Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.
Division of Cancer Prevention and Control, Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA.
Stat Med. 2022 Jan 15;41(1):17-36. doi: 10.1002/sim.9221. Epub 2021 Oct 17.
Many prospective biomedical studies collect longitudinal clinical and lifestyle data that are both continuous and discrete. In some studies, there is interest in the association between a binary outcome and the values of these longitudinal measurements at a specific time point. A common problem in these studies is inconsistency in timing of measurements and missing follow-ups which can lead to few measurements at the time of interest. Some methods have been developed to address this problem, but are only applicable to continuous measurements. To address this limitation, we propose a new class of joint models for a binary outcome and longitudinal explanatory variables of mixed types. The longitudinal model uses a latent normal random variable construction with regression splines to model time-dependent trends in mean with a Dirichlet Process prior assigned to random effects to relax distribution assumptions. We also standardize timing of the explanatory variables by relating the binary outcome to imputed longitudinal values at a set time point. The proposed model is evaluated through simulation studies and applied to data from a cancer survivor study of participants in the Women's Health Initiative.
许多有前景的生物医学研究收集连续和离散的纵向临床和生活方式数据。在某些研究中，人们对二元结果与特定时间点这些纵向测量值之间的关联感兴趣。这些研究中的一个常见问题是测量时间的不一致性和随访的缺失，这可能导致在感兴趣的时间点测量值很少。已经开发了一些方法来解决这个问题，但仅适用于连续测量。为了解决这个限制，我们提出了一种新的联合模型类，用于二元结果和混合类型的纵向解释变量。纵向模型使用潜在正态随机变量构造和回归样条来对均值的时变趋势建模，使用 Dirichlet 过程先验为随机效应分配以放宽分布假设。我们还通过将二元结果与设定时间点的推断纵向值相关联来标准化解释变量的时间。通过模拟研究评估了所提出的模型，并将其应用于妇女健康倡议参与者癌症幸存者研究的数据。
