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A flexible parametric approach for estimating continuous‐time inverse probability of treatment and censoring weights
Author(s) -
Saarela Olli,
Liu Zhihui Amy
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6979
Subject(s) - inverse probability , censoring (clinical trials) , marginal structural model , inverse probability weighting , parametric statistics , statistics , weighting , event (particle physics) , computer science , marginal distribution , proportional hazards model , confounding , parametric model , econometrics , mathematics , posterior probability , medicine , estimator , random variable , bayesian probability , physics , quantum mechanics , radiology
Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time‐dependent confounders, avoiding conditioning on factors on the causal pathway. To estimate the IPTC weights, the treatment assignment mechanism is conventionally modeled in discrete time. While this is natural in situations where treatment information is recorded at scheduled follow‐up visits, in other contexts, the events specifying the treatment history can be modeled in continuous time using the tools of event history analysis. This is particularly the case for treatment procedures, such as surgeries. In this paper, we propose a novel approach for flexible parametric estimation of continuous‐time IPTC weights and illustrate it in assessing the relationship between metastasectomy and mortality in metastatic renal cell carcinoma patients. Copyright © 2016 John Wiley & Sons, Ltd.