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. 2009 Apr 15;28(8):1218-37.
doi: 10.1002/sim.3540.

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

Fibrinogen Studies Collaboration; Dan Jackson, Ian White, J B Kostis, A C Wilson, A R Folsom, K Wu, L Chambless, M Benderly, U Goldbourt, J Willeit, S Kiechl, J W G Yarnell, P M Sweetnam, P C Elwood, M Cushman, B M Psaty, R P Tracy, A Tybjaerg-Hansen, F Haverkate, M P M de Maat, S G Thompson, F G R Fowkes, A J Lee, F B Smith, V Salomaa, K Harald, V Rasi, E Vahtera, P Jousilahti, R D'Agostino, W B Kannel, P W F Wilson, G Tofler, D Levy, R Marchioli, F Valagussa, A Rosengren, L Wilhelmsen, G Lappas, H Eriksson, P Cremer, D Nagel, J D Curb, B Rodriguez, K Yano, J T Salonen, K Nyyssönen, T-P Tuomainen, B Hedblad, G Engström, G Berglund, H Loewel, W Koenig, H W Hense, T W Meade, J A Cooper, B De Stavola, C Knottenbelt, G J Miller, J A Cooper, K A Bauer, R D Rosenberg, S Sato, A Kitamura, Y Naito, H Iso, V Salomaa, K Harald, V Rasi, E Vahtera, P Jousilahti, T Palosuo, P Ducimetiere, P Amouyel, D Arveiler, A E Evans, J Ferrieres, I Juhan-Vague, A Bingham, H Schulte, G Assmann, B Cantin, B Lamarche, J-P Despres, G R Dagenais, H Tunstall-Pedoe, G D O Lowe, M Woodward, Y Ben-Shlomo, G Davey Smith, V Palmieri, J L Yeh, T W Meade, A Rudnicka, P Brennan, C Knottenbelt, J A Cooper, P Ridker, F Rodeghiero, A Tosetto, J Shepherd, G D O Lowe, I Ford, M Robertson, E Brunner, M Shipley, E J M Feskens, E Di Angelantonio, S Kaptoge, S Lewington, G D O Lowe, N Sarwar, S G Thompson, M Walker, S Watson, I R White, A M Wood, J Danesh
Collaborators
Free PMC article

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

Fibrinogen Studies Collaboration et al. Stat Med. .
Free PMC article

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

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Figures

Figure 1
Figure 1
Fully and partially adjusted estimated effects of fibrinogen level and corresponding 95 per cent confidence intervals, using the bootstrap within-cohort correlations. The line of equality is also shown.
Figure 2
Figure 2
Profile log-likelihood plot for βf using the analytic within-cohort correlations, shown in column 7 of Table I.
Figure 3
Figure 3
Profile log-likelihood plot for κ using the analytic within-cohort correlations, shown in column 7 of Table I.
Figure 4
Figure 4
The difference between fully and partially adjusted, and partially adjusted, estimated effects of fibrinogen level and corresponding 95 per cent confidence intervals. Note that the partially adjusted estimates shown here adjust for total cholesterol, and hence are not quite the same as those shown in Figure 1 or Table I.
See this image and copyright information in PMC

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