Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population.
Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease...
| Published in: | Statistical Methods in Medical Research Vol. 28; no. 1; pp. 309 - 321 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Sage Publications Inc.
Jan2019
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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=133860304&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133860304 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jan2019 vid: 28 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 133860304 133860304 NLM28812439 133860304 10.1177/0962280217723945 NLM28812439 133860304 ppf: 309 ppct: 12 formats: tig: atl: Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population. aug: au: Powers, Scott McGuire, Valerie Bernstein, Leslie Canchola, Alison J. Whittemore, Alice S. affil: Department of Statistics, Stanford University, Stanford, CA, USA sug: subj: Models, Statistical Prospective Studies Epidemiology Young Adult Ovarian Neoplasms Etiology Ovarian Neoplasms Epidemiology Human Female Adult Calibration Selection Bias Surveys Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adult: 19-44 years Female ab: Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ . pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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