Discrete-time survival mixture analysis
Discrete-time survival mixture analysis
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DOI：
10.3102/10769986030001027
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发表时间：
2005-03-01
影响因子：
2.4
通讯作者：
Masyn, K
中科院分区：
文献类型：
--
作者：
Muthén, B;Masyn, K
本文提出了一种通用的潜变量方法，用于非重复事件(如吸毒开始)的离散时间生存分析。说明了如何将生存分析表示为事件历史指标的广义潜类分析。潜在类别分析可以使用协变量，并可以与其他结果的联合建模相结合，例如对相关过程的重复测量。结果表明，传统的离散时间生存分析对应于单类潜在类分析。提出了多类扩展，包括一类长期幸存者的特殊情况和由与生存相关的结果定义的类。该估计使用了一个通用的潜在变量框架，包括分类和连续的潜在变量，并纳入了Mplus程序。通过EM算法使用最大似然法进行估计。有两个例子可以作为说明。第一个例子涉及在随机现场实验中监禁后的累犯率。第二个例子涉及到与课堂上攻击性行为发展相关的学校停学。
This article proposes a general latent variable approach to discrete-time survival analysis of nonrepeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint modeling of other outcomes such as repeated measures for a related process. It is shown that conventional discrete-time survival analysis corresponds to a single-class latent class analysis. Multiple-class extensions are proposed, including the special cases of a class of long-term survivors and classes defined by outcomes related to survival. The estimation uses a general latent variable framework, including both categorical and continuous latent variables and incorporated in the Mplus program. Estimation is carried out using maximum likelihood via the EM algorithm. Two examples serve as illustrations. The first example concerns recidivism after incarceration in a randomized field experiment. The second example concerns school removal related to the development of aggressive behavior in the classroom.