Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events.
causal inference
joint modeling
mediation analysis
recurrent event
survival data
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 09 2023
30 09 2023
Historique:
revised:
21
06
2023
received:
20
11
2022
accepted:
01
07
2023
medline:
5
9
2023
pubmed:
18
7
2023
entrez:
18
7
2023
Statut:
ppublish
Résumé
Recurrent events are commonly encountered in biomedical studies. In many situations, there exist terminal events, such as death, which are potentially related to the recurrent events. Joint models of recurrent and terminal events have been proposed to address the correlation between recurrent events and terminal events. However, there is a dearth of suitable methods to rigorously investigate the causal mechanisms between specific exposures, recurrent events, and terminal events. For example, it is of interest to know how much of the total effect of the primary exposure of interest on the terminal event is through the recurrent events, and whether preventing recurrent event occurrences could lead to better overall survival. In this work, we propose a formal causal mediation analysis method to compute the natural direct and indirect effects. A novel joint modeling approach is used to take the recurrent event process as the mediator and the survival endpoint as the outcome. This new joint modeling approach allows us to relax the commonly used "sequential ignorability" assumption. Simulation studies show that our new model has good finite sample performance in estimating both model parameters and mediation effects. We apply our method to an AIDS study to evaluate how much of the comparative effectiveness of the two treatments and the effect of CD4 counts on the overall survival are mediated by recurrent opportunistic infections.
Banques de données
ClinicalTrials.gov
['NCT00001022']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
4028-4042Subventions
Organisme : NIGMS NIH HHS
ID : U54 GM115458
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL136942
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG063370
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002345
Pays : United States
Informations de copyright
© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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