Northern Arizona University, Flagstaff, AZ, USA.
Cincinnati Children's Hospital, University of Cincinnati, Cincinnati, OH, USA.
BMC Med Res Methodol. 2019 Mar 12;19(1):56. doi: 10.1186/s12874-019-0700-5.
Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes.
In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes.
The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications.
The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes.
家庭健康史（FHH）本质上涉及收集相关家庭成员健康状况的代理报告。传统上，此类信息是从单一信息源收集的。最近的研究表明，采用多信息源方法收集 FHH 可提高个体风险评估的准确性。同样，最近的研究强调了将与健康相关的行为纳入基于 FHH 的风险计算中的重要性。将多个 FHH 账户与家庭成员的健康相关行为信息相结合，代表了一个重大的方法学挑战，因为这种 FHH 数据本质上是分层的，并且源自潜在易出错的过程。
在本文中，我们引入了一个统计模型，该模型使用疾病流行率背景变化的信息先验和其他潜在相关变量的影响来解决这些挑战，同时考虑到这些数据的嵌套结构。我们的实证示例来自先前发表的关于有糖尿病病史的家庭的数据。
比较模型评估的结果表明，仅考虑多信息源 FHH 数据的结构化性质就可以提高分类准确性，并且将家庭成员的健康相关行为信息纳入模型比替代规范更受欢迎。
拟议的建模框架是一种灵活的解决方案，可用于整合多信息源 FHH 以进行风险预测。
