Table 1 Checklist of items to include when reporting a study developing or validating a multivariable prediction model for diagnosis or prognosis^a

Title and abstract

Title

Identify the study as developing and/or validating a multivariable prediction model, the target population, and the outcome to be predicted.

Abstract

Provide a summary of objectives, study design, setting, participants, sample size, predictors, outcome, statistical analysis, results, and conclusions.

Introduction

Background and objectives

Explain the medical context (including whether diagnostic or prognostic) and rationale for developing or validating the multivariable prediction model, including references to existing models.

Specify the objectives, including whether the study describes the development or validation of the model, or both.

Methods

Source of data

Describe the study design or source of data (e.g., randomised trial, cohort, or registry data), separately for the development and validation data sets, if applicable.

Specify the key study dates, including start of accrual; end of accrual; and, if applicable, end of follow-up.

Participants

Specify key elements of the study setting (e.g., primary care, secondary care, general population) including number and ___location of centres.

Describe eligibility criteria for participants.

Give details of treatments received, if relevant.

Outcome

Clearly define the outcome that is predicted by the prediction model, including how and when assessed.

Report any actions to blind assessment of the outcome to be predicted.

Predictors

Clearly define all predictors used in developing the multivariable prediction model, including how and when they were measured.

Report any actions to blind assessment of predictors for the outcome and other predictors.

Sample size

Explain how the study size was arrived at.

Missing data

Describe how missing data were handled (e.g., complete-case analysis, single imputation, multiple imputation) with details of any imputation method.

Statistical analysis methods

Describe how predictors were handled in the analyses.

Specify type of model, all model-building procedures (including any predictor selection), and method for internal validation.

For validation, describe how the predictions were calculated.

Specify all measures used to assess model performance and, if relevant, to compare multiple models.

Describe any model updating (e.g., recalibration) arising from the validation, if done.

Risk groups

Provide details on how risk groups were created, if done.

Development vs validation

For validation, identify any differences from the development data in setting, eligibility criteria, outcome, and predictors.

Results

Participants

Describe the flow of participants through the study, including the number of participants with and without the outcome and, if applicable, a summary of the follow-up time. A diagram may be helpful.

Describe the characteristics of the participants (basic demographics, clinical features, available predictors), including the number of participants with missing data for predictors and outcome.

For validation, show a comparison with the development data of the distribution of important variables (demographics, predictors, and outcome).

Model development

Specify the number of participants and outcome events in each analysis.

If done, report the unadjusted association between each candidate predictor and outcome.

Model specification

Present the full prediction model to allow predictions for individuals (i.e., all regression coefficients, and model intercept or baseline survival at a given time point).

Explain how to use the prediction model.

Model performance

Report performance measures (with CIs) for the prediction model.

Model updating

If done, report the results from any model updating (i.e., model specification, model performance).

Discussion

Limitations

Discuss any limitations of the study (such as nonrepresentative sample, few events per predictor, missing data).

Interpretation

For validation, discuss the results with reference to performance in the development data, and any other validation data.

Give an overall interpretation of the results, considering objectives, limitations, results from similar studies, and other relevant evidence.

Implications

Discuss the potential clinical use of the model and implications for future research

Other information

Supplementary information

Provide information about the availability of supplementary resources, such as study protocol, Web calculator, and data sets.

Funding

Give the source of funding and the role of the funders for the present study.