Analysis of a Partially Observed Binary Covariate Process and a Censored Failure Time in the Presence of Truncation and Competing Risks

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Abstract
SummaryWe develop methods for assessing the association between a binary time‐dependent covariate process and a failure time endpoint when the former is observed only at a single time point and the latter is right censored, and when the observations are subject to truncation and competing causes of failure. Using a proportional hazards model for the effect of the covariate process on the failure time of interest, we develop an approach utilizing EM algorithm and profile likelihood for estimating the relative risk parameter and cause‐specific hazards for failure. The methods are extended to account for other covariates that can influence the time‐dependent covariate process and cause‐specific risks of failure. We illustrate the methods with data from a recent study on the association between loss of hepatitis B e antigen and the development of hepatocellular carcinoma in a population of chronic carriers of hepatitis B.

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