1. Methods for meta-analysis of individual participant data from Mendelian randomisation studies with binary outcomes.
- Author
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Burgess, Stephen, Thompson, Simon G., and CRP CHD Genetics Collaboration
- Subjects
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META-analysis , *C-reactive protein , *RANDOMIZED controlled trials , *TREATMENT effectiveness , *BAYESIAN analysis , *MEDICAL statistics , *ATTRIBUTION (Social psychology) , *COMPARATIVE studies , *CORONARY disease , *GENETIC polymorphisms , *LONGITUDINAL method , *RESEARCH methodology , *MEDICAL cooperation , *PROBABILITY theory , *RESEARCH , *RESEARCH funding , *STATISTICS , *SURVIVAL analysis (Biometry) , *EVALUATION research , *CROSS-sectional method , *PROPORTIONAL hazards models , *CASE-control method , *STATISTICAL models - Abstract
Mendelian randomisation is an epidemiological method for estimating causal associations from observational data by using genetic variants as instrumental variables. Typically the genetic variants explain only a small proportion of the variation in the risk factor of interest, and so large sample sizes are required, necessitating data from multiple sources. Meta-analysis based on individual patient data requires synthesis of studies which differ in many aspects. A proposed Bayesian framework is able to estimate a causal effect from each study, and combine these using a hierarchical model. The method is illustrated for data on C-reactive protein and coronary heart disease (CHD) from the C-reactive protein CHD Genetics Collaboration (CCGC). Studies from the CCGC differ in terms of the genetic variants measured, the study design (prospective or retrospective, population-based or case-control), whether C-reactive protein was measured, the time of C-reactive protein measurement (pre- or post-disease), and whether full or tabular data were shared. We show how these data can be combined in an efficient way to give a single estimate of causal association based on the totality of the data available. Compared to a two-stage analysis, the Bayesian method is able to incorporate data on 23% additional participants and 51% more events, leading to a 23-26% gain in efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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