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How to Develop, Validate, and Compare Clinical Prediction Models Involving
Radiological Parameters: Study Design and Statistical Methods
#MMPMID27134523
Han K
; Song K
; Choi BW
Korean J Radiol
2016[May]; 17
(3
): 339-50
PMID27134523
show ga
Clinical prediction models are developed to calculate estimates of the
probability of the presence/occurrence or future course of a particular
prognostic or diagnostic outcome from multiple clinical or non-clinical
parameters. Radiologic imaging techniques are being developed for accurate
detection and early diagnosis of disease, which will eventually affect patient
outcomes. Hence, results obtained by radiological means, especially diagnostic
imaging, are frequently incorporated into a clinical prediction model as
important predictive parameters, and the performance of the prediction model may
improve in both diagnostic and prognostic settings. This article explains in a
conceptual manner the overall process of developing and validating a clinical
prediction model involving radiological parameters in relation to the study
design and statistical methods. Collection of a raw dataset; selection of an
appropriate statistical model; predictor selection; evaluation of model
performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal
and external validation; comparison of different models using c-index, net
reclassification improvement, and integrated discrimination improvement; and a
method to create an easy-to-use prediction score system will be addressed. This
article may serve as a practical methodological reference for clinical
researchers.
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free
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