Developing more generalizable prediction models from pooled studies and large clustered data sets.

ABSTRACT: Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor-outcome associations between different settings and populations. This limits the generalizability of developed models (even from large, combined, clustered data sets) and necessitates local revisions. We aim to develop methodology for producing prediction models that require less tailoring to different settings and populations. We adopt internal-external cross-validation to assess and reduce heterogeneity in models' predictive performance duri

SUBMITTER: de Jong VMT 

PROVIDER: S-EPMC8252590 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature