Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Addiction Recovery Research Center, Virginia Tech Carilion Research Institute, Roanoke, Virginia, USA.
Stat Med. 2022 May 30;41(12):2276-2290. doi: 10.1002/sim.9354. Epub 2022 Feb 22.
Individual participant data meta-analysis is a frequently used method to combine and contrast data from multiple independent studies. Bayesian hierarchical models are increasingly used to appropriately take into account potential heterogeneity between studies. In this paper, we propose a Bayesian hierarchical model for individual participant data generated from the Cigarette Purchase Task (CPT). Data from the CPT details how demand for cigarettes varies as a function of price, which is usually described as an exponential demand curve. As opposed to the conventional random-effects meta-analysis methods, Bayesian hierarchical models are able to estimate both the study-specific and population-level parameters simultaneously without relying on the normality assumptions. We applied the proposed model to a meta-analysis with baseline CPT data from six studies and compared the results from the proposed model and a two-step conventional random-effects meta-analysis approach. We conducted extensive simulation studies to investigate the performance of the proposed approach and discussed the benefits of using the Bayesian hierarchical model for individual participant data meta-analysis of demand curves.
个体参与者数据荟萃分析是一种常用于合并和对比来自多个独立研究的数据的方法。贝叶斯层次模型越来越多地被用来适当考虑研究之间潜在的异质性。在本文中，我们提出了一种用于从吸烟购买任务（CPT）中产生的个体参与者数据的贝叶斯层次模型。CPT 中的数据详细说明了香烟的需求如何随价格变化，这通常被描述为指数需求曲线。与传统的随机效应荟萃分析方法不同，贝叶斯层次模型能够同时估计研究特定和总体水平的参数，而无需依赖正态性假设。我们将提出的模型应用于一项荟萃分析，该分析包含来自六项研究的基线 CPT 数据，并比较了提出的模型和两步传统随机效应荟萃分析方法的结果。我们进行了广泛的模拟研究来研究所提出方法的性能，并讨论了使用贝叶斯层次模型进行需求曲线个体参与者数据荟萃分析的好处。
