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Assessing risk prediction models using individual participant data from multiple studies.

Lisa Pennells, Stephen Kaptoge, Ian R White

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    |December 25, 2013
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    Summary
    This summary is machine-generated.

    Combining data from multiple studies enhances coronary heart disease risk prediction. Meta-analysis of individual participant data, using event counts for weighting, improves risk model accuracy and consistency across diverse cohorts.

    Keywords:
    C indexD measurecoronary heart diseaseindividual participant datainverse variancemeta-analysisrisk predictionweighting

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Cardiovascular Disease Research

    Background:

    • Individual participant data from multiple prospective studies offer robust insights into risk model predictive capabilities.
    • Combining data across studies presents methodological challenges for accurate risk prediction modeling.

    Purpose of the Study:

    • To address challenges in combining time-to-event data from multiple prospective epidemiologic studies.
    • To exemplify methods for deriving and combining risk prediction models across studies, focusing on coronary heart disease.
    • To compare different weighting approaches for meta-analysis of risk estimates.

    Main Methods:

    • Utilized individual participant time-to-event data from the Emerging Risk Factors Collaboration.
    • Developed risk prediction models using Cox proportional hazards regression, stratified by study.
    • Calculated risk discrimination measures (concordance index, Royston's discrimination measure) within studies.
    • Combined estimates across studies using weighted meta-analysis, recommending weighting by the number of events.

    Main Results:

    • Weighted meta-analysis effectively combined risk estimates across studies.
    • Weighting by the number of events is recommended for meta-analysis.
    • Increments in the concordance index from adding log C-reactive protein were homogeneous across studies, despite overall heterogeneity.
    • Comparison of predictive ability differences across subgroups should rely on within-study information only.

    Conclusions:

    • Combining individual participant data through meta-analysis is a valid approach for enhancing risk prediction models.
    • The number of events is a suitable weight for combining risk estimates across studies.
    • Log C-reactive protein consistently improves risk prediction for coronary heart disease across diverse cohorts.