Brown Elizabeth R, Ibrahim Joseph G
Department of Biostatistics, University of Washington, Seattle, Washington 98105, USA.
Biometrics. 2003 Sep;59(3):686-93. doi: 10.1111/1541-0420.00079.
Complex issues arise when investigating the association between longitudinal immunologic measures and time to an event, such as time to relapse, in cancer vaccine trials. Unlike many clinical trials, we may encounter patients who are cured and no longer susceptible to the time-to-event endpoint. If there are cured patients in the population, there is a plateau in the survival function, S(t), after sufficient follow-up. If we want to determine the association between the longitudinal measure and the time-to-event in the presence of cure, existing methods for jointly modeling longitudinal and survival data would be inappropriate, since they do not account for the plateau in the survival function. The nature of the longitudinal data in cancer vaccine trials is also unique, as many patients may not exhibit an immune response to vaccination at varying time points throughout the trial. We present a new joint model for longitudinal and survival data that accounts both for the possibility that a subject is cured and for the unique nature of the longitudinal data. An example is presented from a cancer vaccine clinical trial.
在癌症疫苗试验中，研究纵向免疫学指标与事件发生时间（如复发时间）之间的关联时会出现复杂问题。与许多临床试验不同，我们可能会遇到已治愈且不再易患该事件终点的患者。如果总体中有已治愈的患者，经过充分随访后，生存函数S(t)会出现一个平台期。如果我们想在存在治愈情况时确定纵向指标与事件发生时间之间的关联，现有的纵向和生存数据联合建模方法将不合适，因为它们没有考虑到生存函数中的平台期。癌症疫苗试验中纵向数据的性质也很独特，因为在整个试验的不同时间点，许多患者可能对疫苗接种没有免疫反应。我们提出了一种新的纵向和生存数据联合模型，该模型既考虑了受试者已治愈的可能性，也考虑了纵向数据的独特性质。文中给出了一个癌症疫苗临床试验的例子。
