clinical research: basic statistics and appraising the literature

1. Clinical Research:Basic Statistics and Appraising the Literature

2. Importance of Understanding Basic Statistics in Medicine Research Design Studies Plan Analyses Data Interpretation Clinical Medicine Understanding the Literature Evidence-based practice

3. Learning the Language Sampling Variable types Determine anlaysis method(s) Categorical (qualitative; nominal) Ordinal Numerical (continuous; interval; ratio) Independent vs. Correlated Data Parametric vs. Non-parametric

4. Sampling: Is the study group representative?

5. Sampling: Is the study group representative?

6. Learning the Language Sampling Variable types Determine anlaysis method(s) Categorical (qualitative; nominal) Ordinal Numerical (continuous; interval; ratio) Independent vs. Correlated Data Parametric vs. Non-parametric

7. Variable Types: Ordinal, Numerical and Categorical

8. Learning the Language Sampling Variable types Categorical (qualitative; nominal) Ordinal Numerical (continuous; interval; ratio) Independent vs. Correlated Data Parametric vs. Non-parametric

9. Data from Independent Samples

10. Data from Repeated Measures: Correlated Data

11. Learning the Language Sampling Variable types Categorical (qualitative; nominal) Ordinal Numerical (continuous; interval; ratio) Independent vs. Correlated Data Parametric vs. Non-parametric

12. Parametric (Gaussian) Distribution

13. Skewed Data

14. Learning the Language Analysis Types Discrete vs. Time-dependent (survival) Logistic vs Linear Regression Modeling Trend Analyses Interactions Quantitative-common Qualitative-rare

15. Discrete Continuous Data Analysis: Correlated or Independent

16. Discrete Categorical Data Analysis: ?-square test

17. Categorical Data Analysis: Trend

18. Time-dependent Categorical Data Analysis: “Gold Standard”

19. PRISM-PLUS: Combined MI and Death During Initial 48 Hours in All Patients The effect of AGGRASTAT on reducing the combined endpoint of MI and death was seen during the first 48 hours of drug infusion. The combination of AGGRASTAT plus heparin produced a significant 66% risk reduction during this initial medical stabilization period. As previously noted, at 48 hours there was a reduction in the composite endpoint (death, MI, or refractory ischemia), but that reduction was nonsignificant (significance was reached at the next measurement point, 7 days).4The effect of AGGRASTAT on reducing the combined endpoint of MI and death was seen during the first 48 hours of drug infusion. The combination of AGGRASTAT plus heparin produced a significant 66% risk reduction during this initial medical stabilization period. As previously noted, at 48 hours there was a reduction in the composite endpoint (death, MI, or refractory ischemia), but that reduction was nonsignificant (significance was reached at the next measurement point, 7 days).4

20. Point Estimate Plots

21. Power: The assumptions Power = (1-?): Determines the # of subjects or assessments required in a study to achieve “statistical significance”, given a number of a priori assumptions: Control value and variance, or event rate Effect size dependent on parameter of interest best to have pilot data Significance level (?) 1-tailed or 2-tailed testing Confounders Non-compliance, Cross-overs (Drop Ins/Outs), Lost to follow up

22. Standards for Effect Size Small –20% Medium – 50% Large – 80% only rough guidelines Small, medium and large are subject dependent

23. Adequacy of Sample: Size Matters

24. Effect of trial size on results: 24 trials of ?-blockade vs. Placebo

25. Ways to Reduce Required Sample Size Higher Event Rate High risk populations Composite Endpoints Larger Effect Size Lower power Larger ? 1-tailed or 2 Change analysis type Time dependent

26. Sample size planning How much money do you have? How much time to you have? How many patients/subjects can you expect to reasonably get? “What sample size and study design can I afford?”

27. The words to use to describe this The study was designed to have >80% power to detect an effect size of >20% with a 2-tailed significance level of 0.05, with a planned sample size of 400 participants in each group.

28. Suggested Reading Reference texts Dawson-Saunders B, Trapp RG. Basic and Clinical Biostatistics, Appleton and Lange, Norwalk, CT, 2nd Edition, 1994. Sackett DL. Clinical Epidemiology: a basic science for clinical medicine. Little Brown, Boston, MA, 2nd Edition, 1991. Selected papers: Bias Sackett DL. Bias in analytic research. J Chron Dis 1979; 32:51-63 Power Moher D, Dulberg CS, Wells GA. Statistical power, sample size, and their reporting in randomized controlled trials. JAMA 1994; 272: 122-4. Subgroup analyses Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)use of baseline data in clinical trials. Lancet 2000; 355: 1064-1069. Yusuf S, Wittes J, Probstfield J, Tyroler HA. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA 1991; 266: 93-98.

29. Approaching the Literature: The Users’ Guides Are the results of the study valid If “Yes”, then What are the results? Will they help me care for my patients?

30. JAMA Users’ Guide to the Medical Literature: I-XXV

31. Objectives to the Users’ Guides Understand level of (un)certainty a perpetual shade of gray Key skill to critically appraise study validity prior to appraising results validity is qualitative assessment of the “closeness to the truth” is estimate unbiased?

32. “Is the Study Valid?” Checklist Primary Randomized? Accounting for all study subjects at conclusion, and analyzed as randomized? Lost patients considered in “worst case scenario” Secondary Blinded comparison with referent? Appropriate sampling to represent clinical population? Study groups similar except for comparator? Outcomes measured identically between groups? Was cohort at a well-defined point in course of disease? Was f/u sufficiently long/complete?

33. What are the Results? How large was the treatment effect Absolute difference (and “NNT”) Relative difference How precise is the estimate of treatment effect? Point estimate of effect Confidence intervals

34. Will Results Help me Care for my Patients? Are results applicable to my patients? Beyond the eligibility criteria Are there compelling reasons NOT to extrapolate to your patient? Beware the Sub-groups Were all clinically important outcomes considered? Intermediate biomarkers vs. Clinical endpoints Lumping vs. splitting Are benefits adequately balanced with risks and with cost? NNT Redux