多地点试验中的因果效应变异性与不遵从性：一种用于二元结局的双变量层级广义随机系数模型

Variability in Causal Effects and Noncompliance in a Multisite Trial: A Bivariate Hierarchical Generalized Random Coefficients Model for a Binary Outcome

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STATISTICS IN MEDICINE

影响因子:1.8

分区:数学3区 / 数学与计算生物学2区医学：研究与实验3区公共卫生3区统计学与概率论3区

发表日期:2024 Dec 10

作者: Xinxin Sun, Yongyun Shin, Jennifer Elston Lafata, Stephen W Raudenbush

DOI: 10.1002/sim.10229

摘要

在170名医生的每一位中，患者被随机分配以访问e-assist，一种旨在增加结直肠癌筛查（CRCS）的在线程序，或对照组。依从性为部分：78.34%$$78.34\%$$的实验患者访问了e-assist，而没有对照组获得访问权限。研究兴趣在于分配治疗的平均因果效应和遵从者平均因果效应，以及这些因果效应在医生之间的变异性。每位医生会生成实验遵从者（访问了e-assist的实验患者）、对照遵从者（如果被分配到e-assist则会访问的对照患者）和永不遵从者（无论何种情况都避免e-assist的患者）的筛查概率。联合估计每位医生的概率面临新颖的挑战。我们采用最大似然法，通过将“完全数据似然”唯一地分解为给定随机效应的条件筛查分布和部分观察到的遵从性分布，并利用自适应高斯-赫尔米特积分对该似然进行边际化。该方法具有双重迭代性质，因为条件分布难以解析评估。由于每位医生样本量较小，限制了多重随机效应的可估性，我们通过具有因子分析结构的共享随机效应模型降低其维度。我们通过模拟评估估计量的性能，并建议样本规模以获得合理准确的估计，同时分析一项CRCS干预试验的数据。

Abstract

Within each of 170 physicians, patients were randomized to access e-assist, an online program that aimed to increase colorectal cancer screening (CRCS), or control. Compliance was partial: 78.34 % $$ 78.34\% $$ of the experimental patients accessed e-assist while no controls were provided the access. Of interest are the average causal effect of assignment to treatment and the complier average causal effect as well as the variation of these causal effects across physicians. Each physician generates probabilities of screening for experimental compliers (experimental patients who accessed e-assist), control compliers (controls who would have accessed e-assist had they been assigned to e-assist), and never takers (patients who would have avoided e-assist no matter what). Estimating physician-specific probabilities jointly over physicians poses novel challenges. We address these challenges by maximum likelihood, factoring a "complete-data likelihood" uniquely into the conditional distribution of screening and partially observed compliance given random effects and the distribution of random effects. We marginalize this likelihood using adaptive Gauss-Hermite quadrature. The approach is doubly iterative in that the conditional distribution defies analytic evaluation. Because the small sample size per physician constrains estimability of multiple random effects, we reduce their dimensionality using a shared random effects model having a factor analytic structure. We assess estimators and recommend sample sizes to produce reasonably accurate and precise estimates by simulation, and analyze data from a trial of a CRCS intervention.