Joint analysis of time-to-event and multiple binary indicators of latent classes
Joint analysis of time-to-event and multiple binary indicators of latent classes
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DOI：
10.1111/j.0006-341x.2004.00141.x
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发表时间：
2004-03-01
期刊：
影响因子：
1.9
通讯作者：
Larsen, K
中科院分区：
文献类型：
--
作者：
Larsen, K
医学和流行病学研究通常使用多个分类变量来衡量人类健康和功能的特定方面。为了分析这些数据，已经开发了模型，将这些分类变量视为个人健康或功能的"真实"状态的不完美指标。在本文中，潜在类回归模型用于对协变量、潜在类变量（未观察到的健康或功能状态）和观察到的指标（例如，问卷中的变量）。考克斯模型扩展到包括一个潜在的类变量作为预测的时间事件，同时使用信息的潜在类成员资格从多个分类指标。采用期望最大化（EM）算法获得最大似然估计，并根据轮廓似然计算标准误，将非参数基线风险视为滋扰参数。提出了一种基于采样的模型检测方法。它允许图形调查的假设成比例的风险在潜在的类。它也可用于检查其他模型假设，例如给定潜在类别的观测指标没有额外影响。的模型框架和所提出的技术的有用性说明在分析的数据，从妇女的健康和老龄化研究的影响，严重的流动性残疾的时间死亡的老年妇女。
Multiple categorical variables are commonly used in medical and epidemiological research to measure specific aspects of human health and functioning. To analyze such data, models have been developed considering these categorical variables as imperfect indicators of an individual's "true" status of health or functioning. In this article, the latent class regression model is used to model the relationship between covariates, a latent class variable (the unobserved status of health or functioning), and the observed indicators (e.g., variables from a questionnaire). The Cox model is extended to encompass a latent class variable as predictor of time-to-event, while using information about latent class membership available from multiple categorical indicators. The expectation-maximization (EM) algorithm is employed to obtain maximum likelihood estimates, and standard errors are calculated based on the profile likelihood, treating the nonparametric baseline hazard as a nuisance parameter. A sampling-based method for model checking is proposed. It allows for graphical investigation of the assumption of proportional hazards across latent classes. It may also be used for checking other model assumptions, such as no additional effect of the observed indicators given latent class. The usefulness of the model framework and the proposed techniques are illustrated in an analysis of data from the Women's Health and Aging Study concerning the effect of severe mobility disability on time-to-death for elderly women.