PTP 560 Research Methods Week 12 Thomas Ruediger, PT

April 5 th is last 5 chapters-comprehensive review April 12 th 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.

ValidityTruth Test Sp Sn ab cd 1-Sn = - LR + LR = 1-Sp Sp = d/b+d Sn = a/a+c

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  R 2 (coefficient of determination) is amount of total variance explained by all IVs  Adjusted R 2 corrects for chance  Discriminate Analysis  Analogue to multiple regression  Used with categorical variables