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

Daniel Jakson; Ian White; J. B. Kostis; A. C. Wilson; A. R. Folsom; K. Wu; L. Chambless; U. Benderley; U. Goldbourt; J. Willeit; S. Kiechl; J. W G Yarnell; P. M. Sweetnam; P. C. Elmwood; M. Cushman; B. M. Psaty; R. P. Tracy; A. Tybjærg-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. Wilhemsen; 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. Copper; 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; T. Palosou; 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; A. Rudnicka; P. Brennan; P. Ridker; F. Rodeghiero; A. Tosetto; J. Shepherd; D. O. Lowe; I. Ford; M. Robertson; E. Brunner; M. Shipley; E. J M Fesken; E. Di Angelantonio

doi:10.1002/sim.3540

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

Daniel Jakson, Ian White, J. B. Kostis, A. C. Wilson, A. R. Folsom, K. Wu, L. Chambless, U. Benderley, U. Goldbourt, J. Willeit, S. Kiechl, J. W G Yarnell, P. M. Sweetnam, P. C. Elmwood, M. Cushman, B. M. Psaty, R. P. Tracy, A. Tybjærg-Hansen, F. Haverkate, M. P M de MaatS. 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. Wilhemsen, 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. Copper, 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, T. Palosou, 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, A. Rudnicka, P. Brennan, P. Ridker, F. Rodeghiero, A. Tosetto, J. Shepherd, D. O. Lowe, I. Ford, M. Robertson, E. Brunner, M. Shipley, E. J M Fesken, E. Di Angelantonio

Epidemiology & Community Health

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English (US)
Pages (from-to)	1218-1237
Number of pages	20
Journal	Statistics in Medicine
Volume	28
Issue number	8
DOIs	https://doi.org/10.1002/sim.3540
State	Published - Apr 15 2009

Keywords

Confounders
Meta-analysis
Missing covariates
Observational studies
Survival analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1002/sim.3540

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Jakson, D., White, I., Kostis, J. B., Wilson, A. C., Folsom, A. R., Wu, K., Chambless, L., Benderley, U., Goldbourt, U., Willeit, J., Kiechl, S., Yarnell, J. W. G., Sweetnam, P. M., Elmwood, P. C., Cushman, M., Psaty, B. M., Tracy, R. P., Tybjærg-Hansen, A., Haverkate, F., ... Di Angelantonio, E. (2009). Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Statistics in Medicine, 28(8), 1218-1237. https://doi.org/10.1002/sim.3540

Jakson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderley, U, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JWG, Sweetnam, PM, Elmwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjærg-Hansen, A, Haverkate, F, de Maat, MPM, Thompson, SG, Fowkes, FGR, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, V, Vahtera, E, Jousilahti, P, D'Agostino, R, Kannel, WB, Wilson, PWF, Tofler, G, Levy, D, Marchioli, R, Valagussa, F, Rosengren, A, Wilhemsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Engström, G, Berglund, G, Loewel, H, Koenig, W, Hense, HW, Meade, TW, Copper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Cooper, JA, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosou, 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, GDO, Woodward, M, Ben-Shlomo, Y, Smith, GD, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Lowe, DO, Ford, I, Robertson, M, Brunner, E, Shipley, M, Fesken, EJM & Di Angelantonio, E 2009, 'Systematically missing confounders in individual participant data meta-analysis of observational cohort studies', Statistics in Medicine, vol. 28, no. 8, pp. 1218-1237. https://doi.org/10.1002/sim.3540

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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.",

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year = "2009",

month = apr,

day = "15",

doi = "10.1002/sim.3540",

language = "English (US)",

volume = "28",

pages = "1218--1237",

journal = "Statistics in Medicine",

issn = "0277-6715",

publisher = "John Wiley and Sons Ltd",

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TY - JOUR

T1 - Systematically missing confounders in individual participant data meta-analysis of observational cohort studies

AU - Jakson, Daniel

AU - White, Ian

AU - Kostis, J. B.

AU - Wilson, A. C.

AU - Folsom, A. R.

AU - Wu, K.

AU - Chambless, L.

AU - Benderley, U.

AU - Goldbourt, U.

AU - Willeit, J.

AU - Kiechl, S.

AU - Yarnell, J. W G

AU - Sweetnam, P. M.

AU - Elmwood, P. C.

AU - Cushman, M.

AU - Psaty, B. M.

AU - Tracy, R. P.

AU - Tybjærg-Hansen, A.

AU - Haverkate, F.

AU - de Maat, M. P M

AU - Thompson, S. G.

AU - Fowkes, F. G R

AU - Lee, A. J.

AU - Smith, F. B.

AU - Salomaa, V.

AU - Harald, K.

AU - Rasi, V.

AU - Vahtera, E.

AU - Jousilahti, P.

AU - D'Agostino, R.

AU - Kannel, W. B.

AU - Wilson, P. W F

AU - Tofler, G.

AU - Levy, D.

AU - Marchioli, R.

AU - Valagussa, F.

AU - Rosengren, A.

AU - Wilhemsen, L.

AU - Lappas, G.

AU - Eriksson, H.

AU - Cremer, P.

AU - Nagel, D.

AU - Curb, J. D.

AU - Rodriguez, B.

AU - Yano, K.

AU - Salonen, J. T.

AU - Nyyssönen, K.

AU - Tuomainen, T. P.

AU - Hedblad, B.

AU - Engström, G.

AU - Berglund, G.

AU - Loewel, H.

AU - Koenig, W.

AU - Hense, H. W.

AU - Meade, T. W.

AU - Copper, J. A.

AU - De Stavola, B.

AU - Knottenbelt, C.

AU - Miller, G. J.

AU - Cooper, J. A.

AU - Bauer, K. A.

AU - Rosenberg, R. D.

AU - Sato, S.

AU - Kitamura, A.

AU - Naito, Y.

AU - Iso, H.

AU - Palosou, T.

AU - Ducimetiere, P.

AU - Amouyel, P.

AU - Arveiler, D.

AU - Evans, A. E.

AU - Ferrieres, J.

AU - Juhan-Vague, I.

AU - Bingham, A.

AU - Schulte, H.

AU - Assmann, G.

AU - Cantin, B.

AU - Lamarche, B.

AU - Despres, J. P.

AU - Dagenais, G. R.

AU - Tunstall-Pedoe, H.

AU - Lowe, G. D O

AU - Woodward, M.

AU - Ben-Shlomo, Y.

AU - Smith, G. Davey

AU - Palmieri, V.

AU - Yeh, J. L.

AU - Rudnicka, A.

AU - Brennan, P.

AU - Ridker, P.

AU - Rodeghiero, F.

AU - Tosetto, A.

AU - Shepherd, J.

AU - Lowe, D. O.

AU - Ford, I.

AU - Robertson, M.

AU - Brunner, E.

AU - Shipley, M.

AU - Fesken, E. J M

AU - Di Angelantonio, E.

PY - 2009/4/15

Y1 - 2009/4/15

N2 - 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.

AB - 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.

KW - Confounders

KW - Meta-analysis

KW - Missing covariates

KW - Observational studies

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=65649135034&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=65649135034&partnerID=8YFLogxK

U2 - 10.1002/sim.3540

DO - 10.1002/sim.3540

M3 - Article

C2 - 19222087

AN - SCOPUS:65649135034

SN - 0277-6715

VL - 28

SP - 1218

EP - 1237

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 8

ER -

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

Abstract

Keywords

UN SDGs

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