Imperial College London
Alternate

Professor Tom Bourne

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Chair in Gynaecology
 
 
 
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Contact

 

+44 (0)20 3313 5131t.bourne Website

 
 
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Location

 

Early pregnancy and acute gynaecologyInstitute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Luijken:2020:10.1016/j.jclinepi.2019.11.001,
author = {Luijken, K and Wynants, L and van, Smeden M and Van, Calster B and Steyerberg, EW and Groenwold, RHH and Collaborators and Timmerman, D and Bourne, T and Ukaegbu, C},
doi = {10.1016/j.jclinepi.2019.11.001},
journal = {Journal of Clinical Epidemiology},
pages = {7--18},
title = {Changing predictor measurement procedures affected the performance of prediction models in clinical examples},
url = {http://dx.doi.org/10.1016/j.jclinepi.2019.11.001},
volume = {119},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - OBJECTIVE: To quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols. STUDY DESIGN AND SETTING: We examined effects of various scenarios of predictor measurement heterogeneity in real-world clinical examples using previously developed prediction models for diagnosis of ovarian cancer, mutation carriers for Lynch syndrome, and intrauterine pregnancy. RESULTS: Changing the measurement procedure of a predictor influenced the performance at validation of the prediction models in nine clinical examples. Notably, it induced model miscalibration. The calibration intercept at validation ranged from -0.70 to 1.43 (0 for good calibration), while the calibration slope ranged from 0.50 to 1.67 (1 for good calibration). The difference in c-statistic and scaled Brier score between derivation and validation ranged from -0.08 to +0.08 and from -0.40 to +0.16, respectively. CONCLUSION: This study illustrates that predictor measurement heterogeneity can influence the performance of a prediction model substantially, underlining that predictor measurements used in research settings should resemble clinical practice. Specification of measurement heterogeneity can help researchers explaining discrepancies in predictive performance between derivation and validation setting.
AU  - Luijken,K
AU  - Wynants,L
AU  - van,Smeden M
AU  - Van,Calster B
AU  - Steyerberg,EW
AU  - Groenwold,RHH
AU  - Collaborators
AU  - Timmerman,D
AU  - Bourne,T
AU  - Ukaegbu,C
DO  - 10.1016/j.jclinepi.2019.11.001
EP  - 18
PY  - 2020///
SN  - 0895-4356
SP  - 7
TI  - Changing predictor measurement procedures affected the performance of prediction models in clinical examples
T2  - Journal of Clinical Epidemiology
UR  - http://dx.doi.org/10.1016/j.jclinepi.2019.11.001
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31706963
UR  - http://hdl.handle.net/10044/1/75334
VL  - 119
ER  -
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