AI Article Synopsis
- The study aims to enhance meta-analysis methods for assessing prediction model performance using individual participant data from various studies.
- The authors propose a multivariate meta-analysis technique to jointly evaluate calibration and discrimination, considering their correlation, and allowing comparisons of different strategies like recalibration.
- Results indicate that, while calibration performance is generally excellent on average, it varies significantly across populations, emphasizing the importance of recalibration for improved predictive accuracy.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688112 | PMC |
| http://dx.doi.org/10.1016/j.jclinepi.2015.05.009 | DOI Listing |
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