Department of Paediatrics, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
Am J Epidemiol. 2018 Aug 1;187(8):1780-1790. doi: 10.1093/aje/kwy070.
Investigators in large-scale population health studies face increasing difficulties in recruiting representative samples of participants. Nonparticipation, item nonresponse, and attrition, when follow-up is involved, often result in highly selected samples even in well-designed studies. We aimed to assess the potential value of multilevel regression and poststratification, a method previously used to successfully forecast US presidential election results, for addressing biases due to nonparticipation in the estimation of population descriptive quantities in large cohort studies. The investigation was performed as an extensive case study using baseline data (2013-2014) from a large national health survey of Australian males (Ten to Men: The Australian Longitudinal Study on Male Health). Analyses were performed in the open-source Bayesian computational package RStan. Results showed greater consistency and precision across population subsets of varying sizes when compared with estimates obtained using conventional survey sampling weights. Estimates for smaller population subsets exhibited a greater degree of shrinkage towards the national estimate. Multilevel regression and poststratification provides a promising analytical approach to addressing potential participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.
在大规模人群健康研究中，调查人员越来越难以招募到具有代表性的参与者样本。即使在设计良好的研究中，不参与、项目无应答和随访时的流失也常常导致高度选择性的样本。我们旨在评估多层次回归和后分层的潜在价值，这种方法以前曾被用于成功预测美国总统选举结果，用于解决由于在大型队列研究中不参与对人口描述性数量的估计而导致的偏差。该研究是一项广泛的案例研究，使用了澳大利亚男性大型全国健康调查（Ten to Men：澳大利亚男性健康纵向研究）的基线数据（2013-2014 年）。分析是在开源贝叶斯计算包 RStan 中进行的。结果表明，与使用传统调查抽样权重获得的估计值相比，在不同大小的人口子集中，结果更加一致和精确。对于较小的人口子集，估计值向全国估计值的收缩程度更大。多层次回归和后分层为解决从大型健康调查和队列研究中估计人口描述性数量时可能存在的参与偏差提供了一种有前途的分析方法。
