In many clinical trials, the endpoint of interest may not be available immediately, but rather evolves over time. Examples of this are numerous. Survival time is clearly such an example, but also cost of care, quality-adjusted lifetime, or even dichotomous response, such as whether viral load will go below detectable limits after treatment for AIDS patients, are examples of time-lagged responses. The lag time may be part of the biological process or due to administrative delays.Since patient entry is staggered and follow-up is of limited duration, some of the response variables will be missing due to censoring of the lag time. Naive estimates using complete data will be biased with censored data. Moreover, standard methods developed for censored survival problems will also be biased for such problems because of induced informative censoring. We will show how the theory of inverse probability weighting of complete cases developed by Robins and Rotnitzky can be used to find consistent estimators for the mean of the time-lagged variable. We will also show how to use additional information collected during the study to increase efficiency.
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