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      Prognostic Factors for Recurrences in Neck Pain Patients Up to 1 Year After Chiropractic Care

      , , ,
      Journal of Manipulative and Physiological Therapeutics
      Elsevier BV

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          Most cited references28

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          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.
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            Responsiveness of the numeric pain rating scale in patients with low back pain.

            Cohort study of patients with low back pain (LBP) receiving physical therapy. To examine the responsiveness characteristics of the numerical pain rating scale (NPRS) in patients with LBP using a variety of methods. Although several studies have assessed the reliability and validity of the NPRS, few studies have characterized its responsiveness in patients with LBP. Determination of change on the NPRS during 1 and 4 weeks was examined by calculating mean change, standardized effect size, Guyatt Responsiveness Index, area under a receiver operating characteristic curve, minimum clinically important difference, and minimum detectable change. Change in the NPRS from baseline to the 1 and 4-week follow-up was compared to the average of the patient and therapist's perceived improvement using the 15-point Global Rating of Change scale. The majority of patients had clinically meaningful improvement after both 1 and 4 weeks of rehabilitation. The standard error of measure was equal to 1.02, corresponding to a minimum detectable change of 2 points. The area under the curve at the 1 and 4-week follow-up was 0.72 (0.62, 0.81) and 0.92 (0.86, 0.97), respectively. The minimum clinically important difference at the 1 and 4-week follow-up corresponded to a change of 2.2 and 1.5 points, respectively. Clinicians can be confident that a 2-point change on the NPRS represents clinically meaningful change that exceeds the bounds of measurement error.
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              Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.

              M. S. Pepe (2004)
              A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker's ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. In addition, the serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.
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                Author and article information

                Journal
                Journal of Manipulative and Physiological Therapeutics
                Journal of Manipulative and Physiological Therapeutics
                Elsevier BV
                01614754
                September 2015
                September 2015
                : 38
                : 7
                : 458-464
                Article
                10.1016/j.jmpt.2015.06.014
                cf646901-cd9b-4d14-bcea-92f5e433a42c
                © 2015
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