MK0683 Fundamentals Outlined

46 Regression analyses will be performed using SPSS.46 Scoring Each individual will be allocated a risk score. The risk score will be calculated by a sum of the products of individual values of each predictor variable and its regression coefficient.47 The full algorithm will be used to produce a score in the first instance to maximise predictive capacity. For the purpose of examining the performance of the predictive tool, patients will be classified as low, medium and high risks, based on their quartile of risk. Those in the highest quartile will be classified as high risk and those in the lowest quartile as low risk. The middle two quartiles will be classified as medium risk. Performance To examine the apparent performance (internal validity) of the prognostic screening tools, we will assess measures of overall performance, calibration and discrimination. Overall performance will be assessed using the Nagelkerke R2 and Brier score. The Brier score is a method of quantifying differences between actual binary outcomes and their predictions, that is, average prediction error.35 The Brier score ranges from 0 to 0.25, values close to 0 represent a useful model and values close to 0.25 a non-informative model. Calibration, that is, the agreement between observed and predicted frequencies of a given outcome, will be determined by plotting the mean predicted versus observed cases of chronic LBP for 10 risk stratification levels. The calibration slope and calibration-in-the-large statistic (intercept) will be calculated by constructing calibration plots. Discrimination, that is, the ability of the tool to discriminate between patients who did (+ve case) or did not (?ve case) develop chronic LBP, will be determined by using a Receiver Operator Characteristic Curve analysis, by calculating Oxygenase Discrimination slope (box plots) and by examining risk-stratified likelihood ratios. Performance indices and plots will be calculated using R software.48 Rules for interpretation of these statistics are presented in the Discussion. After the performance indices have been calculated, we will internally validate the model using bootstrapping techniques suggested by Moons et al33 (see online supplementary appendix B, Table B). Bootstrapping will be performed in SPSS using syntax available at http://gjyp.nl/marta/BootstrapValidationOfLogisticRegression.SPS. To assess model fit and optimism, bootstrapped estimates of the Nagelkerke R2 and its SE will be compared with the original model estimates. We will conduct a sensitivity analysis to assess performance of the tool for patients in different settings (general practice, physiotherapy, chiropractic). Validation of the tool Validation sample The validation sample consists of 1643 participants from a randomised trial conducted over 235 primary care centres in Greater Metropolitan Sydney, Australia.