Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

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.

Development and validation of risk prediction model for venous thromboembolism in postpartum women: multinational cohort study

“…We repeated the model development process in each bootstrap sample (as outlined above, including variable selection) to produce a model, applied the model to the same bootstrap sample to quantify apparent performance, and applied the model to the original dataset to test model performance (calibration slope and C statistic) and optimism (difference in test performance and apparent performance). We then estimated the overall optimism across all models (for example, derive shrinkage coefficient=average calibration slope from each of the bootstrap samples) 25. To account for over-fitting during the development process, we multiplied the original β coefficients by the uniform shrinkage factor in the final model.…”

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

“…We then repeated this entire process 50 times and averaged the C‐statistic estimates to derive an optimism‐corrected C‐statistic. We qualitatively assessed calibration by comparing observed to predicted probabilities of readmission by quintiles of predicted risk, and with the Hosmer‐Lemeshow goodness‐of‐fit test 21, 22…”

Large Study But Weak Test of Internal Validation: Comment on the Article by Solomon et al

“…For large data sets like the Consortium of Rheumatology Researchers of North America registry, random splitting merely creates 2 identical data sets (as observed in Solomon and colleagues' study), and therefore, evaluating the performance of the model on the validation cohort will unsurprisingly yield performance measures similar to those obtained on the development cohort-hardly a strong test of the risk score. An alternative and stronger approach when a large data set is available is to split geographically or temporally; this approach can be considered an intermediate step between internal and external validation (3,4).…”

“…A key characteristic of model performance that should be assessed and reported is calibration, as recommended in the recent TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement (3,4). Calibration is the agreement between outcome predictions from the model and the observed outcomes.…”

“…Solomon et al evaluated calibration by calculating the Hosmer-Lemeshow test, which is widely known as being affected by sample size and uninformative in that it provides no indication of the magnitude or direction of (mis)calibration. In accordance with recent recommendations on the reporting of prediction model studies (3,4), calibration should be assessed graphically by plotting predicted outcome probabilities (x-axis) against observed outcomes (y-axis) using a high-resolution smoothed (lowess) line. …”