Dang Qianyu, Mazumdar Sati, Anderson Stewart J, Houck Patricia R, Reynolds Charles F
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Stat Med. 2007 Feb 20;26(4):800-11. doi: 10.1002/sim.2558.
An important research objective in most psychiatric clinical trials of maintenance treatment is to find predictors of recurrence of illness. In those trials, patients are first admitted into an open treatment period also called acute treatment. If they respond to the treatment and are considered to have stable remission from the illness, they enter the second phase of the trial where they are randomized into different arms of the 'maintenance treatments'. Often, more than one response variable is measured longitudinally in the acute treatment phase to monitor treatment responses. Trajectories of these response measures are believed to have predictive ability for recurrences in the maintenance phase of the trial. By using a bivariate growth curve from two such longitudinal measures, we developed a method to use the estimated trajectories of each subject in a Cox regression model to predict recurrence in the maintenance phase. To adjust for the parameter estimation errors, we applied a full likelihood approach based on the conditional expectations of the predictors. Simulation studies indicate that the estimation error corrected estimators for the Cox model parameters are less biased when compared to the naive regression estimators without accounting for these errors. The uniqueness of this method lies in estimating trajectories from bivariate unequally spaced longitudinal response measures. An illustrative example is provided with data from a maintenance treatment trial for major depression in an elderly population. Visual Fortran 90 programs were developed to implement the algorithm.
大多数精神疾病维持治疗临床试验的一个重要研究目标是找到疾病复发的预测因素。在这些试验中，患者首先进入一个开放治疗期，也称为急性治疗期。如果他们对治疗有反应并被认为病情已稳定缓解，就进入试验的第二阶段，在该阶段他们被随机分配到“维持治疗”的不同组。通常，在急性治疗阶段会纵向测量多个反应变量以监测治疗反应。这些反应测量的轨迹被认为对试验维持阶段的复发具有预测能力。通过使用来自两个此类纵向测量的双变量生长曲线，我们开发了一种方法，在Cox回归模型中使用每个受试者的估计轨迹来预测维持阶段的复发。为了校正参数估计误差，我们基于预测变量的条件期望应用了一种全似然方法。模拟研究表明，与未考虑这些误差的朴素回归估计器相比，Cox模型参数的估计误差校正估计器偏差更小。该方法的独特之处在于从双变量不等距纵向反应测量中估计轨迹。文中给出了一个来自老年人群重度抑郁症维持治疗试验数据的示例。开发了Visual Fortran 90程序来实现该算法。
