Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
2015/01/05 by Karel G.M. Moons, Douglas G. Altman, Johannes B. Reitsma +8 · 78 citations
Decision Sciences · Health Professions · Economics, Econometrics and Finance · #Meta-analysis and systematic reviews #Healthcare cost, quality, practices #Health Systems, Economic Evaluations, Quality of Life
paper · doi:10.7326/m14-0698
Abstract
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
Citations
Cited by
- PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
- Addressing Missingness in Predictive Models That Use Electronic Health Record Data
- Development and Validation of the Summary Elixhauser Comorbidity Score for Use With ICD-10-CM–Coded Data Among Older Adults
- Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
- Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
- Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population
- Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement
- Waste, Leaks, and Failures in the Biomarker Pipeline
- Nomogram as a novel predictive tool for lymph node metastasis in T1 colorectal cancer treated with endoscopic resection: a nationwide, multicenter study
- Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board
- Five critical quality criteria for artificial intelligence-based prediction models
- Evidence of unreliable data and poor data provenance in clinical prediction model research and clinical practice
- Calibration: the Achilles heel of predictive analytics
- FRAMR-EMR: Framework for Prognostic Predictive Model Development Using\n Electronic Medical Record Data with a Case Study in Osteoarthritis Risk
- Sample size considerations for the external validation of a multivariable prognostic model: a resampling study
- Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
- Radiomics in Cancer Radiotherapy: a Review
- Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
- The impact of population-wide lifestyle modifications on improving life expectancy in the Chinese population: a simulation study
- Beyond the binary: integrating “real-world evidence” with randomized trials in contemporary health care
- Development and validation of a nomogram for predicting high‐burnout risk in nurses
- Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care
- Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
- Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement
- Predicting postoperative delirium severity in older adults: The role of surgical risk and executive function
- Designing a quasi-experiment to study the clinical impact of adaptive risk prediction models
- Prognostic models predicting clinical outcomes in patients diagnosed with visceral leishmaniasis: a systematic review
- Regression without regrets –initial data analysis is a prerequisite for multivariable regression
- Prediction models need appropriate internal, internal–external, and external validation
- Current state and prospects of artificial intelligence in allergy
- Improving Science That Uses Code
- Association between fat and fat-free body mass indices on shock attenuation during running
- Serum identification of at-risk MASH: The metabolomics-advanced steatohepatitis fibrosis score (MASEF)
- Sample size for binary logistic prediction models: Beyond events per variable criteria
- Machine learning for endoscopic third ventriculostomy success prediction—a systematic review and meta-analysis
- Can machine learning models predict oocyte yield during assisted conception?: a systematic review
- The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
- A Point-of-Care Prediction Tool for Recurrent Tuberculosis
- Prediction meets causal inference: the role of treatment in clinical prediction models
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
- There is no such thing as a validated prediction model
- Write your abstracts carefully—The impact of abstract reporting quality on findability by semi-automated title-abstract screening tools
- Refining adjuvant radiation therapy decision using a nomogram in major salivary gland carcinoma
- Predicting type 2 diabetes and testosterone effects in high-risk Australian men: development and external validation of a 2-year risk model
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
- Minimum Sample Size Calculation for Multivariable Regression of Continuous Outcomes in Chemometrics for Astrobiology and Planetary Science
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review
- Do Birthweight‐For‐Gestational Age Centiles Predict Serious Neonatal Morbidity and Neonatal Mortality?
- External validation of prognostic models: what, why, how, when and where?
- Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare
- Nested Case-Control and Case-Cohort: Efficient Study Designs to Develop Biomarker-Based Prediction Models for Rare Outcomes
- Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury
- Calculating the sample size required for developing a clinical prediction model
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. [europepmc]
- Prediction models need appropriate internal, internal-external, and external validation. [europepmc]
- Prediction models for cardiovascular disease risk in the general population: systematic review. [europepmc]
- External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. [europepmc]
- No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. [europepmc]
- Adaptation and Validation of a Pediatric Sequential Organ Failure Assessment Score and Evaluation of the Sepsis-3 Definitions in Critically Ill Children. [europepmc]
- A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. [europepmc]
- Sample size for binary logistic prediction models: Beyond events per variable criteria. [europepmc]
- Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. [europepmc]
- A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. [europepmc]
- Emergency department triage prediction of clinical outcomes using machine learning models. [europepmc]
- Evaluating a New International Risk-Prediction Tool in IgA Nephropathy. [europepmc]
- Calibration: the Achilles heel of predictive analytics. [europepmc]
- FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study. [europepmc]
- Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. [europepmc]
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. [europepmc]
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal [europepmc]
- Artificial Intelligence in Dentistry: Chances and Challenges. [europepmc]
- Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. [europepmc]
- External validation of prognostic models: what, why, how, when and where? [europepmc]
- Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis. [europepmc]
- Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. [europepmc]
- Evaluation of clinical prediction models (part 1): from development to external validation. [europepmc]
- Metrics reloaded: recommendations for image analysis validation. [europepmc]
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. [europepmc]
Related