40
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      PredictABEL: an R package for the assessment of risk prediction models

      research-article
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org) and CRAN ( http://cran.r-project.org/).

          Related collections

          Most cited references8

          • Record: found
          • Abstract: found
          • Article: not found

          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Use and misuse of the receiver operating characteristic curve in risk prediction.

            The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association.

              There is increasing interest in utilizing novel markers of cardiovascular disease risk, and consequently, there is a need to assess the value of their use. This scientific statement reviews current concepts of risk evaluation and proposes standards for the critical appraisal of risk assessment methods. An adequate evaluation of a novel risk marker requires a sound research design, a representative at-risk population, and an adequate number of outcome events. Studies of a novel marker should report the degree to which it adds to the prognostic information provided by standard risk markers. No single statistical measure provides all the information needed to assess a novel marker, so measures of both discrimination and accuracy should be reported. The clinical value of a marker should be assessed by its effect on patient management and outcomes. In general, a novel risk marker should be evaluated in several phases, including initial proof of concept, prospective validation in independent populations, documentation of incremental information when added to standard risk markers, assessment of effects on patient management and outcomes, and ultimately, cost-effectiveness.
                Bookmark

                Author and article information

                Contributors
                +31-10-7044214 , +31-10-7044657 , a.janssens@erasmusmc.nl
                Journal
                Eur J Epidemiol
                European Journal of Epidemiology
                Springer Netherlands (Dordrecht )
                0393-2990
                1573-7284
                24 March 2011
                24 March 2011
                April 2011
                : 26
                : 4
                : 261-264
                Affiliations
                Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
                Article
                9567
                10.1007/s10654-011-9567-4
                3088798
                21431839
                630af643-0c98-4825-9ce0-26c915500783
                © The Author(s) 2011
                History
                : 8 February 2011
                : 8 March 2011
                Categories
                Methods
                Custom metadata
                © Springer Science+Business Media B.V. 2011

                Public health
                genetic,assessment,measures,risk prediction,software
                Public health
                genetic, assessment, measures, risk prediction, software

                Comments

                Comment on this article