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On Double-Sampling in the Presence of Self-Selected Right Censoring

Constantine Frangakis, Asst Prof of Biostatistics

Follow-up studies, such as after surgery for total-hip-replacement, often suffer from more than one type of missing information. Here we investigate the use of follow-up samples of individuals to estimate survival curves when individuals' times are subject to right-censoring from two sources:
(i) early termination of the study, namely, administrative censoring, or
(ii) censoring due to lost data prior to administrative censoring, so-called dropout.
We assume that, for the full cohort of individuals, administrative censoring times are independent of the subjects' inherent characteristics, including survival time. To address the loss to censoring due to dropout, which we allow to be possibly selective, we consider an intensive second phase of the study where a sample of the originall y lost subjects is subsequently followed and their data recorded. As with double-sampling designs in survey methodology, the objective is to provide data on a representative subset of the dropouts. Despite assumed full response from the follow-up sample, we show that, in general in our setting, administrative censoring times are not independent of survival times within the two subgroups, the nondropouts and the representative sample of the dropouts. As a result, the stratified Kaplan-Meier estimator is not appropriate for the cohort survival curve. Moreover, using the concept of potential outcomes, as opposed to observed outcomes, and thereby explicitly formulating the problem as a missing data one, reveals and addresses these complications. We present an estimation method based on the likelihood of a subset of the data and study its properties for large samples, and in simulations of follow-up after surgery for total-hip-replacement.


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