Prognostic model research
Sample size for developing a prognostic model
The previous paper in the BMJ (2020) provides an overview and summary of the guidance in the following work:
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Minimum sample size for developing a multivariable prediction model: PART I ‐ continuous outcomes (PDF)
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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary & time‐to‐event outcomes (PDF)
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Sample size for binary logistic prediction models: Beyond events per variable criteria (PDF)
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The problems with using a split-sample for model development and validation (blog)
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How Can Machine Learning be Reliable When the Sample is Adequate for Only One Feature? (blog)
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Modern Modelling Techniques Are Data Hungry: A Simulation Study for Predicting Dichotomous Endpoints (PDF)
Videos on sample size for model development available here
"Why the EPV ≥ 10 sample size rule is rubbish and what to use instead" - slides by Dr Maarten van Smeden available here
Sample size for external validation of a prognostic model
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**NEW** Minimum sample size for external validation of a clinical prediction model with a continuous outcome (PDF)
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Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (PDF)
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A calibration hierarchy for risk models was defined: from utopia to empirical data (PDF)
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Substantial effective sample sizes were required for external validation studies of predictive logistic regression models (PDF)
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Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data (PDF)
Videos on sample size for model validation available here
Stages of prognostic model research
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Prognosis and prognostic research: what, why, and how? (PDF)
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Prognosis and prognostic research: developing a prognostic model (PDF)
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Prognosis and prognostic research: Validating a prognostic model (PDF)
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Prognosis and prognostic research: application and impact of prognostic models in clinical practice (PDF)
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Guide to presenting clinical prediction models for use in clinical settings (PDF)
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Presentation of multivariate data for clinical use: The Framingham Study risk score functions (PDF)
Improving prognostic model research
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The basics of prediction modelling, and related topics (slides from Dr Maarten van Smeden)
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Key steps and common pitfalls in developing and validating risk models (PDF)
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Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, ... (PDF)
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Towards better clinical prediction models: seven steps for development and an ABCD for validation (PDF)
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Everything you always wanted to know about evaluating prediction models (but were too afraid to ask) (PDF)
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Variable selection – A review and recommendations for the practicing statistician (PDF)
Notes of caution:
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Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis (PDF)
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Poor performance of clinical prediction models: the harm of commonly applied methods (PDF)
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Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study (PDF)
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Three myths about risk thresholds for prediction models (PDF)
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Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model (PDF)
Video on controversies in prediction modelling using statistical methods and machine learning available here
Video on COVID-19 related prediction models available here
Evaluating the performance of a prognostic model
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A calibration hierarchy for risk models was defined: from utopia to empirical data (PDF)
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Calibration: the Achilles heel of predictive analytics (PDF)
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Internal validation of predictive models: efficiency of some procedures for logistic regression analysis (PDF)
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Prediction models need appropriate internal, internal-external, and external validation (PDF)
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Construction and validation of a prognostic model across several studies ... (PDF)
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Assessment of predictive performance in incomplete data by combining internal validation & multiple imputation (PDF)
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External validation of a Cox prognostic model: principles and methods (PDF)
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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis (PDF)
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Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests (PDF)
Improving prognostic survival models
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Temporal recalibration for improving prognostic model development and risk predictions ... (PDF)
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Prognostic Models With Competing Risks: Methods and Application to Coronary Risk Prediction (PDF)
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Validation, calibration, revision and combination of prognostic survival models (PDF)
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Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model (PDF)