Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy.
Clin Pharmacol Ther. 2012 May;91(5):863-71. doi: 10.1038/clpt.2011.322.
Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.
在评估抗抑郁药物的临床试验中，汉密尔顿抑郁量表（HAM D）评分的时间过程建模可能会遇到许多困难：（i）灵活的设计，用于增加选择更有效剂量的机会，（ii）脱落事件，以及（iii）与实验化合物相关的不良反应。在设计 HAM D 时间过程的适当模型时，必须考虑到所有这些因素，并获得脱落过程的现实描述。在这项工作中，我们提出了一种综合方法来建模一项双盲、灵活剂量、安慰剂对照、二期抑郁症试验，包括反应、耐受性和脱落。我们根据信息量研究了三种不同的脱落机制。通过响应（HAM D 评分）和脱落数据对拟合优度进行定量评估。我们表明，脱落是一种复杂的现象，可能受到 HAM D 演变、剂量变化和药物相关不良反应发生的影响。
