Eickhoff Jens C
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726-2397, USA.
Stat Med. 2008 Apr 30;27(9):1387-402. doi: 10.1002/sim.3180.
Quality of life (QoL) has become an accepted and widely used endpoint in clinical trials. The analytical tools used for QoL evaluations in clinical trials differ from those used for the more traditional endpoints, such as response to disease, overall survival or progression-free survival. Since QoL assessments are generally performed on self-administered questionnaires, QoL endpoints are more prone to a placebo effect than traditional clinical endpoints. The placebo effect is a well-documented phenomenon in clinical trials, which has led to dramatic consequences on the clinical development of new therapeutic agents. In order to account for the placebo effect, a multivariate latent variable model is proposed, which allows for misclassification in the QoL item responses. The approach is flexible in the sense that it can be used for the analysis of a wide variety of multi-dimensional QoL instruments. For statistical inference, maximum likelihood estimates and their standard errors are obtained using a Monte Carlo EM algorithm. The approach is illustrated with analysis of data from a cardiovascular phase III clinical trial.
生活质量（QoL）已成为临床试验中被认可且广泛使用的终点指标。临床试验中用于生活质量评估的分析工具与用于更传统终点指标（如疾病反应、总生存期或无进展生存期）的分析工具不同。由于生活质量评估通常通过自我管理问卷进行，与传统临床终点指标相比，生活质量终点指标更容易受到安慰剂效应的影响。安慰剂效应是临床试验中一个有充分记录的现象，这对新型治疗药物的临床开发产生了重大影响。为了考虑安慰剂效应，提出了一种多变量潜在变量模型，该模型允许生活质量项目反应中出现错误分类。这种方法具有灵活性，因为它可用于分析各种多维生活质量工具。对于统计推断，使用蒙特卡罗期望最大化（EM）算法获得最大似然估计及其标准误差。通过对一项心血管III期临床试验的数据进行分析来说明该方法。
