• The Cochrane Prognosis Methods Group

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Systematic review guidance and tools:

• A guide to systematic reviews and meta-analysis of prediction model performance (PDF)

• A guide to systematic reviews and meta-analysis of prognostic factor studies (PDF)

• CHARMS: Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prediction modelling studies (PDF)

• CHARMS-PF: Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prognostic factor studies (PDF)

• PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies (PDF & explanation document)

• Use of GRADE for the assessment of evidence about prognostic factors: rating certainty in ... (PDF)

• GRADE concept paper 2: Concepts for judging certainty on the calibration of prognostic models in a body of validation studies (PDF)

• Evidence synthesis in prognosis research (PDF)

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Meta-analysis to develop and validate prognostic models:

• External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis (PDF)

• A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (PDF)

• A framework for developing, implementing, and evaluating clinical prediction models in an IPD meta-analysis (PDF)

• Assessment of heterogeneity in an IPD meta-analysis of prediction models (PDF)

• Assessing risk prediction models using individual participant data from multiple studies (PDF)

• IPD Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use (PDF)

• Random‐effects meta‐analysis of the clinical utility of tests and prediction models (PDF)

• Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research (new textbook) - details here

• Developing more generalizable prediction models from pooled studies and large clustered data sets (PDF)

• IPD meta‐analysis for external validation, recalibration, and updating of a flexible parametric prognostic model (PDF)

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Meta-analysis to examine predictors of treatment effect:

• IPD meta‐analysis to examine interactions between treatment effect and participant‐level covariates (PDF)

• Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach? (PDF)

• Simulation-based power calculations for planning a two-stage IPD meta-analysis (PDF)

• One‐stage IPD meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias ... (PDF)

• Meta-analysis of continuous outcomes: using pseudo IPD ... to ... assess treatment-by-baseline modification (PDF)

• Investigating treatment-effect modification by a continuous covariate in IPD meta-analysis: an approach using fractional polynomials (PDF)