Leiby Benjamin E, Ten Have Thomas R, Lynch Kevin G, Sammel Mary D
Division of Biostatistics, Thomas Jefferson University, 1015 Chestnut St. Suite M100, Philadelphia, PA 19107, U.S.A.
Stat Med. 2014 Sep 10;33(20):3434-52. doi: 10.1002/sim.5596. Epub 2012 Sep 7.
In many clinical studies, the disease of interest is multifaceted, and multiple outcomes are needed to adequately capture information about the characteristics of the disease or its severity. In the analysis of such diseases, it is often difficult to determine what constitutes improvement because of the multivariate nature of the outcome. Furthermore, when the disease of interest has an unknown etiology and/or is primarily a symptom-defined syndrome, there is potential for the disease population to have distinct subgroups. Identification of population subgroups is of interest as it may assist clinicians in providing appropriate treatment or in developing accurate prognoses. We propose multivariate growth curve latent class models that group subjects on the basis of multiple symptoms measured repeatedly over time. These groups or latent classes are defined by distinctive longitudinal profiles of a latent variable, which is used to summarize the multivariate outcomes at each point. The mean growth curve for the latent variable in each class defines the features of the class. We develop this model for any combination of continuous, binary, ordinal, or count outcomes within a Bayesian hierarchical framework. We use simulation studies to validate the estimation procedures. We apply our model to data from a randomized clinical trial evaluating the efficacy of Bacillus Calmette-Guerin in treating symptoms of interstitial cystitis where we are able to identify a class of subjects for whom treatment is effective.
在许多临床研究中，所关注的疾病是多方面的，需要多个结局来充分获取有关疾病特征或严重程度的信息。在分析此类疾病时，由于结局的多变量性质，往往难以确定什么构成改善。此外，当所关注的疾病病因不明和/或主要是症状定义的综合征时，疾病人群有可能存在不同的亚组。识别亚组人群很有意义，因为它可能有助于临床医生提供适当的治疗或制定准确的预后。我们提出了多变量生长曲线潜在类别模型，该模型根据随时间重复测量的多种症状对受试者进行分组。这些组或潜在类别由一个潜在变量的独特纵向概况定义，该潜在变量用于总结每个时间点的多变量结局。每个类别中潜在变量的平均生长曲线定义了该类别的特征。我们在贝叶斯分层框架内为连续、二元、有序或计数结局的任何组合开发了该模型。我们使用模拟研究来验证估计程序。我们将我们的模型应用于一项评估卡介苗治疗间质性膀胱炎症状疗效的随机临床试验数据，在该试验中我们能够识别出一类治疗有效的受试者。
