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Review

Preparing Data for Predictive Modelling

In: Fundamentals of Clinical Data Science [Internet]. Cham (CH): Springer; 2019. Chapter 6.
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Review

Preparing Data for Predictive Modelling

Sander M. J. van Kuijk et al.
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Excerpt

This is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.

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References

    1. Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ (Clin Res Ed). 2016;353:i2416. - PMC - PubMed
    1. Huang Z, Sun B, Wu S, Meng X, Cong Y, Shen G, et al. A nomogram for predicting survival in patients with breast cancer brain metastasis. Oncol Lett. 2018;15(5):7090–6. - PMC - PubMed
    1. van Klaveren D, Gotz HM, Op de Coul EL, Steyerberg EW, Vergouwe Y. Prediction of chlamydia trachomatis infection to facilitate selective screening on population and individual level: a cross-sectional study of a population-based screening programme. Sex Transm Infect. 2016;92(6):433–40. - PubMed
    1. Schoorel EN, van Kuijk SM, Melman S, Nijhuis JG, Smits LJ, Aardenburg R, et al. Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term. BJOG. 2014;121(2):194–201; discussion - PubMed
    1. Schoorel EN, Vankan E, Scheepers HC, Augustijn BC, Dirksen CD, de Koning M, et al. Involving women in personalised decision-making on mode of delivery after caesarean section: the development and pilot testing of a patient decision aid. BJOG. 2014;121(2):202–9. - PubMed

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