PTP 560

• Research MethodsWeek 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

Test+

+

-

Sp

Sn

a b

c d

1-Sn = - LR

+ LR = 1-Sp

Sp = d/b+d

Sn = a/a+c

ValidityRuling 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

ValidityPretest 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

www.openmedicine.ca/article/viewArticle/63/61

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 AnalysisA 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