Indirect comparisons have been increasingly used to compare data from different sources such as clinical trials and observational data in, e.g., a disease registry. To adjust for population differences between data sources, matching-adjusted indirect comparison (MAIC) has been used in several applications including health technology assessment and drug regulatory submissions. In fact, MAIC can be considered as a special case of a range of methods known as calibration estimation in survey sampling. However, to our best knowledge, this connection has not been examined in detail. This paper makes three contributions: 1. We examined this connection by comparing MAIC and a few commonly used calibration estimation methods, including the entropy balancing approach, which is equivalent to MAIC. 2. We considered the standard error (SE) estimation of the MAIC estimators and propose a model-independent SE estimator and examine its performance by simulation. 3. We conducted a simulation to compare these commonly used approaches to evaluate their performance in indirect comparison scenarios.
翻译:为了适应数据来源之间的人口差异,在包括保健技术评估和提交药物管制文件在内的若干应用中采用了经校正调整的间接比较(MAIC),事实上,MAIC可被视为调查抽样中称为校准估计的一系列方法的特例,然而,据我们所知,这一联系没有得到详细研究,本文件作出了三项贡献:1. 我们通过比较MAIC和一些常用的校准估计方法,包括相当于MAIC的酶平衡法,审查了这一联系。 2. 我们审议了MAIC估计标准误差(SE),提出了独立的SE估计模型,并通过模拟审查了其性能。3. 我们进行了模拟,比较了这些常用的方法,以评估其在间接比较情景中的性能。