Weighting Based Approaches to Borrowing Historical Controls for Indirect comparison for Time-to-Event Data with a Cure Fraction

TL;DR

This paper introduces novel weighting and pseudo observation methods to adjust for population differences when using historical controls in time-to-event studies with cure fractions, enhancing indirect comparison accuracy.

Contribution

It proposes new adjustment approaches using pseudo observations and calibration weighting specifically designed for cure fraction data in time-to-event analysis.

Findings

Proposed methods effectively reduce bias in simulations.

Application demonstrates practical utility in breast cancer study.

New estimators improve survival function estimation with cure fractions.

Abstract

To use historical controls for indirect comparison with single-arm trials, the population difference between data sources should be adjusted to reduce confounding bias. The adjustment is more difficult for time-to-event data with a cure fraction. We propose different adjustment approaches based on pseudo observations and calibration weighting by entropy balancing. We show a simple way to obtain the pseudo observations for the cure rate and propose a simple weighted estimator based on them. Estimation of the survival function in presence of a cure fraction is also considered. Simulations are conducted to examine the proposed approaches. An application to a breast cancer study is presented.