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      Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?

      , ,
      Molecular Psychiatry
      Springer Science and Business Media LLC

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          Abstract

          Patients with mental disorders show many biological abnormalities which distinguish them from normal volunteers; however, few of these have led to tests with clinical utility. Several reasons contribute to this delay: lack of a biological 'gold standard' definition of psychiatric illnesses; a profusion of statistically significant, but minimally differentiating, biological findings; 'approximate replications' of these findings in a way that neither confirms nor refutes them; and a focus on comparing prototypical patients to healthy controls which generates differentiations with limited clinical applicability. Overcoming these hurdles will require a new approach. Rather than seek biomedical tests that can 'diagnose' DSM-defined disorders, the field should focus on identifying biologically homogenous subtypes that cut across phenotypic diagnosis--thereby sidestepping the issue of a gold standard. To ensure clinical relevance and applicability, the field needs to focus on clinically meaningful differences between relevant clinical populations, rather than hypothesis-rejection versus normal controls. Validating these new biomarker-defined subtypes will require longitudinal studies with standardized measures which can be shared and compared across studies--thereby overcoming the problem of significance chasing and approximate replications. Such biological tests, and the subtypes they define, will provide a natural basis for a 'stratified psychiatry' that will improve clinical outcomes across conventional diagnostic boundaries.

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          Most cited references37

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          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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              Problems of spectrum and bias in evaluating the efficacy of diagnostic tests.

              To determine why many diagnostic tests have proved to be valueless after optimistic introduction into medical practice, we reviewed a series of investigations and identified two major problems that can cause erroneous statistical results for the "sensitivity" and "specificity" indexes of diagnostic efficacy. Unless an appropriately broad spectrum is chosen for the diseased and nondiseased patients who comprise the study population, the diagnostic test may receive falsely high values for its "rule-in" and "rule-out" performances. Unless the interpretation of the test and the establishment of the true diagnosis are done independently, bias may falsely elevate the test's efficacy. Avoidance of these problems might have prevented the early optimism and subsequent disillusionment with the diagnostic value of two selected examples: the carcinoembryonic antigen and nitro-blue tetrazolium tests.
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                Author and article information

                Journal
                Molecular Psychiatry
                Mol Psychiatry
                Springer Science and Business Media LLC
                1359-4184
                1476-5578
                December 2012
                August 7 2012
                December 2012
                : 17
                : 12
                : 1174-1179
                Article
                10.1038/mp.2012.105
                22869033
                7298503c-2e1e-4a43-aef3-6d79925cc1ed
                © 2012

                http://www.springer.com/tdm

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