Cost-effectiveness of esketamine versus alternative treatment strategies for treatment-resistant depression in Hong Kong: A multi-armed modeling study.

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The authors' code

R · 211 lines · 6 KB · no license

#Augmentation
install.packages("heemod")
library(heemod)
pacman::p_load(data.table, dplyr)
# define parameters
par_mod <- define_parameters(
c_3rdOP = 1165,
c_4thOP = 1341,
c_5thOP = 1686,
c_3rdIP = 405,
c_4thIP = 405,
c_5thIP = 572,
c_SOPC = 1190,
c_AUG = 109,
u_remission = 0.85,
u_response = 0.72,
u_nresponse = 0.58,
u_tx = 0.58,
p_3rdTX2RES = 0.186,
p_3rdTX2REM = 0.198,
p_3rdRES2REL = 0.281,
p_3rdREM2REL = 0.125,
p_4thTX2RES = 0.109,
p_4thTX2REM = 0.1,
p_4thRES2REL = 0.359,
p_4thREM2REL = 0.126,
p_5thTX2RES = 0.081,
p_5thTX2REM = 0.073,
p_5thRES2REL = 0.38,
p_5thREM2REL = 0.228,
rr_AUG_remission = 1.28,
rr_AUG_response = 1.27,
rr_AUG_rem2rel = 1,
rr_AUG_res2rel = 1,
d_rate = 0.03,
p_death = case_when(
model_time <= 13 ~ 0.001772008, # cycle 1-13
model_time >= 14 & model_time <= 26 ~ 0.000921875, # cycle 14-26
model_time >= 27 & model_time <= 39 ~ 0.000706614, # cycle 27-39
model_time >= 40 & model_time <= 52 ~ 0.000590032, # cycle 40-52
model_time >= 53 ~ 0.000513965 # cycle after 53
)
)
discount_cost <- function(value, rate, cycle) {
year <- case_when(
cycle <= 13 ~ 1, # y1
cycle >= 14 & cycle <= 26 ~ 2, # y2
cycle >= 27 & cycle <= 39 ~ 3, # y3
cycle >= 40 & cycle <= 52 ~ 4, # y4
cycle >= 53 & cycle <= 65 ~ 5 # y5
)
# discount
ifelse(year == 1,
value, # did not discount at Y1
value / (1 + rate)^(year - 1) # other years
)
}
discount_qaly <- function(value, rate, cycle) {
year <- case_when(
cycle <= 13 ~ 1, # y1
cycle >= 14 & cycle <= 26 ~ 2, # y2
cycle >= 27 & cycle <= 39 ~ 3, # y3
cycle >= 40 & cycle <= 52 ~ 4, # y4
cycle >= 53 & cycle <= 65 ~ 5 # y5
)
# discount
ifelse(year == 1,
value/13, # did not discount at Y1
value/ 13 / (1 + rate)^(year - 1) # other years
)
}
# define states
mat_antidepressant <- define_transition(
state_names = c("3rd_TX", "3rd_Response", "3rd_Remission", "3rd_Nresponse",
"4th_TX", "4th_Response", "4th_Remission", "4th_Nresponse",
"5th_TX", "5th_Response", "5th_Remission", "5th_Nresponse",
"Death"),
# from 3rd_TX
0, p_3rdTX2RES*rr_AUG_response, p_3rdTX2REM*rr_AUG_remission, (1 - p_3rdTX2RES*rr_AUG_response - p_3rdTX2REM*rr_AUG_remission - p_death), 0, 0, 0, 0, 0, 0, 0, 0, p_death,
# from 3rd_Response
0, (1 - p_3rdRES2REL*rr_AUG_res2rel - p_death), 0, 0, p_3rdRES2REL*rr_AUG_res2rel, 0, 0, 0, 0, 0, 0, 0, p_death,
# from 3rd_Remission
0, 0, (1 - p_3rdREM2REL*rr_AUG_rem2rel - p_death), 0, p_3rdREM2REL*rr_AUG_rem2rel, 0, 0, 0, 0, 0, 0, 0, p_death,
# from 3rd_Nresponse
0, 0, 0, 0, (1 - p_death), 0, 0, 0, 0, 0, 0, 0, p_death,
# from 4th_TX
0, 0, 0, 0, 0, p_4thTX2RES, p_4thTX2REM, (1 - p_4thTX2RES - p_4thTX2REM - p_death), 0, 0, 0, 0, p_death,
# from 4th_Response
0, 0, 0, 0, 0, (1 - p_4thRES2REL - p_death), 0, 0, p_4thRES2REL, 0, 0, 0, p_death,
# from 4th_Remission
0, 0, 0, 0, 0, 0, (1 - p_4thREM2REL - p_death), 0, p_4thREM2REL, 0, 0, 0, p_death,
# from 4th_Nresponse
0, 0, 0, 0, 0, 0, 0, 0, (1 - p_death), 0, 0, 0, p_death,
# from 5th_TX
0, 0, 0, 0, 0, 0, 0, 0, 0, p_5thTX2RES, p_5thTX2REM, (1 - p_5thTX2RES - p_5thTX2REM - p_death), p_death,
# from 5th_Response
0, 0, 0, 0, 0, 0, 0, 0, p_5thRES2REL, (1 - p_5thRES2REL - p_death), 0, 0, p_death,
# from 5th_Remission
0, 0, 0, 0, 0, 0, 0, 0, p_5thREM2REL, 0, (1 - p_5thREM2REL - p_death), 0, p_death,
# from 5th_Nresponse
0, 0, 0, 0, 0, 0, 0, 0, (1 - p_death), 0, 0, 0, p_death,
# from Death
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)
# define strategy
mod_antidepressant <- define_strategy(
transition = mat_antidepressant,
"3rd_TX" = define_state(
cost = discount_cost(c_SOPC + c_3rdOP + c_3rdIP + c_AUG, d_rate, model_time),
QALY = discount_qaly(u_tx, d_rate, model_time)
),
"4th_TX" = define_state(
cost = discount_cost(c_4thOP + c_4thIP, d_rate, model_time),
QALY = discount_qaly(u_tx, d_rate, model_time)
),
"5th_TX" = define_state(
cost = discount_cost(c_5thOP + c_5thIP, d_rate, model_time),
QALY = discount_qaly(u_tx, d_rate, model_time)
),
"3rd_Response" = define_state(
cost = discount_cost(c_SOPC + c_3rdOP + c_AUG, d_rate, model_time),
QALY = discount_qaly(u_response, d_rate, model_time)
),
"4th_Response" = define_state(
cost = discount_cost(c_4thOP, d_rate, model_time),
QALY = discount_qaly(u_response, d_rate, model_time)
),
"5th_Response" = define_state(
cost = discount_cost(c_5thOP, d_rate, model_time),
QALY = discount_qaly(u_response, d_rate, model_time)
),
"3rd_Remission" = define_state(
cost = discount_cost(c_SOPC + c_3rdOP + c_AUG, d_rate, model_time),
QALY = discount_qaly(u_remission, d_rate, model_time)
),
"4th_Remission" = define_state(
cost = discount_cost(c_4thOP, d_rate, model_time),
QALY = discount_qaly(u_remission, d_rate, model_time)
),
"5th_Remission" = define_state(
cost = discount_cost(c_5thOP, d_rate, model_time),
QALY = discount_qaly(u_remission, d_rate, model_time)
),
"3rd_Nresponse" = define_state(
cost = discount_cost(c_3rdOP + c_3rdIP, d_rate, model_time),
QALY = discount_qaly(u_nresponse, d_rate, model_time)
),
"4th_Nresponse" = define_state(
cost = discount_cost(c_4thOP + c_4thIP, d_rate, model_time),
QALY = discount_qaly(u_nresponse, d_rate, model_time)
),
"5th_Nresponse" = define_state(
cost = discount_cost(c_5thOP + c_5thIP, d_rate, model_time),
QALY = discount_qaly(u_nresponse, d_rate, model_time)
),
"Death" = define_state(
cost = 0,
QALY = 0
)
)
# run model
result <- run_model(
mod = mod_antidepressant,
parameters = par_mod,
cycles = 65,
cost = cost,
effect = QALY,
method = "beginning",
init = c(1000, rep(0, 12)) # 1000 people
)
state_counts <- get_counts(result)
print(state_counts, n=1000)
cost_by_cycle <- get_values(result)
print(cost_by_cycle)
print(cost_by_cycle)
print(qaly_by_cycle)
print(result$run_model$cost)
print(result$run_model$QALY)

AUG.R at commit b2ba672, no license · at the source

Overview

Authors: Yifan Li^1,2, Vivien Kin Yi Chan², Mark Jit^3,4,5, Franco Wing Tak Cheng^2,6, Hei Hang Edmund Yiu², David Makram Bishai³, Dawn Craig⁷, Esther Wai Yin Chan², Sandra Sau Man Chan⁸, Xue Li^1,2,9

ORCID iDs: Yifan Li, Vivien Kin Yi Chan, Mark Jit, Franco Wing Tak Cheng, Hei Hang Edmund Yiu, Dawn Craig, Esther Wai Yin Chan, Xue Li

Institutions: University of Hong Kong (Hong Kong SAR China); London School of Hygiene & Tropical Medicine (United Kingdom); New York University (United States); University of Hong Kong - Shenzhen Hospital (China); Newcastle University (United Kingdom); Chinese University of Hong Kong (Hong Kong SAR China)

Journal: PLoS medicine, volume 23, issue 4, article e1005047

Dates: received 9 October 2025; accepted 27 March 2026; published online 16 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pmed.1005047 · PMID 41990095 · PMCID PMC13120699 · OpenAlex W7154572395

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: other (modality), human (organism), depression (population), clinical / translational (subfield)

Methods: Statistics

MeSH: Antidepressive Agents*, Depressive Disorder, Treatment-Resistant*, Ketamine*, Adult, Combined Modality Therapy, Cost-Benefit Analysis, Cost-Effectiveness Analysis, Electroconvulsive Therapy, Hong Kong, Humans, Markov Chains, Psychotherapy, Quality-Adjusted Life Years, Transcranial Magnetic Stimulation (* major topic)

Topic: Treatment of Major Depression (Pharmacology, Medicine), according to OpenAlex

Funding: University Grants Committee (R7007-22)

Citations: cited by 1 paper (Europe PMC); 54 references in the paper

Abstract

Background: Treatment-resistant depression (TRD), defined as failure to respond to at least two adequately administered antidepressant (AD) regimens, imposes major clinical and economic burdens. Esketamine nasal spray offers rapid antidepressant clinical effects, yet previous evaluations compared it only with unrealistic comparators such as AD monotherapy. This study assessed the cost-effectiveness of esketamine versus multiple alternative third-line strategies for TRD from the Hong Kong healthcare payer’s perspective.

Methods and findings: A Markov cohort model simulated adults with TRD in Hong Kong over 5 years with 4-week cycles. The model compared esketamine plus AD with six alternative third-line treatment strategies: combination therapy (AD plus AD), augmentation therapy (AD plus antipsychotic or lithium), psychotherapy alone, psychotherapy plus AD, repetitive transcranial magnetic stimulation (rTMS) plus AD, and electroconvulsive therapy (ECT) plus AD. Primary outcomes were quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) under a US$50,000/QALY willingness-to-pay (WTP) threshold. Deterministic and probabilistic sensitivity analyses and scenario analyses were conducted, focusing on alternative esketamine dosing, delivery strategies, and comparisons with other treatment options to assess the robustness of the results. In base-case analysis, esketamine was not cost-effective versus augmentation, combination, psychotherapy, or psychotherapy plus AD with ICERs ranging from US$134,127 to US$312,750 per QALY but was more cost-effective than rTMS (dominated) and ECT (ICER: US$322,407/QALY). Combination therapy was the most cost-effective among all strategies evaluated. The main limitation of this study is the reliance on indirect comparisons and assumptions derived from heterogeneous clinical trial populations, which may not fully reflect real-world patient characteristics and treatment pathways.

Conclusions: Esketamine appeared more cost-effective than rTMS and ECT, but not cost-effective compared with other commonly used third-line treatment strategies for TRD. These findings suggest that cost-effectiveness evidence may help inform more context-sensitive treatment sequencing strategies beyond conventional line-of-therapy frameworks. Policy approaches such as price negotiation, optimized service delivery, and alternative dosing strategies may improve the value of esketamine for TRD management.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above.

scan2030/scan2030-WP-3_TRD_ESK_CEA

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: b2ba6724951638f2459efcd96a502f9f46926811, 11 April 2026

Languages: R (28)

Size: 46 files, 28 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README

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

Tools: data.table (27 files), tidyverse (27 files), ggplot2 (8 files)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

29 files

Basecase/AUG.R, R, 211 lines
Basecase/CEA_Plane.R, R, 151 lines
Basecase/COM.R, R, 211 lines
Basecase/ECT.R, R, 225 lines
Basecase/ESK_Basecase.R, R, 234 lines
Basecase/TMS.R, R, 223 lines
Basecase/cPSY.R, R, 221 lines
Basecase/mPSY.R, R, 225 lines
Scenario Analysis/AUG_20yrs.R, R, 276 lines
Scenario Analysis/COM_20yrs.R, R, 255 lines
Scenario Analysis/ECT_20yrs.R, R, 266 lines
Scenario Analysis/ESK_S1.R, R, 246 lines
Scenario Analysis/ESK_S2.R, R, 239 lines
Scenario Analysis/ESK_S3.R, R, 239 lines
Scenario Analysis/ESK_S4.R, R, 235 lines
Scenario Analysis/ESK_S5.R, R, 230 lines
Scenario Analysis/ESK_S6.R, R, 232 lines
Scenario Analysis/ESK_S7.R, R, 276 lines
Scenario Analysis/TMS_20yrs.R, R, 268 lines
Scenario Analysis/cPSY_20yrs.R, R, 266 lines
Scenario Analysis/mPSY_20yrs.R, R, 265 lines
Sensitivity Analysis/PSA_final.R, R, 1,391 lines
Sensitivity Analysis/SA_AUG.R, R, 521 lines
Sensitivity Analysis/SA_COM.R, R, 524 lines
Sensitivity Analysis/SA_ECT.R, R, 544 lines
Sensitivity Analysis/SA_TMS.R, R, 560 lines
Sensitivity Analysis/SA_cPSY.R, R, 543 lines
Sensitivity Analysis/SA_mPSY.R, R, 540 lines
README.md, Text, 81 lines

At the source: github.com/scan2030/scan2030-WP-3_TRD_ESK_CEA

Zenodo 19520620

License: CC-BY-4.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data Availability”

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

Tools: data.table (27 files), tidyverse (27 files), ggplot2 (8 files)

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

29 files

Basecase/AUG.R, R, 211 lines
Basecase/CEA_Plane.R, R, 151 lines
Basecase/COM.R, R, 211 lines
Basecase/ECT.R, R, 225 lines
Basecase/ESK_Basecase.R, R, 234 lines
Basecase/TMS.R, R, 223 lines
Basecase/cPSY.R, R, 221 lines
Basecase/mPSY.R, R, 225 lines
Scenario Analysis/AUG_20yrs.R, R, 276 lines
Scenario Analysis/COM_20yrs.R, R, 255 lines
Scenario Analysis/ECT_20yrs.R, R, 266 lines
Scenario Analysis/ESK_S1.R, R, 246 lines
Scenario Analysis/ESK_S2.R, R, 239 lines
Scenario Analysis/ESK_S3.R, R, 239 lines
Scenario Analysis/ESK_S4.R, R, 235 lines
Scenario Analysis/ESK_S5.R, R, 230 lines
Scenario Analysis/ESK_S6.R, R, 232 lines
Scenario Analysis/ESK_S7.R, R, 276 lines
Scenario Analysis/TMS_20yrs.R, R, 268 lines
Scenario Analysis/cPSY_20yrs.R, R, 266 lines
Scenario Analysis/mPSY_20yrs.R, R, 265 lines
Sensitivity Analysis/PSA_final.R, R, 1,391 lines
Sensitivity Analysis/SA_AUG.R, R, 521 lines
Sensitivity Analysis/SA_COM.R, R, 524 lines
Sensitivity Analysis/SA_ECT.R, R, 544 lines
Sensitivity Analysis/SA_TMS.R, R, 560 lines
Sensitivity Analysis/SA_cPSY.R, R, 543 lines
Sensitivity Analysis/SA_mPSY.R, R, 540 lines
README.md, Text, 81 lines

At the source: doi.org/10.5281/zenodo.19520620

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
56 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The data used in this study cannot be shared publicly because the data custodian, the Hong Kong Hospital Authority (HA), which manages the Clinical Data Analysis and Reporting System (CDARS), has not granted permission for public release. Researchers who meet the criteria for access to confidential data may apply for access to CDARS data for research purposes through the Hospital Authority Data Sharing Portal (https://www3.ha.org.hk/data). The study protocol, detailed cost and efficacy parameters, and programming code used in this study are publicly available on GitHub (https://github.com/scan2030/scan2030-WP-3_TRD_ESK_CEA) and archived on Zenodo (https://doi.org/10.5281/zenodo.19520620).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 14 MeSH terms, 1 funder, 39 references.

Cite

This paper

Li, Y., Chan, V. K. Y., Jit, M., Cheng, F. W. T., Yiu, H. H. E., Bishai, D. M., Craig, D., Chan, E. W. Y., Chan, S. S. M., & Li, X. (2026). Cost-effectiveness of esketamine versus alternative treatment strategies for treatment-resistant depression in Hong Kong: A multi-armed modeling study. PLoS medicine, 23(4), e1005047. https://doi.org/10.1371/journal.pmed.1005047

BibTeX

@article{li2026cost, author = {Li, Yifan and Chan, Vivien Kin Yi and Jit, Mark and Cheng, Franco Wing Tak and Yiu, Hei Hang Edmund and Bishai, David Makram and Craig, Dawn and Chan, Esther Wai Yin and Chan, Sandra Sau Man and Li, Xue}, title = {{Cost-effectiveness of esketamine versus alternative treatment strategies for treatment-resistant depression in Hong Kong: A multi-armed modeling study}}, journal = {PLoS medicine}, year = {2026}, month = apr, volume = {23}, number = {4}, pages = {e1005047}, publisher = {PLOS}, issn = {1549-1277}, doi = {10.1371/journal.pmed.1005047}, url = {https://doi.org/10.1371/journal.pmed.1005047}, pmid = {41990095}, pmcid = {PMC13120699} }

RIS

TY - JOUR AU - Li, Yifan AU - Chan, Vivien Kin Yi AU - Jit, Mark AU - Cheng, Franco Wing Tak AU - Yiu, Hei Hang Edmund AU - Bishai, David Makram AU - Craig, Dawn AU - Chan, Esther Wai Yin AU - Chan, Sandra Sau Man AU - Li, Xue TI - Cost-effectiveness of esketamine versus alternative treatment strategies for treatment-resistant depression in Hong Kong: A multi-armed modeling study T2 - PLoS medicine J2 - PLoS Med PY - 2026 DA - 2026/04/16 VL - 23 IS - 4 SP - e1005047 SN - 1549-1277 PB - PLOS DO - 10.1371/journal.pmed.1005047 UR - https://doi.org/10.1371/journal.pmed.1005047 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pmed.1005047", "type": "article-journal", "title": "Cost-effectiveness of esketamine versus alternative treatment strategies for treatment-resistant depression in Hong Kong: A multi-armed modeling study", "container-title": "PLoS medicine", "author": [ { "family": "Li", "given": "Yifan" }, { "family": "Chan", "given": "Vivien Kin Yi" }, { "family": "Jit", "given": "Mark" }, { "family": "Cheng", "given": "Franco Wing Tak" }, { "family": "Yiu", "given": "Hei Hang Edmund" }, { "family": "Bishai", "given": "David Makram" }, { "family": "Craig", "given": "Dawn" }, { "family": "Chan", "given": "Esther Wai Yin" }, { "family": "Chan", "given": "Sandra Sau Man" }, { "family": "Li", "given": "Xue" } ], "container-title-short": "PLoS Med", "volume": "23", "issue": "4", "page": "e1005047", "DOI": "10.1371/journal.pmed.1005047", "PMID": "41990095", "PMCID": "PMC13120699", "ISSN": "1549-1277", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pmed.1005047", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 16 ] ] } }

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