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Prediction modeling-part 1: regression modeling. | LitMetric

Prediction modeling-part 1: regression modeling.

Kidney Int

School of Public Health, The University of Sydney, Sydney, New South Wales, Australia; Centre for Kidney Research, Children's Hospital at Westmead, Sydney, New South Wales, Australia.

Published: May 2020

Risk prediction models are statistical models that estimate the probability of individuals having a certain disease or clinical outcome based on a range of characteristics, and they can be used in clinical practice to stratify disease severity and characterize the risk of disease or disease prognosis. With technological advancements and the proliferation of clinical and biological data, prediction models are increasingly being developed in many areas of nephrology practice. This article guides the reader through the process of creating a prediction model, including (i) defining the clinical question and type of model, (ii) data collection and data cleaning, (iii) model building and variable selection, (iv) model performance, (v) model validation, (vi) model presentation and reporting, and (vii) impact evaluation. An example of developing a prediction model to predict mortality after intensive care unit admission for patients with end-stage kidney disease is also provided to illustrate the model development process.

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Source
http://dx.doi.org/10.1016/j.kint.2020.02.007DOI Listing

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