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Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data

机译:在没有头对头临床试验数据的情况下比较药物功效的方法概述

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摘要

In most therapeutic areas, multiple drug options are increasingly becoming available, but there is often a lack of evidence from head-to-head clinical trials that allows for direct comparison of the efficacy and/or safety of one drug vs. another. This review provides an introduction to, and overview of, common methods used for comparing drugs in the absence of head-to-head clinical trial evidence. Na?ve direct comparisons are in most instances inappropriate and should only be used for exploratory purposes and when no other options are possible. Adjusted indirect comparisons are currently the most commonly accepted method and use links through one or more common comparators. Mixed treatment comparisons (MTCs) use Bayesian statistical models to incorporate all available data for a drug, even data that are not relevant to the comparator drug. MTCs reduce uncertainty but have not yet been widely accepted by researchers, nor drug regulatory and reimbursement authorities. All indirect analyses are based on the same underlying assumption as meta-analyses, namely that the study populations in the trials being compared are similar.
机译:在大多数治疗领域,越来越多的药物可供选择,但是经常缺乏从头对头临床试验得到的证据,无法直接比较一种药物与另一种药物的功效和/或安全性。这篇综述提供了在没有头对头临床试验证据的情况下用于比较药物的常用方法的介绍和概述。幼稚的直接比较在大多数情况下是不合适的,仅应用于探索性目的,并且在没有其他选择的可能时。调整后的间接比较是当前最普遍接受的方法,并使用通过一个或多个常用比较器的链接。混合治疗比较(MTC)使用贝叶斯统计模型来合并药物的所有可用数据,甚至与比较药物无关的数据。 MTC减少了不确定性,但尚未被研究人员,药物监管和报销当局广泛接受。所有间接分析均基于与荟萃分析相同的基本假设,即所比较的试验中的研究人群相似。

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