Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

Simon Thompson, Stephen Kaptoge, Ian White, Angela Wood, Philip Perry, John Danesh, The Emerging Risk Factors Collaboration, Amanda Lee

Research output: Contribution to journalArticle

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Abstract

Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure–risk relationships, but involves a number of analytical challenges.
Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes.
Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure–risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure–risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available.
Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses.
Original languageEnglish
Pages (from-to)1345-1359
Number of pages15
JournalInternational Journal of Epidemiology
Volume39
Issue number5
Early online date3 May 2010
DOIs
Publication statusPublished - Oct 2010

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Meta-Analysis
Epidemiologic Studies
Prospective Studies
Proportional Hazards Models
Fibrinogen
Coronary Disease
Cardiovascular Diseases

Keywords

  • meta-analysis
  • epidemiological studies
  • individual participant data
  • statistical methods
  • survival analysis

Cite this

Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. / Thompson, Simon; Kaptoge, Stephen; White, Ian; Wood, Angela; Perry, Philip; Danesh, John; The Emerging Risk Factors Collaboration ; Lee, Amanda.

In: International Journal of Epidemiology, Vol. 39, No. 5, 10.2010, p. 1345-1359.

Research output: Contribution to journalArticle

Thompson, S, Kaptoge, S, White, I, Wood, A, Perry, P, Danesh, J, The Emerging Risk Factors Collaboration & Lee, A 2010, 'Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies', International Journal of Epidemiology, vol. 39, no. 5, pp. 1345-1359. https://doi.org/10.1093/ije/dyq063
Thompson, Simon ; Kaptoge, Stephen ; White, Ian ; Wood, Angela ; Perry, Philip ; Danesh, John ; The Emerging Risk Factors Collaboration ; Lee, Amanda. / Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. In: International Journal of Epidemiology. 2010 ; Vol. 39, No. 5. pp. 1345-1359.
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abstract = "Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure–risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure–risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure–risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses.",
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AB - Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure–risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure–risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure–risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses.

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