Intervention Studies

1. Intervention Studies Principles of Epidemiology Lecture 10 Dona Schneider, PhD, MPH, FACE

2. Intervention Studies • Subjects are selected from a reference population, the group to which investigators hope to extrapolate their findings • Clear, specific definition of subjects prior to selection • No subjective decision making!

3. Intervention Studies (cont.) • Individuals are enrolled on the basis of exposure (the investigators control the intervention) • Both cases and controls come from the experimental group • All subjects (cases and controls) should be at high enough risk for manifesting the outcome so that the study is likely to detect a difference if the intervention works

4. Potential Problems • Selection bias • Volunteerism – after screening, the experimental group may no longer be generalizable to the reference population • Ethical considerations • IRB issues

5. Informed Consent • Describe the overall experience that will be encountered • Describe benefits of participation • Describe the risks of participation • Describe alternatives to participation • Describe the extent to which the subject’s information will remain confidential • Describe compensation and/or expenses that may be incurred

6. Informed Consent (cont.) • Obtain informed consent prior to a subject’s enrollment in a study • A subject may choose not to participate or withdraw at any time without negative consequences • Provide the subject with a list of people s/he can contact with further questions regarding the research, his/her rights as a participant, and potential research-related injuries • Remember, informed consent is a process, not just a form!

7. Potential Problems (cont.) • Reporting bias • Observer bias • Watch the experimental group more carefully than the control group

8. Controlling Bias and Confounding • Randomization • Distribute known and unknown confounders evenly among treatment groups • Occurs after informed consent is provided • Sufficient sample size • Improves the power to detect a difference • Improves the probability of generalizability to the reference population

9. Controlling Bias and Confounding (cont.) • Masking • Prevent subjects and study personnel from knowing who is in which treatment group • Verify compliance (reduce reporting bias) • Pill counting, laboratory studies, interviews of living companions

10. Controlling Bias and Confounding (cont.) • Maintaining compliance with the intervention • Home visits • Payment at time of visit • Telephone and postcard reminders • Calendar pill packs • Daily logs • Pre-study compliance checks • Document reasons for noncompliance

11. Intention to Treat Analysis • Once randomized ALWAYS analyzed • The analysis must always include subjects who did not comply with the intervention or who did not finish the study • If you eliminate those who did not comply, you cannot address the research question – whether offering a treatment program is of benefit • Those who comply may be different from the entire experimental group • By using only those subjects who comply, you introduce further selection bias and reduce the generalizability of your results

12. Internal vs. External Validity • Large controlled trials usually have a high degree of internal validity • Randomization and masking minimizes the risk of confounding and bias, and a large “n” makes it more likely that chance can be ruled out as an explanation of an observed association • However, controlled trials also often have poor external validity (i.e., generalizability)

13. External Validity in Controlled Trials Reference Population Respond to letter? - Yes Respond to letter? - No Agree to screening? - Yes Agree to screening? - No Are they similar? Meet inclusion criteria? - Yes Meet inclusion criteria? - No Wish to continue? - Yes Wish to continue? - No Agree to randomization? - Yes Agree to randomization? - No Experimental Population

14. Crossover Studies • Subjects begin the study on Treatment A and later switch to Treatment B • Patients serve as their own control • Variation between individuals remains constant • Washout period between treatments reduces residual carryover

15. Group 1 Group 1 Group 2 Group 2 Group 2 Group 1 Design of a Planned Crossover Trial Randomized Treatment A Treatment B

16. Factorial Design • Use the same study population to test Drug A & Drug B • Assume: • The outcomes for each drug are different • Modes of action are independent • If you need to terminate the study of Drug A, you can continue the study to determine the effects of Drug B instead of beginning an entirely new study.

17. Factorial Design (cont.) • Example: Physician’s Health Study • Test aspirin as a means of preventing cardiovascular disease • Test beta-carotene as a means of preventing cancer • Terminated aspirin arm early due to a significant drop in the risk of first myocardial infarctions • Continued beta-carotene arm to completion

18. Factorial Design for Studying Effects of Two Treatments • Treatment B • + - • + • Treatment A • -

19. To Estimate Sample Size in a Clinical Trial You Need • The difference in response rates to be detected • An estimate of the response rate in one of the groups • Level of statistical significance () • The value of the power desired (1 – ) • Whether the test should be one- or two-sided

20. One-Sided Test

21. Two-Sided Test