Robust Evaluation of Longitudinal Surrogate Markers with Censored Data
arxiv(2024)
摘要
The development of statistical methods to evaluate surrogate markers is an
active area of research. In many clinical settings, the surrogate marker is not
simply a single measurement but is instead a longitudinal trajectory of
measurements over time, e.g., fasting plasma glucose measured every 6 months
for 3 years. In general, available methods developed for the single-surrogate
setting cannot accommodate a longitudinal surrogate marker. Furthermore, many
of the methods have not been developed for use with primary outcomes that are
time-to-event outcomes and/or subject to censoring. In this paper, we propose
robust methods to evaluate a longitudinal surrogate marker in a censored
time-to-event outcome setting. Specifically, we propose a method to define and
estimate the proportion of the treatment effect on a censored primary outcome
that is explained by the treatment effect on a longitudinal surrogate marker
measured up to time t_0. We accommodate both potential censoring of the
primary outcome and of the surrogate marker. A simulation study demonstrates
good finite-sample performance of our proposed methods. We illustrate our
procedures by examining repeated measures of fasting plasma glucose, a
surrogate marker for diabetes diagnosis, using data from the Diabetes
Prevention Program (DPP).
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