Cure rate estimation with insufficient follow-up: A median-based bootstrap correction approach
Yumiko Ibi, Takashi Omori
TL;DR
This paper introduces a new method to estimate cure rates in clinical trials when follow-up data is limited.
Contribution
The novel approach uses a median-based bootstrap correction to reduce bias in cure rate estimation.
Findings
The proposed method showed smaller variation due to outliers compared to existing methods.
It enabled stable estimation of cure rates in clinical trials with insufficient follow-up.
The method was successfully applied to real data from a primary biliary cirrhosis study.
Abstract
The cure rate in clinical trials can be estimated using the Kaplan–Meier (KM) estimator. However, when the clinical trial follow-up period is insufficient and short, the KM estimator may overestimate the proportion of cured patients. Although a correction method was proposed by Escobar-Bach and Keilegom based on bootstrap sampling, this can also lead to bias when the bootstrap distribution is skewed. We propose a median-based approach for bootstrap samples to address these issues. Simulation results showed that the effect of the variation of the proposed method due to many outliers was smaller than that of the other method and enabled stable estimation. The method was successfully applied to real clinical trial data from a D-penicillamine study on primary biliary cirrhosis.
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods in Clinical Trials · Advanced Causal Inference Techniques