多站点试验中因果效应和不合规的可变性：二进制结果的双变量层次概括随机系数模型
Variability in Causal Effects and Noncompliance in a Multisite Trial: A Bivariate Hierarchical Generalized Random Coefficients Model for a Binary Outcome
影响因子:1.80000
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
摘要
在170名医生中的每一位中，患者被随机访问电子辅助，这是一个在线计划，旨在增加结直肠癌筛查（CRC）或对照。合规性是部分的：78.34％$$ 78.34 \％$$的实验患者访问了电子辅助者，而没有提供对照。感兴趣的是分配治疗的平均因果关系和平均因果关系以及这些因果关系在医生中的变化。每位医生都会产生筛查实验符合剂（访问电子辅助者的实验患者），对照符号（如果将电子辅助者被分配给电子辅助者）和从不接受的人（无论如何都可以避免E-Note sansist的患者）的筛查概率）。共同估计医师对医师的概率提出了新的挑战。我们通过最大可能性解决了这些挑战，将“完全数据的可能性”分解在筛选的条件分布中，并在随机效应的情况下进行部分观察到的依从性。我们使用自适应高斯 - 铁矿正体将这种可能性边缘化。该方法是双重迭代的，因为条件分布违反了分析评估。由于每个医师的样本量较小，因此限制了多个随机效应的估计性，因此我们使用具有因子分析结构的共享随机效应模型降低了它们的维度。我们评估估计器并建议样本量通过模拟产生合理准确和精确的估计，并分析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.