One of the main aims of statistics is to control and model variability in observed phenomena. A second important aim is to translate the results of such modelling into clinical decision-making, e.g., by constructing appropriate prediction models. Currently, model-based individualized predictions play an important role in the era of personalized medicine, where diagnosis and prognosis of a clinical outcome are based on a large number of observed clinical, individual and genetic characteristics ( ). The paper by Zhou ( ) describes an interesting summary of clinical prediction models that range from the establishment of a clinical problem, study design and data collection to the identification, construction, validation and assessment of the effectiveness of a prediction model. Moreover, it presents a brief discussion about the necessity to update a clinical prediction model over time and current practical issues. Finally, most of the paper is dedicated to the implementation in R of the different steps of construction, validation and effectiveness of two key examples of prediction models, the logistic regression model for categorical data and the Cox proportional hazards model for survival data (time-to-event data). The overview of how to apply the different R packages is highly useful and promotes the translation of statistical theory to its practical use.
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