Altstein L, Li G
Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA.
Biometrics. 2013 Mar;69(1):52-61. doi: 10.1111/j.1541-0420.2012.01818.x. Epub 2013 Feb 5.
This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma.
本文研究了一种半参数加速失效时间混合模型，用于在随机临床试验中估计生物治疗对潜在感兴趣亚组的治疗效果，该亚组具有事件发生时间结局。之所以会产生潜伏期，是因为在试验的一个组中成员身份是可观察到的，而在另一组中则无法确定。当对照组患者无法接受活性治疗时，这种方法在全有或全无的不依从随机临床试验中很有用，例如在肿瘤学试验中，用于识别潜在亚组的活检仅在随机接受活性治疗的受试者身上进行。我们推导了一种计算方法，通过在期望步骤和加权Buckley-James优化步骤之间迭代来估计模型参数。采用自助法进行方差估计，并在模拟中验证了我们方法的性能。我们通过对一项多中心黑色素瘤选择性淋巴结清扫试验的分析来说明我们的方法。
