Demystifying statistical language in clinical research reports Tuesdays with Faculty: An EBP Evening Series February 27

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 • Patient • Intervention • Comparison • Outcome PICO (McMaster University)

5. Example PICO (McMaster University) • P – adults with shoulder pain due to impingement • I – Therapeutic exercise • C – Rest and NSAIDs • O - improved function

6. Types of questions Defining question type facilitates search for information • Therapy / Intervention • Diagnosis • Etiology • Prognosis “User’s Guides”

7. How is outcome measured? • Count • Proportion • Continuous • test score • BMI • blood pressure • Time to event • disease progression • return to work

8. Statistics match outcome’s level of measurement • Count • Proportion (between-group differences) • Time to event (median times by group) • Continuous (differences in means)

10. Online sources for evidence • Pubmed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi • OUHSC library http://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” | H0 is true) OR • pr(you obtained this sample | H0) • 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 I • Rejecting a null hypothesis that is true •  = p(reject Ho | Ho) • Type II • Failing to reject null when alternative hypothesis is true OR • Failing to reject false null •  = p(fail to reject H0 | Ha)

16. Power and Sample Size •  = p(fail to reject H0 | Ha) • 1- = p(reject H0 | Ha) = POWER • A 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 H0 | Ha) • a function of: • Ha, 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/power/hypoth.htm

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

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