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

Intervention Studies

Principles of Epidemiology

Lecture 10

Dona Schneider, PhD, MPH, FACE

PHCO 0502 Principles of Epidemiology (Schneider)

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!


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


Potential Problems Selection bias

Volunteerism – after screening, the experimental group may no longer be generalizable to the reference population

Ethical considerations IRB issues


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


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!


Potential Problems (cont.)

Reporting bias

Observer bias Watch the experimental group more

carefully than the control group


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


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


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


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


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)

Agree to screening? - Yes Agree to screening? - No

Meet inclusion criteria? - Yes

Wish to continue? - Yes Wish to continue? - No

Agree to randomization? - Yes Agree to randomization? - No

Experimental Population

External Validity in Controlled Trials

Respond to letter? - Yes Respond to letter? - No

Meet inclusion criteria? - NoAre they

similar?

Reference Population


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

Design of a Planned Crossover Trial

Randomized

Treatment A

Group 1

Group 1

Group 2

Treatment B

Group 2

Group 2

Group 1


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.


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


Factorial Design for Studying Effects of Two Treatments

Both A and B

(a)

A only

(b)

B only

(c)

Neither

A nor B

(d)

Treatment B

+ -

+Treatment A

-


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

One-Sided Test

Number of Patients Needed in Each Group to Detect Various Differences in Cure Rates; = .05; Power (1-) = .80 (One-Sided Test)

Lower of the

Two Cure Rates

Differences in Cure Rates Between the Two Treatment Groups

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

0.05 330 105 55 40 33 24 20 17 13 12 10 9 9 8

0.10 540 155 76 47 37 30 23 19 16 13 11 11 9 8

0.15 710 200 94 56 43 32 26 22 17 15 11 10 9 8

0.20 860 230 110 63 42 36 27 23 17 15 12 10 9 8

0.25 980 260 120 69 45 37 31 23 17 15 12 10 9 -

0.30 1,080 280 130 73 47 37 31 23 17 15 11 10 - -

0.35 1,160 300 135 75 48 37 31 23 17 15 11 - - -

0.40 1,210 310 135 76 48 37 30 23 17 13 - - - -

0.45 1,230 310 135 75 47 36 26 22 16 - - - - -

0.50 1,230 310 135 73 45 36 26 19 - - - - - -

Modified from Gehan E.: Clinical trials in cancer research. Environ Health Perspect 32:31, 1979.

Two-Sided Test Number of Patients Needed in Each Group to Detect Various Differences in Cure Rates; = .05; Power (1-) = .80 (Two-

Sided Test)

Lower of the Two

Cure Rates

Differences in Cure Rates Between the Two Treatment Groups

0.05 0.100.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55 0.60 0.65 0.70

0.05 420 130 69 44 36 31 23 20 17 14 13 11 10 8

0.10 680 195 96 59 41 35 29 23 19 17 13 12 11 8

0.15 910 250 120 71 48 39 31 25 20 17 15 12 11 9

0.20 1,090 290 135 80 53 42 33 26 22 18 16 12 11 9

0.25 1,250 330 150 88 57 44 35 28 22 18 16 12 11 -

0.30 1,380 360 160 93 60 44 36 29 22 18 15 12 - -

0.35 1,470 370 170 96 61 44 36 28 22 17 13 - - -

0.40 1,530 390 175 97 61 44 35 26 20 17 - - - -

0.45 1,560 390 175 96 60 42 33 25 19 - - - - -

0.50 1,560 390 170 93 57 40 31 23 - - - - - -

Modified from Gehan E.: Clinical trials in cancer research. Environ Health Perspect 32:31, 1979.

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