With advancement of medicine, alternative exposures or interventions are emerging with respect to a common outcome, and there are needs to formally test the difference in the associations of multiple exposures. We propose a duplication method-based multivariate Wald test in the Cox proportional hazard regression analyses to test the difference in the associations of multiple exposures with a same outcome. The proposed method applies to linear or categorical exposures. To illustrate our method, we applied our method to compare the associations between alignment to two different dietary patterns, either as continuous or quartile exposures, and incident chronic diseases, defined as a composite of CVD, cancer, and diabetes, in the Health Professional Follow-up Study. Relevant sample codes in R that implement the proposed approach are provided. The proposed duplication-method-based approach offers a flexible, formal statistical test of multiple exposures for the common outcome with minimal assumptions.

翻译：随着医学的进步，针对同一结局的替代暴露或干预措施不断涌现，需要正式检验多个暴露因素关联差异的需求日益增长。本文提出了一种基于重复数据方法的多元Wald检验方法，用于Cox比例风险回归分析中检验多个暴露因素与同一结局的关联差异。该方法适用于线性或分类暴露变量。为说明该方法的应用，我们将其应用于健康专业人员随访研究数据，比较两种不同膳食模式（以连续变量或四分位数形式）与复合慢性病结局（包括心血管疾病、癌症和糖尿病）的关联。文中提供了实现该方法的R语言示例代码。所提出的基于重复数据方法的方法为检验多个暴露因素与同一结局的关联提供了灵活且假设条件较少的正式统计检验。