Personalised approaches to antithrombotic therapies : insights from linked electronic health records

2 D;V Provide a summary of objectives, study design, setting, participants, sample size, predictors, outcome, statistical analysis, results, and conclusions. Introduction Background and objectives 3a D;V Explain the medical context (including whether diagnostic or prognostic) and rationale for developing or validating the multivariable prediction model, including references to existing models. 3b D;V Specify the objectives, including whether the study describes the development or validation of the model or both. Methods Source of data 4a D;V Describe the study design or source of data (e.g., randomized trial, cohort, or registry data), separately for the development and validation data sets, if applicable. 4b D;V Specify the key study dates, including start of accrual; end of accrual; and, if applicable, end of follow-up. Participants 5a D;V Specify key elements of the study setting (e.g., primary care, secondary care, general population) including number and location of centres. 5b D;V Describe eligibility criteria for participants. 5c D;V Give details of treatments received, if relevant. Outcome 6a D;V Clearly define the outcome that is predicted by the prediction model, including how and when assessed. 6b D;V Report any actions to blind assessment of the outcome to be predicted. Predictors 7a D;V Clearly define all predictors used in developing or validating the multivariable prediction model, including how and when they were measured. 7b D;V Report any actions to blind assessment of predictors for the outcome and other predictors. Sample size 8 D;V Explain how the study size was arrived at. Missing data 9 D;V Describe how missing data were handled (e.g., complete-case analysis, single imputation, multiple imputation) with details of any imputation method. Statistical analysis methods 10a D Describe how predictors were handled in the analyses. 10b D Specify type of model, all model-building procedures (including any predictor selection), and method for internal validation. 10c V For validation, describe how the predictions were calculated. 10d D;V Specify all measures used to assess model performance and, if relevant, to compare multiple models. 10e V Describe any model updating (e.g., recalibration) arising from the validation, if done. Risk groups 11 D;V Provide details on how risk groups were created, if done. Development vs. validation 12 V For validation, identify any differences from the development data in setting, eligibility criteria, outcome, and predictors. Results Participants 13a D;V 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. 13b D;V Describe the characteristics of the participants (basic demographics, clinical features, available predictors), including the number of participants with missing data for predictors and outcome. 13c V For validation, show a comparison with the development data of the distribution of important variables (demographics, predictors and outcome). Model development 14a D Specify the number of participants and outcome events in each analysis. 14b D If done, report the unadjusted association between each candidate predictor and outcome. Model specification 15a D 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). 15b D Explain how to the use the prediction model. Model performance 16 D;V Report performance measures (with CIs) for the prediction model. Model-updating 17 V If done, report the results from any model updating (i.e., model specification, model performance). Discussion Limitations 18 D;V Discuss any limitations of the study (such as nonrepresentative sample, few events per predictor, missing data). Interpretation 19a V For validation, discuss the results with reference to performance in the development data, and any other validation data.