2. Definitions and differences
3. Prediction model development, performance and impact
- •Common pitfalls in diagnostic and prognostic model studies include: incomplete or unclear reporting, use of nonrepresentative data, no internal or external prediction validation, modeling and validation with too low sample size, dichotomization of predictors, data-driven selection of predictors in small sample size settings, and missing data that are ignored.
- •For reporting guidance, risk of bias assessments and checklists for diagnostic and prognostic model studies: see TRIPOD [,and PROBAST [].
- •Developing new clinical prediction models should be avoided if there are relevant existing prediction models already available for the same outcome or target population that can be validated, updated, extended or evaluated for their impact.
Declarations of competing interest
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration.Ann Intern Med. 2015; 162: W1-W73
- Explanation and elaboration paper of TRIPOD, a reporting guideline for diagnostic and prognostic model development and validation studies. Contains many explanations and key references for further reading.
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the tripod statement.Ann Intern Med. 2015; 162: 55
- The TRIPOD statement. More information about TRIPOD, the reporting guideline and reporting checklists are found on: https://www.tripod-statement.org/. Extensions of TRIPOD to reporting of prediction models for clustered data and machine learning/artificial intelligence are currently in development.
- PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration.Ann Intern Med. 2019; 170: W1
- Explanation and elaboration paper of PROBAST, a risk of bias tool for diagnostic and prognostic model development and validation studies. Contains many clues on how to avoid risk of bias and contains key references for further reading.
- Exclusion of deep vein thrombosis using the wells rule in clinically important subgroups: individual patient data meta-analysis.BMJ. 2014; 348: g1340
- Prediction models for cardiovascular disease risk in the general population: systematic review.BMJ. 2016; 353: i2416
- Calibration: the Achilles heel of predictive analytics.BMC Med. 2019; 17: 230
- Prediction model calibration is often ignored in the development and validation of diagnostic and prognostic prediction models. This paper explains why calibration is an essential part of the evaluation of prediction model performance.
- Three myths about risk thresholds for prediction models.BMC Med. 2019; 17: 192
- Describes three common myths about defining risk thresholds, e.g. a cut-off point on the probability scale above which patients are classified as a predicted case, and presents methods to evaluate and select context-dependent thresholds.
- Calculating the sample size required for developing a clinical prediction model.BMJ. 2020; 368: m441
- Presents formulas for the minimal sample size required for developing a diagnostic or prognostic prediction model. Comes with software in R and Stata to simplify calculations.
- Poor performance of clinical prediction models: the harm of commonly applied methods.J Clin Epidemiol. 2018; 98: 133-143
- Illustration the impact of a small sample size and use of common but suboptimal statistical approaches.
- Prognosis and prognostic research: application and impact of prognostic models in clinical practice.BMJ. 2009; 338: b606
- b606Discusses how to determine the practical value of prediction models with focus on prognostic models.
- Towards better clinical prediction models: seven steps for development and an ABCD for validation.Eur Heart J. 2014; 35: 1925-1931
- Describes steps to improve development and validation of clinical prediction models.