Sheikh Md Tuhin, Chen Ming-Hui, Gelfond Jonathan A, Sun Wei, Ibrahim Joseph G
Department of Statistics, University of Connecticut, Storrs, Connecticut, USA.
Department of Epidemiology and Biostatistics, University of Texas Health, Houston, Texas, USA.
Stat Med. 2023 Apr 30;42(9):1308-1322. doi: 10.1002/sim.9671. Epub 2023 Jan 25.
Competing risks survival data in the presence of partially masked causes are frequently encountered in medical research or clinical trials. When longitudinal biomarkers are also available, it is of great clinical importance to examine associations between the longitudinal biomarkers and the cause-specific survival outcomes. In this article, we propose a cause-specific C-index for joint models of longitudinal and competing risks survival data accounting for masked causes. We also develop a posterior predictive algorithm for computing the out-of-sample cause-specific C-index using Markov chain Monte Carlo samples from the joint posterior of the in-sample longitudinal and competing risks survival data. We further construct the C-index to quantify the strength of association between the longitudinal and cause-specific survival data, or between the out-of-sample longitudinal and survival data. Empirical performance of the proposed assessment criteria is examined through an extensive simulation study. An in-depth analysis of the real data from large cancer prevention trials is carried out to demonstrate the usefulness of the proposed methodology.
在医学研究或临床试验中，经常会遇到存在部分隐匿原因的竞争风险生存数据。当纵向生物标志物也可用时，研究纵向生物标志物与特定原因生存结果之间的关联具有重要的临床意义。在本文中，我们提出了一种针对纵向和竞争风险生存数据联合模型的特定原因C指数，该模型考虑了隐匿原因。我们还开发了一种后验预测算法，用于使用来自样本内纵向和竞争风险生存数据联合后验的马尔可夫链蒙特卡罗样本计算样本外特定原因C指数。我们进一步构建C指数，以量化纵向和特定原因生存数据之间，或样本外纵向和生存数据之间的关联强度。通过广泛的模拟研究检验了所提出评估标准的实证性能。对大型癌症预防试验的真实数据进行了深入分析，以证明所提出方法的实用性。
