Transporting summary measures of relative effects from randomised trials to the treated patient population: an application to breast cancer endocrine therapy

2026-09-21

AI Summary

该研究通过结合常规护理数据和随机试验的相对效应指标，解决了估算治疗绝对效应的问题，以评估乳腺癌内分泌疗法的效果。

Abstract

Randomised trials often report relative treatment effects, such as risk ratios and hazard ratios, for trial populations. Clinical decision-making, however, often benefits from estimates of absolute treatment effects in the population eligible for treatment. Trial participants may not represent this target population well, and restrictions on access to individual participant trial data can further complicate absolute effect estimation. Routine care data are often representative of the target population but may be subject to uncontrolled confounding. We consider estimation of the average treatment effect on the treated (ATT), an absolute measure, by combining a representative sample of treated routine care patients with summary measures (i.e., estimated risk or hazard ratios) from either a randomised trial or a meta-analysis of trials. Under marginal or conditional transportability assumptions, the ATT is shown to be identifiable. The implications of collapsibility of the effect measure on transportability are discussed, and plug-in estimators of the ATT are presented. Simulation studies are used to assess finite sample performance of the estimators in a range of settings. The proposed methods are applied to estimate the ATT of endocrine therapy on 15-year breast cancer mortality using results from a meta-analysis of randomised trials and England's National Disease Registration Service.

Problem

• randomised trials
• absolute treatment effects
• average treatment effect on the treated (ATT)

Innovation

• average treatment effect on the treated (ATT)
• summary measures
• transportability assumptions
• collapsibility of effect measure
• plug-in estimators