靶向临床试验效应估计的偏倚及校正

Bias of treatment effects and its correction in targeted clinical trials

随着人类基因组计划的完成,检测出与疾病相关的分子标靶成为可能,针对有特定疾病标靶的靶向药物开发日渐增多,靶向临床试验呈快速增长趋势。然而,以标靶检测阳性作为入组标准的靶向临床试验,由于疾病标靶诊断技术的不完善,致使入组病人中存在假阳性的现象,因而导致了靶向临床试验效应估计的偏倚。本文在分析疾病标靶的诊断性能对效应估计影响的基础上,指出常规分析方法估计效应存在的偏倚,并介绍一种基于EM算法联合参数Bootstrap抽样,实现靶向临床试验效应无偏估计的统计方法,为正确评价靶向临床试验提供了方法学支持。

With the completion of the Human Genome Project, we are able to identify the disease-related targets and apply them to disease treatment by developing targeted drugs. Hence, targeted clinical trials are becoming increasingly popular in recent years. However, due to the imperfect diagnostic techniques the patients enrolled in targeted clinical trials with positive result might not all truly have the targets. To include patients with false positive results would result in the bias of treatment effects. This paper analyses the influence of the accuracy of diagnostic method in targeted clinical trials and figures out the bias of treatment effects. Then a new statistical method which incorporated EM algorithm and parametric bootstrap would be introduced to give an unbiased estimation of the effect size. The method provides an adequate methodological support for the targeted clinical trials.