The effect of treatment on binary disease outcome can differ across subgroups characterised by other covariates. Testing for the existence of subgroups that are associated with heterogeneous treatment effects can provide valuable insight regarding the optimal treatment recommendation in practice. Our research in this paper is motivated by the question of whether host genetics could modify a vaccine's effect on HIV acquisition risk. To answer this question, we used data from an HIV vaccine trial with a two‐phase sampling design and developed a general threshold‐based model framework to test for the existence of subgroups associated with the heterogeneity in disease risks, allowing for subgroups based on multivariate covariates. We developed a testing procedure based on maximum of likelihood ratio statistics over change‐planes and demonstrated its advantage over alternative methods. We further developed the testing procedure to account for bias sampling of expensive (i.e. resource‐intensive to measure) covariates through the incorporation of inverse probability weighting techniques. We used the proposed method to analyse the motivating HIV vaccine trial data. Our proposed testing procedure also has broad applications in epidemiological studies for assessing heterogeneity in disease risk with respect to univariate or multivariate predictors.

中文翻译：

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两阶段抽样设计中逻辑回归模型中基于阈值的子组测试

治疗对二元疾病结果的影响在以其他协变量为特征的亚组中可能有所不同。测试是否存在与异质治疗效果相关的亚组可以为实践中的最佳治疗建议提供有价值的见解。我们本文研究的动机是宿主遗传学是否可以改变疫苗对艾滋病毒感染风险的影响。为了回答这个问题，我们使用了两阶段抽样设计的艾滋病毒疫苗试验数据，并开发了一个基于阈值的通用模型框架来测试与疾病风险异质性相关的亚组的存在，允许基于多变量的亚组协变量。我们开发了一种基于变化平面上的最大似然比统计的测试程序，并证明了其相对于其他方法的优势。我们进一步开发了测试程序，通过结合逆概率加权技术来解决昂贵（即测量资源密集型）协变量的偏差采样。我们使用所提出的方法来分析激励艾滋病毒疫苗试验数据。我们提出的测试程序在流行病学研究中也有广泛的应用，用于评估单变量或多变量预测因子的疾病风险异质性。