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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Nonparametric inference for assessing treatment efficacy in randomized clinical trials with a time-to-event outcome
Robert M Elashoff1, Gang Li, Ying Zhou
1Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California 90095, U.S.A. , relashof@biomath.ucla.edu.
Abstract:
To evaluate the biological efficacy of a treatment in a randomized clinical trial, one needs to compare patients in the treatment arm who actually received treatment with the subgroup of patients in the control arm who would have received treatment had they been randomized into the treatment arm. In practice, subgroup membership in the control arm is usually unobservable. This paper develops a nonparametric inference procedure to compare subgroup probabilities with right-censored time-to-event data and unobservable subgroup membership in the control arm. We also present a procedure to estimate the onset and duration of treatment effect. The performance of our method is evaluated by simulation. An illustration is given using a randomized clinical trial for melanoma.
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