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

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

SUBMITTER: Snell KI 

PROVIDER: S-EPMC4688112 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature