ABSTRACT
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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