Estimating risk factors for incidence of a disease is crucial for understanding its etiology. For diseases caused by enteric pathogens, off-the-shelf statistical model-based approaches do not consider the biological mechanisms through which infection occurs and thus can only be used to make comparatively weak statements about association between risk factors and incidence. Building off of established work in quantitative microbiological risk assessment, we propose a new approach to determining the association between risk factors and dose accrual rates. Our more mechanistic approach achieves a higher degree of biological plausibility, incorporates currently-ignored sources of variability, and provides regression parameters that are easily interpretable as the dose accrual rate ratio due to changes in the risk factors under study. We also describe a method for leveraging information across multiple pathogens. The proposed methods are available as an R package at \url{https://github.com/dksewell/dare}. Our simulation study shows unacceptable coverage rates from generalized linear models, while the proposed approach empirically maintains the nominal rate even when the model is misspecified. Finally, we demonstrated our proposed approach by applying our method to infant data obtained through the PATHOME study (\url{https://reporter.nih.gov/project-details/10227256}), discovering the impact of various environmental factors on infant enteric infections.

翻译：估计疾病发病的风险因素对于理解其病因学至关重要。对于由肠道病原体引起的疾病，现有的基于统计模型的方法未考虑感染发生的生物学机制，因此仅能用于对风险因素与发病率之间的关联做出相对较弱的陈述。基于定量微生物风险评估领域的既定工作，我们提出了一种确定风险因素与剂量累积率之间关联的新方法。我们这种更具机制性的方法实现了更高程度的生物学合理性，纳入了当前被忽略的变异性来源，并且提供的回归参数易于解释为因所研究风险因素变化而引起的剂量累积率比。我们还描述了一种跨多种病原体利用信息的方法。所提出的方法可通过R软件包获取，地址为 \url{https://github.com/dksewell/dare}。我们的模拟研究表明，广义线性模型的覆盖率不可接受，而所提出的方法即使在模型设定错误的情况下，经验上仍能维持名义覆盖率。最后，我们通过将我们的方法应用于通过PATHOME研究（\url{https://reporter.nih.gov/project-details/10227256}）获得的婴儿数据，展示了所提出的方法，发现了各种环境因素对婴儿肠道感染的影响。