Estimating the effect of treatment in a proportional hazards model in the presence of non-compliance and contamination
Jack Cuzick, Peter Sasieni, Jonathan Myles, Jonathan Tyrer
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2007 · Volume 69 · Issue 4
DOI: 10.1111/j.1467-9868.2007.00600.x
Abstract
Summary.Methods for adjusting for non-compliance and contamination, which respect the randomization, are extended from binary outcomes to time-to-event analyses by using a proportional hazards model. A simple non-iterative method is developed when there are no covariates, which is a generalization of the Mantel–Haenszel estimator. More generally, a ‘partial likelihood’ is developed which accommodates covariates under the assumption that they are independent of compliance. A key feature is that the proportion of contaminators and non-compliers in the risk set is updated at each failure time. When covariates are not independent of compliance, a full likelihood is developed and explored, but this leads to a complex estimator. Estimating equations and information matrices are derived for these estimators and they are evaluated by simulation studies.
Keywords
Non-compliance, Partial likelihood, Proportional hazards model, Randomized clinical trials, Semiparametric models
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