Computer programs to estimate overoptimism in measures of discrimination for predicting the risk of cardiovascular diseases.
Background Development of chronic disease risk prediction models has become a growing area of research in recent years. The internal validity of such models is sometimes lower than estimated from the development sample. Overfitting or overoptimism of the developed model and/or differences between th...
| Published in: | Journal of Evaluation in Clinical Practice Vol. 19; no. 2; pp. 358 - 363 |
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| Main Authors: | , |
| Format: | research tables/charts Journal Article |
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Wiley-Blackwell
Apr2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104245038&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104245038 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13561294 EV1 jtl: Journal of Evaluation in Clinical Practice issn: 13561294 maglogo: Y pubinfo: dt: Apr2013 vid: 19 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104245038 85938120 10.1111/j.1365-2753.2012.01834.x NLM22409210 104245038 ppf: 358 ppct: 5 formats: tig: atl: Computer programs to estimate overoptimism in measures of discrimination for predicting the risk of cardiovascular diseases. aug: au: Mannan, Haider R. McNeil, John J. affil: Research Fellow sug: subj: Cardiovascular Risk Factors Risk Assessment Discrimination Optimism Evaluation Computers and Computerization Utilization Cox Proportional Hazards Model Funding Source Human Hypertension Risk Factors Data Analysis Software Descriptive Statistics ab: Background Development of chronic disease risk prediction models has become a growing area of research in recent years. The internal validity of such models is sometimes lower than estimated from the development sample. Overfitting or overoptimism of the developed model and/or differences between the samples are likely causes for this. For modelling of an uncommon outcome, bootstrapping for overoptimism is the preferred method for afterwards shrinking of regression coefficients and the model's discrimination and calibration for overoptimism. However, computer programs for different types of bootstrap validation are not readily available. We developed two SAS macro programs - one for the simple bootstrap that compares the discriminatory performance of the Cox proportional hazards model from the original sample in bootstrap samples; and another (which is more efficient), known as stepwise bootstrap validation, that makes the same comparison but from models developed by variable selection from bootstrap samples in the original sample. These are illustrated through an example from cardiovascular disease (CVD) risk prediction. Methods Two SAS macro programs for Cox proportional hazards model using Proc PHREG were developed for estimating overoptimism in Harrell's C and Somers' D statistics. The computer programs were applied to data on CVD incidence for a Framingham cohort that combined both the original and offspring exams. The risk factors considered were current smoking, diabetes, age, sex, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, triglycerides and body mass index. Results The degree of overoptimism in both Harrell's C and Somers' D statistics were low. Both these statistics were corrected for overoptimism by subtracting overoptimism from their observed values. Between the two bootstrap validation algorithms, the degree of overoptimism was estimated to be higher for stepwise bootstrap validation. Conclusion The programs are very useful for evaluating the 'overoptimism corrected' predictive performance of Cox proportional hazards model. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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