Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration.
Ewout Willem SteyerbergDaan NieboerThomas P A DebrayHans C van HouwelingenPublished in: Statistics in medicine (2019)
Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.
Keyphrases
- end stage renal disease
- traumatic brain injury
- electronic health record
- systematic review
- ejection fraction
- chronic kidney disease
- newly diagnosed
- single cell
- big data
- prognostic factors
- peritoneal dialysis
- randomized controlled trial
- type diabetes
- cardiovascular disease
- case report
- data analysis
- machine learning
- meta analyses
- artificial intelligence