Martin-Luther-University Halle-Wittenberg, Institute of Medical Epidemiology, Biostatistics, and Informatics, Magdeburger Strasse 8, Halle (Saale), Germany.
Comput Methods Programs Biomed. 2012 Sep;107(3):582-97. doi: 10.1016/j.cmpb.2011.05.004. Epub 2011 Jul 18.
In survival analysis individuals are followed over some period and the time until the transition from an initial to a final state is of particular interest. An important tool for analyzing potential risk factors of this transition is the Cox proportional hazards model. This model requires homogeneity in the study population and independence between the observations. An extension of the Cox model to deal with both, unobserved heterogeneity and clustered survival data, are frailty models [1]. Different software are available for the analysis of shared frailty models such as coxph, coxme, phmm, frailtyPenal, SPGAM or SPLN3. This makes it difficult for the user to find the appropriate tool for the specific problem under investigation. To compare the performance of the aforementioned software a large simulation study was conducted. Advantages and limitations of the software are discussed in detail.
在生存分析中，个体在一段时间内被跟踪，从初始状态到最终状态的时间是特别关注的。分析这种转变的潜在风险因素的一个重要工具是 Cox 比例风险模型。该模型要求研究人群具有同质性，并且观察之间具有独立性。Cox 模型的扩展版本用于处理未观察到的异质性和聚类生存数据，是脆弱性模型[1]。用于分析共享脆弱性模型的不同软件，如 coxph、coxme、phmm、frailtyPenal、SPGAM 或 SPLN3。这使得用户难以找到适用于特定研究问题的适当工具。为了比较上述软件的性能，进行了一项大型模拟研究。详细讨论了软件的优缺点。
