Need:
Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.
Approach for solving today healthcare problems
In both the diagnostic and prognostic setting, estimates of probabilities are rarely based on a single predictor (Riley et al, 2013). Doctors naturally integrate several patient characteristics and symptoms (predictors, test results) to make a prediction. Prediction is therefore inherently multivariable. Prediction models (also commonly called ‘prognostic models,’ ‘risk scores,’ or ‘prediction rules’ (Steyerberg et al, 2013) are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability
Studies developing or validating (both internal and external ) a multivariable prediction model share specific challenges for researchers (Steyerberg et al, 2013). Several reviews have evaluated the quality of published reports that describe the development or validation prediction models Reporting was found to be poor with insufficient information described in all aspects of model development, from descriptions of patient data to statistical modelling methods. Reporting was also found to be generally poor, with key details on which predictors were examined, the handling and reporting of missing data, and model-building strategy often poorly described.
Solution
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.
The checklist link
Therefore when ever we are building a predictive model we need to make sure that it must satisfy this external validation via TRIPOD along with internal validation for the better usability and transparency
