Estimating the effect of treatment in a proportional hazards model in the presence of non‐compliance and contamination

Jack Cuzick, Peter Sasieni, Jonathan Myles and Jonathan Tyrer

Journal of the Royal Statistical Society Series B, 2007, vol. 69, issue 4, 565-588

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.

Date: 2007
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