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Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.

Fibrinogen Studies Collaboration, ; Jackson, D; White, I; Kostis, JB; Wilson, AC; Folsom, AR; Wu, K; Chambless, L; Benderly, M; Goldbourt, U; et al. Fibrinogen Studies Collaboration; Jackson, D; White, I; Kostis, JB; Wilson, AC; Folsom, AR; Wu, K; Chambless, L; Benderly, M; Goldbourt, U; Willeit, J; Kiechl, S; Yarnell, JW; Sweetnam, PM; Elwood, PC; Cushman, M; Psaty, BM; Tracy, RP; Tybjaerg-Hansen, A; Haverkate, F; de Maat, MP; Thompson, SG; Fowkes, FG; Lee, AJ; Smith, FB; Salomaa, V; Harald, K; Rasi, V; Vahtera, E; Jousilahti, P; D'Agostino, R; Kannel, WB; Wilson, PW; Tofler, G; Levy, D; Marchioli, R; Valagussa, F; Rosengren, A; Wilhelmsen, L; Lappas, G; Eriksson, H; Cremer, P; Nagel, D; Curb, JD; Rodriguez, B; Yano, K; Salonen, J; Nyyssönen, K; Tuomainen, TP; Hedblad, B; Engström, G; Berglund, G; Loewel, H; Koenig, W; Hense, HW; Meade, TW; Cooper, JA; De Stavola, B; Knottenbelt, C; Miller, GJ; Cooper, JA; Bauer, KA; Rosenberg, RD; Sato, S; Kitamura, A; Naito, Y; Iso, H; Salomaa, V; Harald, K; Rasi, V; Vahtera, E; Jousilahti, P; Palosuo, T; Ducimetiere, P; Amouyel, P; Arveiler, D; Evans, AE; Ferrieres, J; Juhan-Vague, I; Bingham, A; Schulte, H; Assmann, G; Cantin, B; Lamarche, B; Despres, JP; Dagenais, GR; Tunstall-Pedoe, H; Lowe, GD; Woodward, M; Ben-Shlomo, Y; Davey Smith, G; Palmieri, V; Yeh, JL; Meade, TW; Rudnicka, A; Brennan, P; Knottenbelt, C; Cooper, JA; Ridker, P; Rodeghiero, F; Tosetto, A; Shepherd, J; Lowe, GD; Ford, I; Robertson, M; Brunner, E; Shipley, M; Feskens, EJ; Di Angelantonio, E; Kaptoge, S; Lewington, S; Lowe, GD; Sarwar, N; Thompson, SG; Walker, M; Watson, S; White, IR; Wood, AM; Danesh, J (2009) Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Stat Med, 28 (8). 1218 - 1237. ISSN 0277-6715 https://doi.org/10.1002/sim.3540
SGUL Authors: Rudnicka, Alicja Regina

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Abstract

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts

Item Type: Article
Additional Information: PMCID: PMC2922684
Keywords: Cohort Studies, Computer Simulation, Coronary Disease, Data Interpretation, Statistical, Female, Fibrinogen, Humans, Male, Meta-Analysis as Topic, Models, Statistical
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: Stat Med
ISSN: 0277-6715
Dates:
DateEvent
15 April 2009Published
PubMed ID: 19222087
Web of Science ID: 19222087
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URI: http://openaccess.sgul.ac.uk/id/eprint/1507
Publisher's version: https://doi.org/10.1002/sim.3540

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