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

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; R D Rosenberg; E Brunner; Fibrinogen Studies Collaboration

doi:10.1002/sim.3540

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

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, R D Rosenberg, E Brunner, Fibrinogen Studies Collaboration

48 Citationer (Scopus)

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

Originalsprog	Engelsk
Tidsskrift	Statistics in Medicine
Vol/bind	28
Udgave nummer	8
Sider (fra-til)	1218-37
Antal sider	20
ISSN	0277-6715
DOI	https://doi.org/10.1002/sim.3540
Status	Udgivet - 15 apr. 2009
Udgivet eksternt	Ja

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10.1002/sim.3540

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Jackson, D., White, I., Kostis, J. B., Wilson, A. C., Folsom, A. R., Wu, K., Chambless, L., Benderly, M., Goldbourt, U., Willeit, J., Kiechl, S., Yarnell, J. W. G., Sweetnam, P. M., Elwood, P. C., Cushman, M., Psaty, B. M., Tracy, R. P., Tybjaerg-Hansen, A., Haverkate, F., ... Fibrinogen Studies Collaboration (2009). Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Statistics in Medicine, 28(8), 1218-37. https://doi.org/10.1002/sim.3540

Jackson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JWG, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-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, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssönen, K, Tuomainen, T-P, Rosenberg, RD, Brunner, E & Fibrinogen Studies Collaboration 2009, 'Systematically missing confounders in individual participant data meta-analysis of observational cohort studies', Statistics in Medicine, bind 28, nr. 8, s. 1218-37. https://doi.org/10.1002/sim.3540

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title = "Systematically missing confounders in individual participant data meta-analysis of observational cohort studies",

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",

keywords = "Cohort Studies, Computer Simulation, Coronary Disease/metabolism, Data Interpretation, Statistical, Female, Fibrinogen/analysis, Humans, Male, Meta-Analysis as Topic, Models, Statistical",

author = "Dan Jackson and Ian White and Kostis, \{J B\} and Wilson, \{A C\} and Folsom, \{A R\} and K Wu and L Chambless and M Benderly and U Goldbourt and J Willeit and S Kiechl and Yarnell, \{J W G\} and Sweetnam, \{P M\} and Elwood, \{P C\} and M Cushman and Psaty, \{B M\} and Tracy, \{R P\} and A Tybjaerg-Hansen and F Haverkate and \{de Maat\}, \{M P M\} and Thompson, \{S G\} and Fowkes, \{F G R\} and Lee, \{A J\} and Smith, \{F B\} and V Salomaa and K Harald and V Rasi and E Vahtera and P Jousilahti and R D'Agostino and Kannel, \{W B\} and Wilson, \{P W F\} and G Tofler and D Levy and R Marchioli and F Valagussa and A Rosengren and L Wilhelmsen and G Lappas and H Eriksson and P Cremer and D Nagel and Curb, \{J D\} and B Rodriguez and K Yano and Salonen, \{J T\} and K Nyyss{\"o}nen and T-P Tuomainen and Rosenberg, \{R D\} and E Brunner and \{Fibrinogen Studies Collaboration\}",

year = "2009",

month = apr,

day = "15",

doi = "10.1002/sim.3540",

language = "English",

volume = "28",

pages = "1218--37",

journal = "Statistics in Medicine",

issn = "0277-6715",

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TY - JOUR

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

AU - Jackson, Dan

AU - White, Ian

AU - Kostis, J B

AU - Wilson, A C

AU - Folsom, A R

AU - Wu, K

AU - Chambless, L

AU - Benderly, M

AU - Goldbourt, U

AU - Willeit, J

AU - Kiechl, S

AU - Yarnell, J W G

AU - Sweetnam, P M

AU - Elwood, P C

AU - Cushman, M

AU - Psaty, B M

AU - Tracy, R P

AU - Tybjaerg-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 - Wilhelmsen, 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 - Rosenberg, R D

AU - Brunner, E

AU - Fibrinogen Studies Collaboration

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 - Cohort Studies

KW - Computer Simulation

KW - Coronary Disease/metabolism

KW - Data Interpretation, Statistical

KW - Female

KW - Fibrinogen/analysis

KW - Humans

KW - Male

KW - Meta-Analysis as Topic

KW - Models, Statistical

U2 - 10.1002/sim.3540

DO - 10.1002/sim.3540

M3 - Review

C2 - 19222087

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

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