Key Aspects of Prognostic Model Development and Interpretation From a Clinical Perspective

IMPORTANCE Prognostication is an important aspect of clinical decision-making, but it is often challenging. Previous studies show that both patients and physicians tend to overestimate survival chances. Prediction models may assist in estimating and quantifying prognosis. However, insufficient understanding of the development, possibilities, and limitations of such models can lead to misinterpretations. Although many excellent books and comprehensive methodological articles on prognostic model research are published, they may not be accessible enough for the clinical audience. Our aim is to provide an overview on the main issues regarding prediction research for health care professionals to achieve better interpretation and increase the use of prognostic models in daily clinical practice.OBSERVATIONS The first steps of model development include coding of predictors, model specification, and estimation. Next, we discuss the assessment of the performance of a prediction model, including discrimination and calibration aspects, followed by approaches to internal and external validation and updating. Finally, model reporting, presentation, and steps toward clinical implementation are presented.CONCLUSIONS AND RELEVANCE After thorough consideration of the research question, data inspection, and coding of predictors, one can start with the specification of a prediction model. The number of candidate predictors should be kept limited, in view of the number of events in the data, to prevent overfitting. Calibration and discrimination are 2 aspects of model performance that complement each other and should be assessed preferably at external validation. Model development should be accompanied by qualitative research among patients and physicians to facilitate the development of a valuable tool and maximize possibilities for successful implementation. After model presentation is optimized, impact studies are required to assess the clinical value of a prediction model.Analysis and support of clinical decision makingDevelopment and application of statistical models for medical scientific researc