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J Clin Epidemiol. 2016 Jan;69:40-50. doi: 10.1016/j.jclinepi.2015.05.009. Epub 2015 May 16.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

Author information

1
Public Health, Epidemiology and Biostatistics, School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
2
School of Mathematics, Watson Building, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
3
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands; Dutch Cochrane Centre, University Medical Center Utrecht, Str. 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands.
4
Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire ST5 5BG, UK.
5
Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands.
6
Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire ST5 5BG, UK. Electronic address: r.riley@keele.ac.uk.

Abstract

OBJECTIVES:

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation.

STUDY DESIGN AND SETTING:

We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality.

RESULTS:

In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance.

CONCLUSION:

Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies.

KEYWORDS:

Calibration; Discrimination; External validation; Heterogeneity; Individual participant data (IPD); Model comparison; Multivariate meta-analysis; Prognostic model; Risk prediction

PMID:
26142114
PMCID:
PMC4688112
DOI:
10.1016/j.jclinepi.2015.05.009
[Indexed for MEDLINE]
Free PMC Article

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