Transcript Slide 1
PTP 560 • Research Methods Week 12 Thomas Ruediger, PT April 5th is last 5 chapters-comprehensive review April 12th Final Bulk of final 80% Chap. 4,5,6 Underpinning for Scale, reliability, validity, chap 8 Sampling, 10 Experimental Designs, 17-21, 23, 24, 26, 27 Last 20%: 16, 22,25,28,29,32,34 Independent T-test • If the top row of Levene’s Sig is >.05, then do NOT assume equal variances and use the bottom row of chart. • If we research sig. then the t-stat has to be bigger than the critical value. If t-stat is bigger than critical then REJECT the NULL (because there is a difference) • The bigger the t-stat then will have a better chance of being bigger than the critical value. Validity Truth + 1-Sn = - LR Sp + a b - c d Test Sn = a/a+c Sn + LR = 1-Sp Sp = d/b+d Validity Ruling in/Ruling Out • SpPin – With high Specificity, – a Positive tests rules in the diagnosis • SnNout – With high Sensitivity, – a Negative tests rules out the diagnosis Validity Pretest Posttest Probability • Pretest – – – – What we think might be the problem Conceptually a “best guess” However, it is enhanced by pertinent literature Influenced by your clinical experience • Posttest – Revised probability based on test outcome – Likelihood ratios widely used in PT literature • +LR – How many more times a positive test will be seen in those with the disorder than without the disorder • -LR – How many more times a negative test will be seen in those with the disorder than without the disorder Receiver Operating Characteristic (ROC) Curves Strikes a balance between Sensitivity Specificity So that we can trade-off over and under diagnosing. Construction Set several cutoff points Plot Sensitivity and 1-Specificity Interpret Visually - which is best diagnostic tool? Mathematically the Area under the curve is best diagnostic trade-off Decide on Cutoff Based on the impact of incorrect decision Receiver Operating Characteristic (ROC) Curves 50:50 Clinical Prediction Rules • Incorporates Sensitivity, Specificity • Quantifies the contributions of different variables • Used to increase diagnostic utility – Is the patient at risk for a certain outcome? – Does the patient have this pathology • Ottawa ankle rules a good example Measuring Change MDD=can we find a difference one test to another MCID=can you find a difference being made for patients Distribution based methods (normalized data) Effect Size Index Standardized Response Mean Guyatt’s Responsiveness Index Standard Error of the Measurement Anchor Based Methods (like a pain scale) Global Rating of Change Ordinal scale based on subjective rating of change Global Rating Scale common Scale Epidemiology • Distribution and determinants of: – Disease – Injury – Dysfunction • Descriptive • Analytic Descriptive Epidemiology Incidence: the amount of new cases May be cumulative Number of new cases (during a given period) Total population at risk May be in person-time (used to be Number of new cases (during a given period) Total person time Prevalence: the amount of all cases (new & old) Number of existing cases (during a given period) Total population at risk Relationship between Incidence and Prevalence Analytic Epidemiology • Relative vs. Absolute Effects – Ratio vs. Actual difference • Relative Risk – Likelihood that exposed person gets disease • Odds Ratio – Analogous to RR – Applicable to Case-Control Situation Analytic Epidemiology • Event Rates and Risk Reduction • Experimental Event Rate (EER): with exposure • Control Event Rate (CER): without exposure • EER/CER = Relative Risk (RR) • CER-EER/CER = RRR (RR reduction) • CER-EER = ARR (Absolute Risk Reduction) Analytic Epidemiology • CER-EER = ARR (Absolute Risk Reduction) • 1/ARR = (Number needed to treat) NNT – If represents the number of patients that would be needed to be treated to make a change in their disorder as big as that in the study. Multivariate Analysis A one time read thorough is warranted Examine several variables for interrelationships Applications to correlation Partial correlation coefficient Regression Multiple independent variables Beta weights are standardized values for relative weighting R2 (coefficient of determination) is amount of total variance explained by all IVs Adjusted R2 corrects for chance Discriminate Analysis Analogue to multiple regression Used with categorical variables