Marital status and risk of cardiovascular disease – a multi-analyst study in epidemiology

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.

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Publication Details

Journal
European Journal of Epidemiology
Published
2025-05-01
DOI
https://doi.org/10.1007/s10654-025-01235-8
Primary Topic
Health disparities and outcomes
Type
article
Field-Weighted Citation Impact
0.00

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article

Marital status and risk of cardiovascular disease – a multi-analyst study in epidemiology

Eva Furrer, Kathy Taylor, Nina Timmesfeld, Susanne Rospleszcz et al.
European Journal of Epidemiology
Health disparities and outcomes
article

Marital status and risk of cardiovascular disease – a multi-analyst study in epidemiology

Eva Furrer, Kathy Taylor, Nina Timmesfeld, Susanne Rospleszcz, Swaantje Casjens, Thomas Behrens, Manuela Peters, Heiko Becher, Wolfgang Galetzka, Gabriele Berg‐Beckhoff, Heiner Claessen, Susanne Stolpe, Viktoria Rücker, Marianne Tokic, Hajo Zeeb, Linda Juel Ahrenfeldt, Carolin Girschik, Sophie Diexer, Hans H. Diebner, Robin Denz, Tilman Brand, Nora Eisemann, Rafael Mikolajczyk, André Karch, Jale Basten, Bernd Kowall, Till Ittermann, Nicole Rübsamen, Julia Braun, Andreas Stang
article en

Abstract

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.

European Journal of EpidemiologyVol. 40(5)
Boston University (US), University of Freiburg (DE), University of Southern Denmark (DK), University of Bremen (DE), Johannes Gutenberg University Mainz (DE), University of Zurich (CH), Heidelberg University (DE), University of Münster (DE), University of Würzburg (DE), Universitätsmedizin Greifswald (DE), Prevention Institute (US), University Hospital Heidelberg (DE), Helmholtz Zentrum München (DE), Institut für Medizinische Informatik, Biometrie und Epidemiologie (DE), Zimmer Biomet (Germany) (DE), Leibniz Institute for Prevention Research and Epidemiology - BIPS (DE), Essen University Hospital (DE), Institute for Prevention and Occupational Medicine (DE), Deutsches Diabetes-Zentrum e.V. (DE), Institute for Medical Informatics and Biostatistics (CH), Heinrich Heine University Düsseldorf (DE), Martin Luther University Halle-Wittenberg (DE), Ruhr University Bochum (DE), University of Lübeck (DE)
Universitätsklinikum Essen
Good health and well-being
Openalex Percentile: Top 88%
Health disparities and outcomes
DOIOpen Access PDFOpenAlex
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