发表机构
Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center; Department of Statistics, SungKyunKwan University; Biostatistics Department, Boston University（贝斯以色列女医生医疗中心心脏结局研究史密斯中心; 成均馆大学统计系; 波士顿大学生物统计学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高安慰剂反应削弱ITT效应的难题，提出两阶段框架估计标准化因果治疗效果，通过单盲导入期测量安慰剂反应并利用预后评分估计CATE，模拟验证有效性。
AI 中文摘要
在安慰剂对照随机临床试验（RCT）中，安慰剂反应显著改变治疗效果，并削弱意向性治疗（ITT）治疗效果 $\Delta_{ITT}$。本研究提出一种新颖的两阶段框架，用于在假设ITT人群的安慰剂反应与在家自行服药水平相似的情况下，估计标准化因果治疗效果 $\Delta_{STD}$。第一阶段采用真实世界、务实、单盲的安慰剂导入期，以测量在常规居家使用期间预期的安慰剂反应水平。这是通过保持参与者的期望并控制使反应膨胀的试验相关因素来实现的。第二阶段使用双盲随机阶段，估计作为安慰剂反应水平和其他重要效应修饰因子函数的条件平均治疗效果（CATE）。为促进CATE估计，使用预后评分（定义为预期安慰剂反应）进行降维。因果估计量 $\Delta_{STD}$ 通过将CATE函数对第一阶段预期安慰剂反应水平分布及其他修饰因子的积分来计算。我们进一步推导 $\Delta_{ITT}-\Delta_{STD}$ 的理论值，以量化因高安慰剂反应而低估的治疗获益。通过综合模拟评估所提出框架的有效性和统计性能。
英文摘要
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.
