SynthesisBMC medical research methodology2014
Developing and validating risk prediction models in an individual participant data meta-analysis.
Synthesis in BMC medical research methodology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 11 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2026Pooled it
- Psychological interventions for preventing relapse in individuals with partial remission of depression: a systematic review and individual participant data meta-analysis.Psychological medicine · 2025Pooled it
- Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis.The American journal of tropical medicine and hygiene · 2024Pooled it
- Pooled it
- Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Clinical utility of WHO-recommended screening tools and development and validation of novel clinical prediction models for pulmonary tuberculosis screening among outpatients living with HIV: an individual participant data meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Association between maternal adiposity measures and infant health outcomes: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2022Pooled it
- Association between maternal adiposity measures and adverse maternal outcomes of pregnancy: Systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2022Pooled it
- Developing more generalizable prediction models from pooled studies and large clustered data sets.Statistics in medicine · 2021Pooled it
- Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis.Frontiers in psychiatry · 2019Pooled it
- Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis.British journal of cancer · 2016Pooled it
- The Impact of the Number and the Size of Clusters on Prediction Performance of the Stratified and the Conditional Shared Gamma Frailty Cox Proportional Hazards Models.Biometrical journal. Biometrische Zeitschrift · 2026Article
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Development and validation of the CARE-DM model to predict the cardiovascular risk in older persons with type 2 diabetes.European journal of preventive cardiology · 2026Article
- Five essential features for adoption of clinical risk prediction tools: Insights from the VOCAL-Penn score.Hepatology communications · 2025Article
- Clinical prediction model for transition to psychosis in individuals meeting At Risk Mental State criteria.Schizophrenia (Heidelberg, Germany) · 2025Article
- Maternal near-miss prediction model development in Bahir Dar city administration, Northwest Ethiopia.PloS one · 2025Article
- The Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews.The breast journal · 2024Review
- Study of How Adiposity in Pregnancy has an Effect on outcomeS (SHAPES): protocol for a prospective cohort study.BMJ open · 2023Article
- Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.BMJ (Clinical research ed.) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundRisk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach.
methodsA qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies.
resultsThe IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model's applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study.
conclusionsAn IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using 'internal-external cross-validation' to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.