2. Lab 8: Types of Studies and Study Designs Lab Workbook (pp. 37 – 40)

3. A fish or being taught to fish? Lab based on study by Jolson et al. (1992) Concepts and techniques remain valid for –all disciplines –all populations –all designs

4. Background Population = patients undergoing bone marrow ablation Exposure = generic drug –Group 1 = exposed (N 1 = 25) –Group 0 = nonexposed (N 0 = 34) Disease (outcome) = cerebellar toxicity Hypothesis – generic drug presents greater risk of toxicity

5. Question 1 (p. 37) Read the Patients and Methods of the article. Is this study experimental or nonexperimental? The investigators studied the exposure without intervention. Thus: nonexperimental (“observational”)

6. Question 2 Suppose you could redesign the study as a trial. Describe a scheme for randomizing the exposure. Options: –Flip of coin –Tokens in a hat (half 1, half 0) –Use www.randomization.comwww.randomization.com

7. Question 3 What is the primary benefit of randomization? Randomization balances measured and unmeasured cofactors (potential confounders) Hence, difference found at end of study attribute to exposure and not confounding

8. Question 4 The study is a cohort study... Suppose it we had conducted it ecologically... difficulties with ecological design... ? Greater opportunity for confounding (discuss) Opportunity for the aggregation bias / ecological fallacy (discuss)

9. Question 5 Results risk 1 = 11 / 25 = 44% risk 0 = 3 / 34 = 9% What is random error in this context? …discuss… How it was dealt: –one-way ANOVA tests of means –chi-square and Fisher’s tests of proportions –95% confidence intervals for risk ratios

10. Question 6 Confounding derives from inherent differences at baseline... How did investigators address potential for confounding Table 1 -- no large differences by age, sex, type of leukemia, stage of disease, kidney function, etc. Also adjustment of RRs [Mantel-Haenszel] Concluded: potential for confounding was small

11. Question 7 Misclassification / (information) diagnostic suspicion bias? Yes, greater level of scrutiny in patients taking the generic drug!

12. Question 8 Study population was identified because of the problem. Selection bias? Yes, this might be a 1 in a 1000 chance- occurrence –What does the p value mean in the context? –Is this like shooting the broad side of a barn and drawing the bull’s-eye afterwards?

13. Question 9 Is the relation between the exposure and outcome causal? Causal inference consider other factors –e.g., Hill’s criteria (studied in epi) –Understanding causal mechanism is key

14. Question 10 Should drug be pulled from market? Factors that contribute to the decision –Scientific evidence – Finance (profitability) –Medico-legal (law suits) –Politics Use of scientific results for political and economic purposes are always suspect!