Estimating Causal Treatment Effects in Placebo-Controlled Randomized Clinical Trials When High Placebo Response is Anticipated

2026-09-08

AI Summary

本文提出一种两阶段框架，通过估计标准化因果治疗效应解决高安慰剂反应导致的ITT治疗效应低估问题。

Abstract

In placebo-controlled randomized clinical trials (RCTs), the placebo response significantly modifies treatment effects and diminishes the intention-to-treat (ITT) treatment effect, $Δ_{ITT}$. This study presents a novel two-stage framework for estimating the standardized causal treatment effect, $Δ_{STD}$, among the ITT population, under the assumption that their placebo responses are similar to the levels of self-administering medication at home. The first stage employs a real-world, pragmatic, single-blinded placebo lead-in to measure placebo responses to levels expected during routine at-home use. This is achieved by preserving the participants' expectations and controlling for trial-related factors that inflate the responses. In the second stage, a double-blinded randomized phase is used to estimate the conditional average treatment effect (CATE) as a function of placebo response levels and other important effect modifiers. To facilitate CATE estimation, the prognostic scores, defined as the expected placebo responses, are used for dimension reduction. The causal estimand $Δ_{STD}$ is computed by integrating the CATE function over the distribution of the expected placebo response levels from stage one and other modifiers. We further derive theoretical values for $Δ_{ITT}-Δ_{STD}$ to quantify the underestimated treatment benefit due to high placebo responses. The validity and statistical performance of the proposed framework are evaluated through comprehensive simulations.

Problem

• Placebo Response
• Causal Treatment Effects
• Randomized Clinical Trials
• Intention-to-Treat Effect

Innovation

• two-stage framework
• placebo response
• CATE
• prognostic scores
• dimension reduction