Gueorguieva Ralitza, Rosenheck Robert, Lin Haiqun
Yale University School of Medicine, New Haven, USA.
J R Stat Soc Ser A Stat Soc. 2012 Apr;175(2):417-433. doi: 10.1111/j.1467-985X.2011.00719.x. Epub 2011 Aug 4.
The 'Clinical antipsychotic trials in intervention effectiveness' study, was designed to evaluate whether there were significant differences between several antipsychotic medications in effectiveness, tolerability, cost and quality of life of subjects with schizophrenia. Overall, 74 % of patients discontinued the study medication for various reasons before the end of 18 months in phase I of the study. When such a large percentage of study participants fail to complete the study schedule, it is not clear whether the apparent profile in effectiveness reflects genuine changes over time or is influenced by selection bias, with participants with worse (or better) outcome values being more likely to drop out or to discontinue. To assess the effect of dropouts for different reasons on inferences, we construct a joint model for the longitudinal outcome and cause-specific dropouts that allows for interval-censored dropout times. Incorporating the information regarding the cause of dropout improves inferences and provides better understanding of the association between cause-specific dropout and the outcome process. We use simulations to demonstrate the advantages of the joint modelling approach in terms of bias and efficiency.
“干预有效性的临床抗精神病药物试验”研究旨在评估几种抗精神病药物在精神分裂症患者的有效性、耐受性、成本和生活质量方面是否存在显著差异。总体而言,在研究的第一阶段,74%的患者在18个月结束前因各种原因停用了研究药物。当如此大比例的研究参与者未能完成研究计划时,尚不清楚有效性方面的明显特征是反映了随时间的真实变化,还是受到选择偏倚的影响,即结局值较差(或较好)的参与者更有可能退出或停药。为了评估不同原因导致的退出对推断的影响,我们构建了一个纵向结局和特定原因退出的联合模型,该模型考虑了区间删失的退出时间。纳入关于退出原因的信息可改善推断,并能更好地理解特定原因退出与结局过程之间的关联。我们使用模拟来证明联合建模方法在偏差和效率方面的优势。