Marginal Structural Cox Models with Case-Cohort Sampling.

TitleMarginal Structural Cox Models with Case-Cohort Sampling.
Publication TypeJournal Article
Year of Publication2016
AuthorsLee, Hana, Michael G. Hudgens, Jianwen Cai, and Stephen R. Cole
JournalStat Sin
Date Published2016 Apr

A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estimation of parameters in the semiparametric marginal structural Cox model (MSCM) from a case-cohort study. Case-cohort sampling entails assembling covariate histories only for cases and a random subcohort, which can be cost effective, particularly in large cohort studies with low outcome rates. Following Cole et al. (2012), we consider estimating the causal hazard ratio from a MSCM by maximizing a weighted-pseudo-partial-likelihood. The estimator is shown to be consistent and asymptotically normal under certain regularity conditions. Finite sample performance of the proposed estimator is evaluated in a simulation study. In the corresponding supplementary document, computation of the estimator using standard survival analysis software is presented.

Alternate JournalStat Sin
Original PublicationMarginal structural Cox models with case-cohort sampling.
PubMed ID27057128
PubMed Central IDPMC4820319
Grant ListR01 AI085073 / AI / NIAID NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI042853 / AI / NIAID NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
R01 AI100654 / AI / NIAID NIH HHS / United States
P30 AI050410 / AI / NIAID NIH HHS / United States