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      The Science of Prognosis in Psychiatry : A Review

      Author(s): 1 , 2 , 3 , 4 , 5 , 6
      Publication date Created:
      Publication date (Print):
      Journal: JAMA Psychiatry
      Publisher: American Medical Association (AMA)

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          Abstract

          Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.

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          Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

          FRANK E. HARRELL, KERRY L LEE, DANIEL B. MARK (1996)
          Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
          Article 0 comments Cited 1279 times – based on 0 reviews     Review now
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            The Elements of Statistical Learning

            Trevor J. Hastie, Robert Tibshirani, Jerome Friedman (2009)
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              Assessing the performance of prediction models: a framework for traditional and novel measures.

              Ewout W. Steyerberg, Andrew J Vickers, Nancy R Cook … (2010)
              The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
              Article 0 comments Cited 847 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: JAMA Psychiatry
                Abbreviated Title: JAMA Psychiatry
                Publisher: American Medical Association (AMA)
                ISSN (Print): 2168-622X
                Publication date Created: December 01 2018
                Publication date (Print): December 01 2018
                Volume: 75
                Issue: 12
                Page: 1289
                Affiliations
                [1 ]Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
                [2 ]OASIS Service, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
                [3 ]Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
                [4 ]Department of Medical Sciences, Cardiology, and Uppsala Clinical Research Center, Uppsala University, Uppsala University Hospital, Uppsala, Sweden
                [5 ]Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
                [6 ]Department of Biomedical Data Sciences, Medical Statistics and Medical Decision Making, Leiden University Medical Center, Leiden, the Netherlands
                Article
                DOI: 10.1001/jamapsychiatry.2018.2530
                PubMed ID: 30347013
                SO-VID: 8982a5e6-f2d7-4662-b932-6fee39867119
                Copyright © © 2018
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