quantile(na.rm=TRUE,df_cbcl$X2, prob=0.975))
- )
- df_cog <- data.frame(coef.mtrx.cog)
- summary_cog <- data.frame(
- variable = c("Cognition", "Cognition"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_cog$X1), mean(df_cog$X2)),
- lower = c(quantile(na.rm=TRUE,df_cog$X1, prob=0.025), quantile(na.rm=TRUE,df_cog$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_cog$X1, prob=0.975), quantile(na.rm=TRUE,df_cog$X2, prob=0.975))
- )
- df_sleep_ES <- data.frame(coef.mtrx.sleep.ES)
- summary_sleep_ES <- data.frame(
- variable = c("Sleep", "Sleep"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_ES$X1), mean(df_sleep_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_ES$X2, prob=0.975))
- )
- df_cbcl_ES <- data.frame(coef.mtrx.cbcl.ES)
- summary_cbcl_ES <- data.frame(
- variable = c("Behavior Problems", "Behavior Problems"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_cbcl_ES$X1), mean(df_cbcl_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_cbcl_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_cbcl_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_cbcl_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_cbcl_ES$X2, prob=0.975))
- )
- df_cog_ES <- data.frame(coef.mtrx.cog.ES)
- summary_cog_ES <- data.frame(
- variable = c("Cognition", "Cognition"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_cog_ES$X1), mean(df_cog_ES$X2)),
- lower = c(quantile(na.rm=TRUE,df_cog_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_cog_ES$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_cog_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_cog_ES$X2, prob=0.975))
- )
- summary_clinical <- bind_rows(summary_sleep, summary_cbcl, summary_cog)
- summary_ES <- bind_rows(summary_cbcl_ES,summary_cog_ES,summary_thk_ES, summary_vol_ES, summary_fa_ES)
- ###################
- #Summary Scores: Sensitivity: associations between sleep at follow-up and outcomes
- ###################
- df_sleep_CBCL <- data.frame(coef.mtrx.sleep_cbcl) %>% mutate(X1 = coef.mtrx.sleep_cbcl)
- summary_sleep_CBCL <- data.frame(
- Outcome = c("Behavior Problems"),
- Timepoint = c("Follow-Up"),
- mean = c(mean(df_sleep_CBCL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_CBCL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_CBCL$X1, prob=0.975))
- )
- df_sleep_cog <- data.frame(coef.mtrx.sleep_cog)%>% mutate(X1 = coef.mtrx.sleep_cog)
- summary_sleep_cog <- data.frame(
- Outcome = c("Cognition"),
- Timepoint = c("Follow-Up"),
- mean = c(mean(df_sleep_cog$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_cog$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_cog$X1, prob=0.975))
- )
- df_sleep_thk <- data.frame(coef.mtrx.sleep_thk)%>% mutate(X1 = coef.mtrx.sleep_thk)
- summary_sleep_thk <- data.frame(
- Outcome = c("Thickness"),
- Timepoint = c("Follow-Up"),
- mean = c(mean(df_sleep_thk$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_thk$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_thk$X1, prob=0.975))
- )
- df_sleep_vol <- data.frame(coef.mtrx.sleep_vol)%>% mutate(X1 = coef.mtrx.sleep_vol)
- summary_sleep_vol <- data.frame(
- Outcome = c("Volume"),
- Timepoint = c("Follow-Up"),
- mean = c(mean(df_sleep_vol$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_vol$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_vol$X1, prob=0.975))
- )
- df_sleep_fa <- data.frame(coef.mtrx.sleep_fa)%>% mutate(X1 = coef.mtrx.sleep_fa)
- summary_sleep_fa <- data.frame(
- Outcome = c("Fractional Anisotropy"),
- Timepoint = c("Follow-Up"),
- mean = c(mean(df_sleep_fa$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_fa$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_fa$X1, prob=0.975))
- )
- summary_sleep_2y <- bind_rows(summary_sleep_CBCL, summary_sleep_cog, summary_sleep_thk, summary_sleep_vol, summary_sleep_fa)
- ###################
- #Summary Scores: Sensitivity: associations between sleep at baseline and outcomes
- ###################
- df_sleep_CBCL_BL <- data.frame(coef.mtrx.sleep_cbcl_BL) %>% mutate(X1 = coef.mtrx.sleep_cbcl_BL)
- summary_sleep_CBCL_BL <- data.frame(
- Outcome = c("Behavior Problems"),
- Timepoint = c("Baseline"),
- mean = c(mean(df_sleep_CBCL_BL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_CBCL_BL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_CBCL_BL$X1, prob=0.975))
- )
- df_sleep_cog_BL <- data.frame(coef.mtrx.sleep_cog_BL)%>% mutate(X1 = coef.mtrx.sleep_cog_BL)
- summary_sleep_cog_BL <- data.frame(
- Outcome = c("Cognition"),
- Timepoint = c("Baseline"),
- mean = c(mean(df_sleep_cog_BL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_cog_BL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_cog_BL$X1, prob=0.975))
- )
- df_sleep_thk_BL <- data.frame(coef.mtrx.sleep_thk_BL)%>% mutate(X1 = coef.mtrx.sleep_thk_BL)
- summary_sleep_thk_BL <- data.frame(
- Outcome = c("Thickness"),
- Timepoint = c("Baseline"),
- mean = c(mean(df_sleep_thk_BL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_thk_BL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_thk_BL$X1, prob=0.975))
- )
- df_sleep_vol_BL <- data.frame(coef.mtrx.sleep_vol_BL)%>% mutate(X1 = coef.mtrx.sleep_vol_BL)
- summary_sleep_vol_BL <- data.frame(
- Outcome = c("Volume"),
- Timepoint = c("Baseline"),
- mean = c(mean(df_sleep_vol_BL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_vol_BL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_vol_BL$X1, prob=0.975))
- )
- df_sleep_fa_BL <- data.frame(coef.mtrx.sleep_fa_BL)%>% mutate(X1 = coef.mtrx.sleep_fa_BL)
- summary_sleep_fa_BL <- data.frame(
- Outcome = c("Fractional Anisotropy"),
- Timepoint = c("Baseline"),
- mean = c(mean(df_sleep_fa_BL$X1)),
- lower = c(quantile(na.rm=TRUE,df_sleep_fa_BL$X1, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_fa_BL$X1, prob=0.975))
- )
- summary_sleep_BL <- bind_rows(summary_sleep_CBCL_BL, summary_sleep_cog_BL, summary_sleep_thk_BL, summary_sleep_vol_BL, summary_sleep_fa_BL)
- ###################
- #Summary Scores: Sensitivity: Interaction effects between group and baseline sleep problems to predict follow-up outcomes
- ###################
- df_sleep_CBCL_int <- data.frame(coef.mtrx.sleep_cbcl_int)
- summary_sleep_CBCL_int <- data.frame(
- Outcome = c("Behavior Problems", "Behavior Problems"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_CBCL_int$X1), mean(df_sleep_CBCL_int$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_CBCL_int$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_CBCL_int$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_CBCL_int$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_CBCL_int$X2, prob=0.975))
- )
- df_sleep_cog_int <- data.frame(coef.mtrx.sleep_cog_int)
- summary_sleep_cog_int <- data.frame(
- Outcome = c("Cognition", "Cognition"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_cog_int$X1), mean(df_sleep_cog_int$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_cog_int$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_cog_int$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_cog_int$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_cog_int$X2, prob=0.975))
- )
- df_sleep_thk_int <- data.frame(coef.mtrx.sleep_thk_int)
- summary_sleep_thk_int <- data.frame(
- Outcome = c("Thickness", "Thickness"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_thk_int$X1), mean(df_sleep_thk_int$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_thk_int$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_thk_int$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_thk_int$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_thk_int$X2, prob=0.975))
- )
- df_sleep_vol_int <- data.frame(coef.mtrx.sleep_vol_int)
- summary_sleep_vol_int <- data.frame(
- Outcome = c("Volume", "Volume"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_vol_int$X1), mean(df_sleep_vol_int$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_vol_int$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_vol_int$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_vol_int$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_vol_int$X2, prob=0.975))
- )
- df_sleep_fa_int <- data.frame(coef.mtrx.sleep_fa_int)
- summary_sleep_fa_int <- data.frame(
- Outcome = c("Fractional Anisotropy", "Fractional Anisotropy"),
- Comparison = c("OI", "TDC"),
- mean = c(mean(df_sleep_fa_int$X1), mean(df_sleep_fa_int$X2)),
- lower = c(quantile(na.rm=TRUE,df_sleep_fa_int$X1, prob=0.025), quantile(na.rm=TRUE,df_sleep_fa_int$X2, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_fa_int$X1, prob=0.975), quantile(na.rm=TRUE,df_sleep_fa_int$X2, prob=0.975))
- )
- summary_sleep_int <- bind_rows(summary_sleep_CBCL_int, summary_sleep_cog_int, summary_sleep_thk_int, summary_sleep_vol_int, summary_sleep_fa_int)
- df_sleepoutcomes_ES <- data.frame(coef.mtrx.sleepoutcome.ES)
- summary_sleepoutcomes_ES <- data.frame(
- outcome = c("CBCL", "Cog", "Thk", "Vol", "FA"),
- mean = c(mean(df_sleepoutcomes_ES$X1), mean(df_sleepoutcomes_ES$X2), mean(df_sleepoutcomes_ES$X3), mean(df_sleepoutcomes_ES$X4), mean(df_sleepoutcomes_ES$X5)),
- lower = c(quantile(na.rm=TRUE,df_sleepoutcomes_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X2, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X3, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X4, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X5, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleepoutcomes_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X2, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X3, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X4, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_ES$X5, prob=0.975))
- )
- ###################
- #Summary Scores: associations of follow-up sleep TOTAL SCORE with follow-up outcomes
- ###################
- df_sleep_CBCL_TBI <- data.frame(coef.mtrx.sleep_cbcl_tbi)
- summary_sleep_CBCL_tbi <- data.frame(
- variable = c("Behavior Problems"),
- mean = c(mean(df_sleep_CBCL_TBI$coef.mtrx.sleep_cbcl_tbi)),
- lower = c(quantile(na.rm=TRUE,df_sleep_CBCL_TBI$coef.mtrx.sleep_cbcl_tbi, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_CBCL_TBI$coef.mtrx.sleep_cbcl_tbi, prob=0.975))
- )
- df_sleep_cog_TBI <- data.frame(coef.mtrx.sleep_cog_tbi)
- summary_sleep_cog_tbi <- data.frame(
- variable = c("Cognition"),
- mean = c(mean(df_sleep_cog_TBI$coef.mtrx.sleep_cog_tbi)),
- lower = c(quantile(na.rm=TRUE,df_sleep_cog_TBI$coef.mtrx.sleep_cog_tbi, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_cog_TBI$coef.mtrx.sleep_cog_tbi, prob=0.975))
- )
- df_sleep_thk_TBI <- data.frame(coef.mtrx.sleep_thk_tbi)
- summary_sleep_thk_tbi <- data.frame(
- variable = c("Thickness"),
- mean = c(mean(df_sleep_thk_TBI$coef.mtrx.sleep_thk_tbi)),
- lower = c(quantile(na.rm=TRUE,df_sleep_thk_TBI$coef.mtrx.sleep_thk_tbi, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_thk_TBI$coef.mtrx.sleep_thk_tbi, prob=0.975))
- )
- df_sleep_vol_TBI <- data.frame(coef.mtrx.sleep_vol_tbi)
- summary_sleep_vol_tbi <- data.frame(
- variable = c("Volume"),
- mean = c(mean(df_sleep_vol_TBI$coef.mtrx.sleep_vol_tbi)),
- lower = c(quantile(na.rm=TRUE,df_sleep_vol_TBI$coef.mtrx.sleep_vol_tbi, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_vol_TBI$coef.mtrx.sleep_vol_tbi, prob=0.975))
- )
- df_sleep_fa_TBI <- data.frame(coef.mtrx.sleep_fa_tbi)
- summary_sleep_fa_tbi <- data.frame(
- variable = c("Fractional Anisotropy"),
- mean = c(mean(df_sleep_fa_TBI$coef.mtrx.sleep_fa_tbi)),
- lower = c(quantile(na.rm=TRUE,df_sleep_fa_TBI$coef.mtrx.sleep_fa_tbi, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleep_fa_TBI$coef.mtrx.sleep_fa_tbi, prob=0.975))
- )
- summary_sleep_TBI <- bind_rows(summary_sleep_CBCL_tbi, summary_sleep_cog_tbi, summary_sleep_thk_tbi, summary_sleep_vol_tbi, summary_sleep_fa_tbi)
- df_sleepoutcomes_tbi_ES <- data.frame(coef.mtrx.sleepoutcome.tbi.ES)
- summary_sleepoutcomes_tbi_ES <- data.frame(
- outcome = c("CBCL", "Cog", "Thk", "Vol", "FA"),
- mean = c(mean(df_sleepoutcomes_tbi_ES$X1), mean(df_sleepoutcomes_tbi_ES$X2), mean(df_sleepoutcomes_tbi_ES$X3), mean(df_sleepoutcomes_tbi_ES$X4), mean(df_sleepoutcomes_tbi_ES$X5)),
- lower = c(quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X2, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X3, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X4, prob=0.025), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X5, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X2, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X3, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X4, prob=0.975), quantile(na.rm=TRUE,df_sleepoutcomes_tbi_ES$X5, prob=0.975))
- )
- ###################
- #Summary Scores: Exploratory Analysis: Sleep subscales - Group Differences and Associations with Outcomes
- ###################
- df_subscales <- data.frame(mtrx.subscales)
- summary_subscales <- data.frame(
- variable = c("DIMS", "DIMS", "SBD", "SBD", "DA", "DA", "SWT", "SWT", "DOES", "DOES", "SHY", "SHY"),
- Comparison = c("OI", "TDC", "OI", "TDC","OI", "TDC","OI", "TDC","OI", "TDC","OI", "TDC"),
- mean = c(mean(df_subscales$X1),mean(df_subscales$X2),mean(df_subscales$X3),mean(df_subscales$X4),mean(df_subscales$X5),mean(df_subscales$X6),mean(df_subscales$X7),mean(df_subscales$X8),mean(df_subscales$X9),mean(df_subscales$X10),mean(df_subscales$X11),mean(df_subscales$X12)),
- lower = c(quantile(na.rm=TRUE,df_subscales$X1, prob=0.025), quantile(na.rm=TRUE,df_subscales$X2, prob=0.025),quantile(na.rm=TRUE,df_subscales$X3, prob=0.025),quantile(na.rm=TRUE,df_subscales$X4, prob=0.025),quantile(na.rm=TRUE,df_subscales$X5, prob=0.025),quantile(na.rm=TRUE,df_subscales$X6, prob=0.025),quantile(na.rm=TRUE,df_subscales$X7, prob=0.025),quantile(na.rm=TRUE,df_subscales$X8, prob=0.025),quantile(na.rm=TRUE,df_subscales$X9, prob=0.025),quantile(na.rm=TRUE,df_subscales$X10, prob=0.025),quantile(na.rm=TRUE,df_subscales$X11, prob=0.025),quantile(na.rm=TRUE,df_subscales$X12, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_subscales$X1, prob=0.975), quantile(na.rm=TRUE,df_subscales$X2, prob=0.975), quantile(na.rm=TRUE,df_subscales$X3, prob=0.975), quantile(na.rm=TRUE,df_subscales$X4, prob=0.975), quantile(na.rm=TRUE,df_subscales$X5, prob=0.975), quantile(na.rm=TRUE,df_subscales$X6, prob=0.975), quantile(na.rm=TRUE,df_subscales$X7, prob=0.975), quantile(na.rm=TRUE,df_subscales$X8, prob=0.975), quantile(na.rm=TRUE,df_subscales$X9, prob=0.975), quantile(na.rm=TRUE,df_subscales$X10, prob=0.975), quantile(na.rm=TRUE,df_subscales$X11, prob=0.975), quantile(na.rm=TRUE,df_subscales$X12, prob=0.975)),
- lower.adj = c(quantile(na.rm=TRUE,df_subscales$X1, prob=0.004166), quantile(na.rm=TRUE,df_subscales$X2, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X3, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X4, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X5, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X6, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X7, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X8, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X9, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X10, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X11, prob=0.004166),quantile(na.rm=TRUE,df_subscales$X12, prob=0.004166)),
- upper.adj = c(quantile(na.rm=TRUE,df_subscales$X1, prob=0.995834), quantile(na.rm=TRUE,df_subscales$X2, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X3, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X4, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X5, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X6, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X7, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X8, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X9, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X10, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X11, prob=0.995834),quantile(na.rm=TRUE,df_subscales$X12, prob=0.995834))
- ) #the .adj p-values are based on the 2-sided CIs for 6 subscales: 0.05/6=0.0083333, therefore 0.004166 on two sides
- df_subscales_ES <- data.frame(mtrx.subscales.ES)
- summary_subscales_ES <- data.frame(
- variable = c("DIMS", "DIMS", "SBD", "SBD", "DA", "DA", "SWT", "SWT", "DOES", "DOES", "SHY", "SHY"),
- Comparison = c("OI", "TDC", "OI", "TDC","OI", "TDC","OI", "TDC","OI", "TDC","OI", "TDC"),
- mean = c(mean(df_subscales_ES$X1),mean(df_subscales_ES$X2),mean(df_subscales_ES$X3),mean(df_subscales_ES$X4),mean(df_subscales_ES$X5),mean(df_subscales_ES$X6),mean(df_subscales_ES$X7),mean(df_subscales_ES$X8),mean(df_subscales_ES$X9),mean(df_subscales_ES$X10),mean(df_subscales_ES$X11),mean(df_subscales_ES$X12)),
- lower = c(quantile(na.rm=TRUE,df_subscales_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_subscales_ES$X2, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X3, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X4, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X5, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X6, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X7, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X8, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X9, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X10, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X11, prob=0.025),quantile(na.rm=TRUE,df_subscales_ES$X12, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_subscales_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X2, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X3, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X4, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X5, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X6, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X7, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X8, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X9, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X10, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X11, prob=0.975), quantile(na.rm=TRUE,df_subscales_ES$X12, prob=0.975)),
- lower.adj = c(quantile(na.rm=TRUE,df_subscales_ES$X1, prob=0.004166), quantile(na.rm=TRUE,df_subscales_ES$X2, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X3, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X4, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X5, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X6, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X7, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X8, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X9, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X10, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X11, prob=0.004166),quantile(na.rm=TRUE,df_subscales_ES$X12, prob=0.004166)),
- upper.adj = c(quantile(na.rm=TRUE,df_subscales_ES$X1, prob=0.995834), quantile(na.rm=TRUE,df_subscales_ES$X2, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X3, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X4, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X5, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X6, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X7, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X8, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X9, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X10, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X11, prob=0.995834),quantile(na.rm=TRUE,df_subscales_ES$X12, prob=0.995834))
- )
- df_subscales_tbi <- data.frame(mtrx.subscales.tbi)
- summary_subscales_tbi <- data.frame(
- outcome = c("Thickness", "Thickness","Thickness","Thickness","Thickness","Thickness","Volume","Volume","Volume","Volume","Volume","Volume","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Cognition","Cognition","Cognition","Cognition","Cognition","Cognition","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems"),
- subscale = c("DIMS", "SBD", "DA", "SWT", "DOES", "SHY", "DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY"),
- mean = c(mean(df_subscales_tbi$X1),mean(df_subscales_tbi$X2),mean(df_subscales_tbi$X3),mean(df_subscales_tbi$X4),mean(df_subscales_tbi$X5),mean(df_subscales_tbi$X6),mean(df_subscales_tbi$X7),mean(df_subscales_tbi$X8),mean(df_subscales_tbi$X9),mean(df_subscales_tbi$X10),mean(df_subscales_tbi$X11),mean(df_subscales_tbi$X12),mean(df_subscales_tbi$X13), mean(df_subscales_tbi$X14),mean(df_subscales_tbi$X15),mean(df_subscales_tbi$X16), mean(df_subscales_tbi$X17), mean(df_subscales_tbi$X18),mean(df_subscales_tbi$X19), mean(df_subscales_tbi$X20), mean(df_subscales_tbi$X21), mean(df_subscales_tbi$X22), mean(df_subscales_tbi$X23), mean(df_subscales_tbi$X24), mean(df_subscales_tbi$X25), mean(df_subscales_tbi$X26), mean(df_subscales_tbi$X27), mean(df_subscales_tbi$X28), mean(df_subscales_tbi$X29), mean(df_subscales_tbi$X30)),
- lower = c(quantile(na.rm=TRUE,df_subscales_tbi$X1, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X2, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X3, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X4, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X5, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X6, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X7, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X8, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X9, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X10, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X11, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X12, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X13, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X14, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X15, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X16, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X17, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X18, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X19, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X20, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X21, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X22, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi$X23, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X24, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X25, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X26, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X27, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X28, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X29, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi$X30, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_subscales_tbi$X1, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X2, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X3, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X4, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X5, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X6, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X7, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X8, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X9, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X10, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X11, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X12, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X13, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X14, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X15, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X16, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X17, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X18, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X19, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X20, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X21, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X22, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X23, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X24, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi$X25, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi$X26, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi$X27, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi$X28, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi$X29, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi$X30, prob=0.975)),
- lower.adj = c(quantile(na.rm=TRUE,df_subscales_tbi$X1, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X2, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X3, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X4, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X5, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X6, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X7, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X8, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X9, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X10, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X11, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X12, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X13, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X14, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X15, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X16, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X17, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X18, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X19, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X20, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X21, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X22, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi$X23, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X24, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X25, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X26, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X27, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X28, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X29, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi$X30, prob=0.004166)),
- upper.adj = c(quantile(na.rm=TRUE,df_subscales_tbi$X1, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X2, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X3, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X4, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X5, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X6, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X7, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X8, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X9, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X10, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X11, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X12, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X13, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X14, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X15, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X16, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X17, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X18, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X19, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X20, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X21, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X22, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X23, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi$X24, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X25, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X26, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X27, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X28, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X29, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi$X30, prob=0.995834))
- )
- df_subscales_tbi_ES <- data.frame(mtrx.subscales.tbi.ES)
- summary_subscales_tbi_ES <- data.frame(
- outcome = c("Thickness", "Thickness","Thickness","Thickness","Thickness","Thickness","Volume","Volume","Volume","Volume","Volume","Volume","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Fractional Anisotropy","Cognition","Cognition","Cognition","Cognition","Cognition","Cognition","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems","Behavior Problems"),
- subscale = c("DIMS", "SBD", "DA", "SWT", "DOES", "SHY", "DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY","DIMS", "SBD", "DA", "SWT", "DOES", "SHY"),
- mean = c(mean(df_subscales_tbi_ES$X1),mean(df_subscales_tbi_ES$X2),mean(df_subscales_tbi_ES$X3),mean(df_subscales_tbi_ES$X4),mean(df_subscales_tbi_ES$X5),mean(df_subscales_tbi_ES$X6),mean(df_subscales_tbi_ES$X7),mean(df_subscales_tbi_ES$X8),mean(df_subscales_tbi_ES$X9),mean(df_subscales_tbi_ES$X10),mean(df_subscales_tbi_ES$X11),mean(df_subscales_tbi_ES$X12),mean(df_subscales_tbi_ES$X13), mean(df_subscales_tbi_ES$X14),mean(df_subscales_tbi_ES$X15),mean(df_subscales_tbi_ES$X16), mean(df_subscales_tbi_ES$X17), mean(df_subscales_tbi_ES$X18),mean(df_subscales_tbi_ES$X19), mean(df_subscales_tbi_ES$X20), mean(df_subscales_tbi_ES$X21), mean(df_subscales_tbi_ES$X22), mean(df_subscales_tbi_ES$X23), mean(df_subscales_tbi_ES$X24), mean(df_subscales_tbi_ES$X25), mean(df_subscales_tbi_ES$X26), mean(df_subscales_tbi_ES$X27), mean(df_subscales_tbi_ES$X28), mean(df_subscales_tbi_ES$X29), mean(df_subscales_tbi_ES$X30)),
- lower = c(quantile(na.rm=TRUE,df_subscales_tbi_ES$X1, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X2, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X3, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X4, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X5, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X6, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X7, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X8, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X9, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X10, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X11, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X12, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X13, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X14, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X15, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X16, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X17, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X18, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X19, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X20, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X21, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X22, prob=0.025),quantile(na.rm=TRUE,df_subscales_tbi_ES$X23, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X24, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X25, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X26, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X27, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X28, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X29, prob=0.025), quantile(na.rm=TRUE,df_subscales_tbi_ES$X30, prob=0.025)),
- upper = c(quantile(na.rm=TRUE,df_subscales_tbi_ES$X1, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X2, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X3, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X4, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X5, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X6, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X7, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X8, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X9, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X10, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X11, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X12, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X13, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X14, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X15, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X16, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X17, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X18, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X19, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X20, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X21, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X22, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X23, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X24, prob=0.975), quantile(na.rm=TRUE,df_subscales_tbi_ES$X25, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi_ES$X26, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi_ES$X27, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi_ES$X28, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi_ES$X29, prob=0.975),quantile(na.rm=TRUE,df_subscales_tbi_ES$X30, prob=0.975)),
- lower.adj = c(quantile(na.rm=TRUE,df_subscales_tbi_ES$X1, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X2, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X3, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X4, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X5, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X6, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X7, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X8, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X9, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X10, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X11, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X12, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X13, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X14, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X15, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X16, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X17, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X18, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X19, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X20, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X21, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X22, prob=0.004166),quantile(na.rm=TRUE,df_subscales_tbi_ES$X23, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X24, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X25, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X26, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X27, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X28, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X29, prob=0.004166), quantile(na.rm=TRUE,df_subscales_tbi_ES$X30, prob=0.004166)),
- upper.adj = c(quantile(na.rm=TRUE,df_subscales_tbi_ES$X1, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X2, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X3, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X4, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X5, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X6, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X7, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X8, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X9, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X10, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X11, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X12, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X13, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X14, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X15, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X16, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X17, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X18, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X19, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X20, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X21, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X22, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X23, prob=0.995834), quantile(na.rm=TRUE,df_subscales_tbi_ES$X24, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X25, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X26, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X27, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X28, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X29, prob=0.995834),quantile(na.rm=TRUE,df_subscales_tbi_ES$X30, prob=0.995834))
- )
- ###################
- #Summary Scores: Linear Mixed-Effects Models: Differences in Outcomes between clinical sleep-trajectories
- ###################
- df_lmer_beh <- data.frame(mtrx.lmer.cbcl)
- summary_lmer_beh <- data.frame(
- contrast = c("2y Chronic-Improving", "2y Chronic-New Onset","2y Chronic-Normal", "2y Improving-New Onset", "2y Improving-Normal", "2y New Onset-Normal", "BL Chronic-Improving", "BL Chronic-New Onset","BL Chronic-Normal", "BL Improving-New Onset", "BL Improving-Normal", "BL New Onset-Normal", "Chronic 2y-BL", "Improving 2y-BL", "New Onset 2y-BL", "Normal 2y-BL"),
- mean = c(mean(df_lmer_beh$X1),mean(df_lmer_beh$X2),mean(df_lmer_beh$X3),mean(df_lmer_beh$X4),mean(df_lmer_beh$X5),mean(df_lmer_beh$X6),mean(df_lmer_beh$X7),mean(df_lmer_beh$X8),mean(df_lmer_beh$X9),mean(df_lmer_beh$X10),mean(df_lmer_beh$X11),mean(df_lmer_beh$X12),mean(df_lmer_beh$X13),mean(df_lmer_beh$X14),mean(df_lmer_beh$X15),mean(df_lmer_beh$X16)),
- lower = c(quantile(na.rm=TRUE,df_lmer_beh$X1, prob=0.00208), quantile(na.rm=TRUE,df_lmer_beh$X2, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X3, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X4, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X5, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X6, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X7, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X8, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X9, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X10, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X11, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X12, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh$X13, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh$X14, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh$X15, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh$X16, prob=0.00625)),
- upper = c(quantile(na.rm=TRUE,df_lmer_beh$X1, prob=0.9979), quantile(na.rm=TRUE,df_lmer_beh$X2, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X3, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X4, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X5, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X6, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X7, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X8, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X9, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X10, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X11, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X12, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh$X13, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh$X14, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh$X15, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh$X16, prob=0.99375))
- )
- df_lmer_beh_TDC <- data.frame(mtrx.lmer.cbcl.tdc)
- summary_lmer_beh_TDC <- data.frame(
- contrast = c("2y Chronic-Improving", "2y Chronic-New Onset","2y Chronic-Normal", "2y Improving-New Onset", "2y Improving-Normal", "2y New Onset-Normal", "BL Chronic-Improving", "BL Chronic-New Onset","BL Chronic-Normal", "BL Improving-New Onset", "BL Improving-Normal", "BL New Onset-Normal", "Chronic 2y-BL", "Improving 2y-BL", "New Onset 2y-BL", "Normal 2y-BL"),
- mean = c(mean(df_lmer_beh_TDC$X1),mean(df_lmer_beh_TDC$X2),mean(df_lmer_beh_TDC$X3),mean(df_lmer_beh_TDC$X4),mean(df_lmer_beh_TDC$X5),mean(df_lmer_beh_TDC$X6),mean(df_lmer_beh_TDC$X7),mean(df_lmer_beh_TDC$X8),mean(df_lmer_beh_TDC$X9),mean(df_lmer_beh_TDC$X10),mean(df_lmer_beh_TDC$X11),mean(df_lmer_beh_TDC$X12),mean(df_lmer_beh_TDC$X13),mean(df_lmer_beh_TDC$X14),mean(df_lmer_beh_TDC$X15),mean(df_lmer_beh_TDC$X16)),
- lower = c(quantile(na.rm=TRUE,df_lmer_beh_TDC$X1, prob=0.00208), quantile(na.rm=TRUE,df_lmer_beh_TDC$X2, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X3, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X4, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X5, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X6, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X7, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X8, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X9, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X10, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X11, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X12, prob=0.00208),quantile(na.rm=TRUE,df_lmer_beh_TDC$X13, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh_TDC$X14, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh_TDC$X15, prob=0.00625),quantile(na.rm=TRUE,df_lmer_beh_TDC$X16, prob=0.00625)),
- upper = c(quantile(na.rm=TRUE,df_lmer_beh_TDC$X1, prob=0.9979), quantile(na.rm=TRUE,df_lmer_beh_TDC$X2, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X3, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X4, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X5, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X6, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X7, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X8, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X9, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X10, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X11, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X12, prob=0.9979),quantile(na.rm=TRUE,df_lmer_beh_TDC$X13, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh_TDC$X14, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh_TDC$X15, prob=0.99375),quantile(na.rm=TRUE,df_lmer_beh_TDC$X16, prob=0.99375))
- )
- df_lmer_thk <- data.frame(mtrx.lmer.thk)
- summary_lmer_thk <- data.frame(
- contrast = c("2y Chronic-Improving", "2y Chronic-New Onset","2y Chronic-Normal", "2y Improving-New Onset", "2y Improving-Normal", "2y New Onset-Normal", "BL Chronic-Improving", "BL Chronic-New Onset","BL Chronic-Normal", "BL Improving-New Onset", "BL Improving-Normal", "BL New Onset-Normal", "Chronic 2y-BL", "Improving 2y-BL", "New Onset 2y-BL", "Normal 2y-BL"),
- mean = c(mean(df_lmer_thk$X1),mean(df_lmer_thk$X2),mean(df_lmer_thk$X3),mean(df_lmer_thk$X4),mean(df_lmer_thk$X5),mean(df_lmer_thk$X6),mean(df_lmer_thk$X7),mean(df_lmer_thk$X8),mean(df_lmer_thk$X9),mean(df_lmer_thk$X10),mean(df_lmer_thk$X11),mean(df_lmer_thk$X12),mean(df_lmer_thk$X13),mean(df_lmer_thk$X14),mean(df_lmer_thk$X15), mean(df_lmer_thk$X16)),
- lower = c(quantile(na.rm=TRUE,df_lmer_thk$X1, prob=0.00208), quantile(na.rm=TRUE,df_lmer_thk$X2, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X3, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X4, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X5, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X6, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X7, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X8, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X9, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X10, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X11, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X12, prob=0.00208),quantile(na.rm=TRUE,df_lmer_thk$X13, prob=0.00625),quantile(na.rm=TRUE,df_lmer_thk$X14, prob=0.00625),quantile(na.rm=TRUE,df_lmer_thk$X15, prob=0.00625),quantile(na.rm=TRUE,df_lmer_thk$X16, prob=0.00625)),
- upper = c(quantile(na.rm=TRUE,df_lmer_thk$X1, prob=0.9979), quantile(na.rm=TRUE,df_lmer_thk$X2, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X3, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X4, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X5, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X6, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X7, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X8, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X9, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X10, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X11, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X12, prob=0.9979),quantile(na.rm=TRUE,df_lmer_thk$X13, prob=0.99375),quantile(na.rm=TRUE,df_lmer_thk$X14, prob=0.99375),quantile(na.rm=TRUE,df_lmer_thk$X15, prob=0.99375),quantile(na.rm=TRUE,df_lmer_thk$X16, prob=0.99375))
- )
- df_lmer_vol <- data.frame(mtrx.lmer.vol)
- summary_lmer_vol <- data.frame(
- contrast = c("2y Chronic-Improving", "2y Chronic-New Onset","2y Chronic-Normal", "2y Improving-New Onset", "2y Improving-Normal", "2y New Onset-Normal", "BL Chronic-Improving", "BL Chronic-New Onset","BL Chronic-Normal", "BL Improving-New Onset", "BL Improving-Normal", "BL New Onset-Normal", "Chronic 2y-BL", "Improving 2y-BL", "New Onset 2y-BL", "Normal 2y-BL"),
- mean = c(mean(df_lmer_vol$X1),mean(df_lmer_vol$X2),mean(df_lmer_vol$X3),mean(df_lmer_vol$X4),mean(df_lmer_vol$X5),mean(df_lmer_vol$X6),mean(df_lmer_vol$X7),mean(df_lmer_vol$X8),mean(df_lmer_vol$X9),mean(df_lmer_vol$X10),mean(df_lmer_vol$X11),mean(df_lmer_vol$X12),mean(df_lmer_vol$X13),mean(df_lmer_vol$X14),mean(df_lmer_vol$X15),mean(df_lmer_vol$X16)),
- lower = c(quantile(na.rm=TRUE,df_lmer_vol$X1, prob=0.00208), quantile(na.rm=TRUE,df_lmer_vol$X2, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X3, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X4, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X5, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X6, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X7, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X8, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X9, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X10, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X11, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X12, prob=0.00208),quantile(na.rm=TRUE,df_lmer_vol$X13, prob=0.00625),quantile(na.rm=TRUE,df_lmer_vol$X14, prob=0.00625),quantile(na.rm=TRUE,df_lmer_vol$X15, prob=0.00625),quantile(na.rm=TRUE,df_lmer_vol$X16, prob=0.00625)),
- upper = c(quantile(na.rm=TRUE,df_lmer_vol$X1, prob=0.9979), quantile(na.rm=TRUE,df_lmer_vol$X2, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X3, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X4, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X5, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X6, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X7, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X8, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X9, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X10, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X11, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X12, prob=0.9979),quantile(na.rm=TRUE,df_lmer_vol$X13, prob=0.99375),quantile(na.rm=TRUE,df_lmer_vol$X14, prob=0.99375),quantile(na.rm=TRUE,df_lmer_vol$X15, prob=0.99375),quantile(na.rm=TRUE,df_lmer_vol$X16, prob=0.99375))
- ) #0.05/12/2 = 0.00208 resp. 0.9979 for the trajectories and 0.05/4/2 = 0.00625 resp. 0.99375 for the single trajectory comparisons
Sleep_Paper_3_Summary_Scores.R, under MIT · at the source
Overview
Authors: Anja K. Betz1,2,3,4, Hanneke S. R. MacLaren1,2,4, Alberto G. Villagran Asiares1,4, Luisa S. Schuhmacher1, Inga K. Koerte1,5,2,3,4
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
- German Center for Child and Adolescent Health (DZKJ), Partner site Munich, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
Institutions: Ludwig-Maximilians-Universität München (Germany); Harvard University (United States); Mass General Brigham (United States)
Journal: JAMA network open, volume 9, issue 3, article e260229
Dates: received 7 August 2025; accepted 22 December 2025; published online 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1001/jamanetworkopen.2026.0229 · PMID 41805957 · PMCID PMC12976784 · OpenAlex W7134971046
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), traumatic brain injury (population), sleep disorders (population)
Methods: Statistics, Preprocessing
MeSH: Brain*, Brain Concussion*, Cognition*, Sleep Wake Disorders*, Child, Cohort Studies, Female, Humans, Magnetic Resonance Imaging, Male, Prevalence (* major topic)
Journal subjects: Pediatrics
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (804326)
Citations: cited by 1 paper (Europe PMC); 45 references in the paper
Abstract
Importance: Sleep disturbances are increasingly recognized as a modifiable factor associated with poor outcomes after mild traumatic brain injury (mTBI) in adults, yet their prevalence, mechanisms, and clinical impact in children remain largely unknown. Identifying whether sleep problems contribute to behavioral, cognitive, and neurobiological outcomes after pediatric mTBI could inform targeted interventions.
Objective: To determine the prevalence of sleep disturbances after pediatric mTBI and to assess associations with behavioral symptoms, cognition, and brain structure.
Design, Setting, and Participants: This multisite, population-based cohort study is a secondary analysis of data collected between September 2016 and January 2020 as part of the Adolescent Brain Cognitive Development Study 5.0 release. Participants were children who sustained an mTBI between baseline (age 9-10 years) and 2-year follow-up (age 11-12 years). Children with mTBI were propensity score–matched on the basis of age, sex, study site, self-reported race, and total family income to typically developing children (TDC) and orthopedic injury (OI) controls. Data were analyzed September 2024 to June 2025.
Exposure: mTBI between baseline and follow-up.
Main Outcomes and Measures: The primary outcomes were parent-reported sleep disturbances (categorized as new onset, chronic, improving, or none), behavioral problems, and cognitive performance. Magnetic resonance imaging was used to examine cortical thickness, cortical volume, and white matter microstructure. Analyses used linear regressions and linear mixed models, controlling for baseline when appropriate.
Results: Of 573 children, there were 191 children with mTBI (mean [SD] age, 12.03 [0.6] years; 112 [58.6%] male). Children with mTBI were more likely than controls to develop new clinical sleep disturbances (29 children with mTBI [15.2%] vs 22 of 191 children [11.5%] in the TDC group and 19 of 191 children [9.9%] in the OI group) and had higher rates of chronic sleep disturbances (41 children [21.5%] with mTBI vs 25 children [13.1%] in the TDC group and 25 children [13.1%] in the OI group). Total sleep disturbance scores were significantly elevated for children with mTBI compared with the TDC group (β, −0.27; 95% CI, −0.45 to −0.10), but not the OI group (β, −0.12; 95% CI, −0.29 to 0.05). Higher behavioral symptoms were found compared with the TDC group (β, −0.30; 95% CI, −0.45 to −0.16) and were associated with sleep problems, especially newly emerging disturbances, after mTBI. Children in the OI group exhibited greater cortical thickness (β, 0.18; 95% CI, 0.06 to 0.30) and volume (β, 0.06; 95% CI, 0.01 to 0.11) than children with mTBI, with both being positively associated with sleep disturbances in children with mTBI. No group differences were found in cognition or white matter microstructure or associations with sleep.
Conclusions and Relevance: In this cohort study, sleep disturbances were more common among children with mTBI than among TDC. In particular, newly developing sleep problems appeared to be a potential key pathway to behavioral problems and a promising interventional target. Cognitive and structural brain outcomes were not significantly associated with sleep disturbances in this cohort.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repositories
Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.
Zenodo 18301364
License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (4)
Size: 6 files, 4 scripts
Software Heritage: not checked
Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), car (1 file), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), multcomp (1 file), neuroCombat (1 file), psych (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
- 30 September 2026: the link answers (HTTP 200)
6 files
- Sensitivity/
Sleep_Paper_Sensitivity_ , R, 489 linesPsychiatricVulnerability .R - Sensitivity/
Sleep_Paper_Sensitivity_ , R, 450 linesSexStratified.R - Sleep_Paper_1_SampleMeas
ures.R , R, 453 lines, 1 match - Sleep_Paper_2_Bootstrapp
ing_Analysis.R , R, 566 lines, 1 match - Sleep_Paper_3_Summary_Sc
ores.R , R, 445 lines, 1 match - Sleep_Paper_4_Visualizat
ion.R , R, 515 lines
At the source: doi.org/10.5281/zenodo.18301364
Zenodo 18301365
License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (4)
Size: 6 files, 4 scripts
Software Heritage: not checked
Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), car (1 file), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), multcomp (1 file), neuroCombat (1 file), psych (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
- 30 September 2026: the link answers (HTTP 200)
6 files
- Sensitivity/
Sleep_Paper_Sensitivity_ , R, 489 linesPsychiatricVulnerability .R - Sensitivity/
Sleep_Paper_Sensitivity_ , R, 450 linesSexStratified.R - Sleep_Paper_1_SampleMeas
ures.R , R, 453 lines - Sleep_Paper_2_Bootstrapp
ing_Analysis.R , R, 566 lines - Sleep_Paper_3_Summary_Sc
ores.R , R, 445 lines - Sleep_Paper_4_Visualizat
ion.R , R, 515 lines
At the source: doi.org/10.5281/zenodo.18301365
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 11 MeSH terms, 1 funder, 42 references.
Cite
This paper
Betz, A. K., MacLaren, H. S. R., Villagran Asiares, A. G., Schuhmacher, L. S., & Koerte, I. K. (2026). Sleep Disturbances and Cognition, Behavior, and Brain Structure in Children With mTBI. JAMA network open, 9(3), e260229. https://
BibTeX
@article{betz2026sleep,
author = {Betz, Anja K. and MacLaren, Hanneke S. R. and Villagran Asiares, Alberto G. and Schuhmacher, Luisa S. and Koerte, Inga K.},
title = {{Sleep Disturbances and Cognition, Behavior, and Brain Structure in Children With mTBI}},
journal = {JAMA network open},
year = {2026},
month = mar,
volume = {9},
number = {3},
pages = {e260229},
publisher = {American Medical Association},
issn = {2574-3805},
doi = {10.1001/
url = {https://
pmid = {41805957},
pmcid = {PMC12976784}
}
RIS
TY - JOUR
AU - Betz, Anja K.
AU - MacLaren, Hanneke S. R.
AU - Villagran Asiares, Alberto G.
AU - Schuhmacher, Luisa S.
AU - Koerte, Inga K.
TI - Sleep Disturbances and Cognition, Behavior, and Brain Structure in Children With mTBI
T2 - JAMA network open
J2 - JAMA Netw Open
PY - 2026
DA - 2026/
VL - 9
IS - 3
SP - e260229
SN - 2574-3805
PB - American Medical Association
DO - 10.1001/
UR - https://
LA - en
ER -
CSL-JSON
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