Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.

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Kaptoge, Stephen 
Royston, Patrick 
Sauerbrei, Willi 
Emerging Risk Factors Collaboration 

Non-linear exposure-outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two-stage methods for meta-analysis of such relationships, where the confounder-adjusted relationship is first estimated in a non-linear regression model in each study, then combined across studies. The "metacurve" approach combines the estimated curves using multiple meta-analyses of the relative effect between a given exposure level and a reference level. The "mvmeta" approach combines the estimated model parameters in a single multivariate meta-analysis. Both methods allow the exposure-outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis-specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all-cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study-specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study-specific powers does not. For all-cause mortality, all methods identify a steep U-shape. The metacurve and mvmeta methods perform well in combining complex exposure-disease relationships across studies.

fractional polynomials, meta-analysis, multivariate meta-analysis, prognostic research, random effects models, Body Mass Index, Coronary Disease, Female, Humans, Male, Meta-Analysis as Topic, Middle Aged, Models, Statistical, Mortality, Nonlinear Dynamics, Risk Factors
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Stat Med
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British Heart Foundation (None)
Cambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
British Heart Foundation (CH/12/2/29428)
Medical Research Council (MR/L003120/1)
Medical Research Council (G0800270)