Title | Marginal Structural Cox Models with Case-Cohort Sampling. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Lee, Hana, Michael G. Hudgens, Jianwen Cai, and Stephen R. Cole |
Journal | Stat Sin |
Volume | 26 |
Issue | 2 |
Pagination | 509-526 |
Date Published | 2016 Apr |
ISSN | 1017-0405 |
Abstract | 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. |
DOI | 10.5705/ss.2014.015 |
Alternate Journal | Stat Sin |
Original Publication | Marginal structural Cox models with case-cohort sampling. |
PubMed ID | 27057128 |
PubMed Central ID | PMC4820319 |
Grant List | R01 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 |
Marginal Structural Cox Models with Case-Cohort Sampling.
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