Kowall Bernd, Ahrenfeldt Linda Juel, Basten Jale, Becher Heiko, Brand Tilman, Braun Julia, Casjens Swaantje, Claessen Heiner, Denz Robin, Diebner Hans H, Diexer Sophie, Eisemann Nora, Furrer Eva, Galetzka Wolfgang, Girschik Carolin, Karch André, Mikolajczyk Rafael, Peters Manuela, Rospleszcz Susanne, Rücker Viktoria, Stang Andreas, Stolpe Susanne, Taylor Katherine J, Timmesfeld Nina, Tokic Marianne, Zeeb Hajo, Berg-Beckhoff Gabriele, Behrens Thomas, Ittermann Till, Rübsamen Nicole
Institute of Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital of Essen, Hufelandstraße 55, 45147, Essen, Germany.
Research Unit for General Practice, Department of Public Health, University of Southern Denmark, Esbjerg-Odense, Denmark.
Eur J Epidemiol. 2025 May 5. doi: 10.1007/s10654-025-01235-8.
In multi-analyst studies, several analysts use the same data to independently investigate identical research questions. Multi-analyst studies have been conducted mainly in psychology, social sciences, and neuroscience, but rarely in epidemiology. Sixteen analyst groups (24 researchers) with backgrounds mainly in statistics, mathematics, and epidemiology were asked to independently perform an analysis on the influence of marital status (never married versus cohabiting married) on cardiovascular outcomes. They were asked to use data from the Survey of Health, Ageing and Retirement in Europe (SHARE), a panel study of 140,000 persons aged 50 years and above from 28 European countries and Israel, and to provide an effect estimate, a comment on their results, and the full syntax of their analyses. In additional analyses beyond the multi-analyst approach, one group selected an exemplary regression model and varied definitions of exposure and outcome and the confounder adjustment set. Each analysis was unique. The size of the 16 datasets used for the analyses ranged from 15,592 to 336,914 observations. The effect estimates (odds ratios, hazard ratios, or relative risks) ranged from 0.72 to 1.02 (reference: cohabiting married) in strictly or partly cross-sectional analyses and from 0.95 to 1.31 in strictly longitudinal analyses. The choice of regression models, adjustment sets for confounding, and variations in the precise definition of exposure and outcome, all had only small effects on the effect estimates. The range of results was mainly due to differences from cross-sectional versus longitudinal analyses rather than to single analytical decisions each of which had less influence.
在多分析师研究中，若干分析师使用相同的数据独立调查相同的研究问题。多分析师研究主要在心理学、社会科学和神经科学领域开展，但在流行病学领域很少进行。16个分析师团队（24名研究人员），其背景主要是统计学、数学和流行病学，被要求独立分析婚姻状况（从未结婚与同居已婚）对心血管结局的影响。他们被要求使用来自欧洲健康、老龄化与退休调查（SHARE）的数据，这是一项对来自28个欧洲国家和以色列的14万名50岁及以上人群的面板研究，并提供效应估计值、对其结果的评论以及分析的完整语法。在多分析师方法之外的额外分析中，一个团队选择了一个典型的回归模型，并对暴露、结局和混杂因素调整集的定义进行了变化。每项分析都是独特的。用于分析的16个数据集的规模从15592到336914个观测值不等。在严格或部分横断面分析中，效应估计值（比值比、风险比或相对风险）范围为0.72至1.02（参照：同居已婚），在严格纵向分析中范围为0.95至1.31。回归模型的选择、混杂因素的调整集以及暴露和结局精确定义的变化，对效应估计值的影响都很小。结果的范围主要是由于横断面分析与纵向分析的差异，而非单个分析决策的影响，每个决策的影响都较小。
