×

Marginal structural Cox models with case-cohort sampling. (English) Zbl 1356.62210

Summary: 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 S. R. Cole et al. [“Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death”, Am. J. Epidemilogy 175, No. 5, 381–390 (2012; doi:10.1093/aje/kwr346)], 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62N01 Censored data models
62N02 Estimation in survival analysis and censored data
62F12 Asymptotic properties of parametric estimators
PDFBibTeX XMLCite
Full Text: DOI Link