Sensitivity analysis for healthcare models fitted to data by statistical methods.
After fitting complex models to data using statistical methods, a sensitivity analysis can be carried out. This determines which parts of a model are causing the bulk of the uncertainty in the model predictions (model "output"), and is a decision-support tool for the modeller who contemplates refining a model further or collecting additional data. A simple methodology for carrying out a sensitivity analysis is described. It is envisaged that such a relatively quick insight-generating step would precede the use of a more formal decision-theoretic approach that would address specific questions. Its use is illustrated using a model for breast cancer screening previously published in this journal. A simpler 3-parameter screening model is used in a simulation study of the error of the method as a function of sample size.
Baker
Rose D
RD
Center for OR and Applied Statistics, School of Acounting, Economics and Management Science, University of Salford, UK. r.d.baker@dial.pipex.com
D001943:Breast Neoplasms / Q000175:diagnosis / Q000517:prevention & control; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D003662:Decision Theory; D003695:Delivery of Health Care / Q000191:economics / Q000706:statistics & numerical data*; D006291:Health Policy; D006801:Humans; D016013:Likelihood Functions; D008403:Mass Screening; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D013222:State Medicine / Q000191:economics / Q000706:statistics & numerical data; D006113:United Kingdom