1. Demystifying statistical language in clinical research reports Tuesdays with Faculty: An EBP Evening Series February 27, 2007 David M. Thompson PT email: dave-thompson@ouhsc.edu web: http://moon.ouhsc.edu/dthompso/

2. A B C

3. Orienting to statistical procedures RESEARCH QUESTION and OUTCOME OF INTEREST LEVEL OF MEASUREMENT OF OUTCOME VARIABLE Nominal Ordinal Interval or Ratio SOURCE OF SAMPLE Independent or correlated observations Number of Samples (1,2, more than 2) ASSUMED DISTRIBUTION OF OUTCOME VARIABLE “Parametric” procedures for outcomes with known or assumed distributions Non-parametric procedures are “distribution free.” INFERENTIAL FOCUS Estimation: point estimate and confidence interval. Hypothesis testing: p-values and associated null hypotheses

4. Locate the key research questions PatientPatient InterventionIntervention ComparisonComparison OutcomeOutcome PICO (McMaster University)

5. Example P – adults with shoulder pain due to impingementP – adults with shoulder pain due to impingement I – Therapeutic exerciseI – Therapeutic exercise C – Rest and NSAIDsC – Rest and NSAIDs O - improved functionO - improved function

6. Types of questions D efining question type facilitates search for information Therapy / InterventionTherapy / Intervention DiagnosisDiagnosis EtiologyEtiology PrognosisPrognosis “User’s Guides” “User’s Guides”

7. How is outcome measured? CountCount ProportionProportion ContinuousContinuous –test score –BMI –blood pressure Time to eventTime to event – disease progression –return to work

8. Statistics match outcome’s level of measurement CountCount ProportionProportion (between-group differences) Time to eventTime to event (median times by group) ContinuousContinuous (differences in means)

10. Online sources for evidence PubmedPubmed http://www.ncbi.nlm.nih.gov/entrez/quer y.fcgi http://www.ncbi.nlm.nih.gov/entrez/quer y.fcgi http://www.ncbi.nlm.nih.gov/entrez/quer y.fcgi http://www.ncbi.nlm.nih.gov/entrez/quer y.fcgi OUHSC libraryOUHSC library http://library.ouhsc.edu http://library.ouhsc.eduhttp://library.ouhsc.edu

11. Inference Estimation Hypotheses testing

12. Estimation Point estimate typically an unbiased estimator of a population quantity Interval estimate 95 % confidence interval (CI) typically center on point estimate “plus or minus” [(z or t) * SE of point estimate]

13. Hypothesis Testing Determine if results are compatible with assumption that null hypothesis is true. Null is typically an assumption of NO difference, no association, no effect.

14. p values pr(“of obtaining a test statistic of this value or larger” | H 0 is true) OR pr(you obtained this sample | H 0 ) Test statistic is based on observations, and on assumption that null is TRUE. Statistic’s non-significance CANNOT IMPLY that the null is true (especially when power is low).

15. Errors Associated with Hypothesis Tests Type IType I Rejecting a null hypothesis that is trueRejecting a null hypothesis that is true  = p(reject H o | H o )  = p(reject H o | H o ) Type II Type II Failing to reject null when alternative hypothesis is true OR Failing to reject null when alternative hypothesis is true OR Failing to reject false null Failing to reject false null  = p(fail to reject H 0 | H a )  = p(fail to reject H 0 | H a )

16. Power and Sample Size  = p(fail to reject H 0 | H a )  = p(fail to reject H 0 | H a ) 1-  = p(reject H 0 | H a ) = POWER1-  = p(reject H 0 | H a ) = POWER A test’s power is its probability of making the correct decision (rejecting the null hypothesis) when a specific alternate hypothesis is trueA test’s power is its probability of making the correct decision (rejecting the null hypothesis) when a specific alternate hypothesis is true

17. Power and sample size POWER=1-  = p(reject H 0 | H a ) a function of: H a, a specific, stated alternative hypothesis, so requires specification of effect size the known or estimated variability. Estimates of variability depend in turn on sample size. Large samples provide more precise estimates of variability, and so also provide greater power. http://moon.ouhsc.edu/dthompso/CDM/powe r/hypoth.htm http://moon.ouhsc.edu/dthompso/CDM/powe r/hypoth.htm

18. Power and sample size calculations All calculations require an estimate of variability. POWER SAMPLE SIZE SIZE EFFECT SIZE

19. Levels of evidence Modified after: SUNY Downstate Medical Center, Medical Research Library of Brooklyn. (2005). A guide to research methods: The evidence pyramid. Retrieved January 4, 2006 from http://servers.medlib.hscbklyn.edu/ebm/2100.htm. Modified after: SUNY Downstate Medical Center, Medical Research Library of Brooklyn. (2005). A guide to research methods: The evidence pyramid. Retrieved January 4, 2006 from http://servers.medlib.hscbklyn.edu/ebm/2100.htm. http://servers.medlib.hscbklyn.edu/ebm/2100.htm

20. Online resources for evidence-based practice http://moon.ouhsc.edu/dthompso/CDM/ebplinks.htm