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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Statistics and Probability

2020

Harvard University Biostatistics Working Paper Series

Inverse probability weighting

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Full-Text Articles in Physical Sciences and Mathematics

Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin Apr 2020

Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin

Harvard University Biostatistics Working Paper Series

In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well-defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We proposed three approaches to redefining the natural direct and indirect effects, which are generalized forms of the conventional causal effects for survival outcomes. Furthermore, we developed three statistical …


Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh Jan 2020

Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh

Harvard University Biostatistics Working Paper Series

The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to …