1. EXPERIMENTAL STUDIES

2. Epidemiological Studies
Epidemiological studies are either observational or experimental.
Observational studies are considered “natural” experiments
Experimental studies are considered true experiments 

3. Assigning Exposure
Defining feature – Investigator assigns exposure to study subjects
most closely resemble controlled laboratory experiments
serve as models for the conduct of observational studies
gold standard of epidemiological research
have high status and validity
can pick up small and modest effects

4. 2. Overall Conduct Hypothesis formed
Study subjects recruited based on specific criteria
Informed consent
Eligible and willing subjects randomly allocated to receive treatment arm(s)
Study groups monitored for outcome
recurrence of disease
first occurrence of disease
getting better, side effects
Outcome rates compared across groups

5. 3. Ways to Categorize Experimental Studies
Individual versus community – treatment allocated to individual OR entire community
Do women with stage I breast cancer given a lumpectomy alone survive as long without recurrence of disease as women given a lumpectomy plus radiation?
Does fluoride in the water supply decrease the frequency of dental caries in a community compared to a similar community without such water treatment?

6. 3. Ways to Categorize Experimental Studies
Preventive versus therapeutic – prophylactic agent given to healthy or high-risk individual to prevent disease OR treatment given to diseased individual to reduce risk of recurrence, improve survival
Does tamoxifen lower the incidence of breast cancer in women with high risk profile compared to high risk women not given tamoxifen?
Do combinations of two or three antiretroviral drugs prolong survival of AIDS patients as well as regimens of single drugs?
Ethics – which study is easier to get approval for?

7. 4. Selection of Study Population
A) Reference population - general group to whom results of a trial should be applicable (all humans, or some restrictions may apply) B) Study or experimental population – people who are considered for enrollment in a trial, potential participants Is this important – does it really matter who participates?

9. 5. Issues to be Considered
Size, size, size - not just number of people in the trial, but how many endpoints (outcome under study) are expected
Restrictions on who is eligible (eligibility criteria)
Substantive: What group are you interested in?
Logistics: What group is accessible? Who will comply with study protocol? How feasible is complete and accurate follow-up on the subjects?
Characteristics of volunteers: How does study population differ from total experimental population?

10. 6. Allocation of Treatment
Should be random assignment
DEFINITION: Each individual has the same chance of receiving each possible “treatment”
Some examples of random allocation
Random number table: as each subject enrolled, assigned a number from the random number table; assign even numbers to treatment A and odd to treatment B.
Toss a coin for each subject: heads=A, tails=B
Some examples of nonrandom allocation
Alternate assignment of treatments
Assignment by day of the week
Why are these not random?

11. Goal of Randomization
To achieve baseline comparability between compared groups on factors related to outcome
Known factors
Unknown factors
Essence of good comparison between “treatments” is that the compared groups are the same EXCEPT for the “treatment.”

12. Goal of Randomization
The compared groups should have the same distribution of all of these characteristics.
Equal distribution of known and unknown factors that are relevant to response to the treatment (confounders)
Larger study – improves randomization works
Do you ever have to worry about confounders?

13. 7. Use of Placebo and Blinding: Goals
Placebos are used to make the groups as comparable as possible
Blinding:
Single blind -- subjects do not know if they are receiving treatment or placebo
Double blind -- neither subjects investigators or coordinators know who is receiving treatment or placebo
Purpose of blinding: To avoid bias in ascertainment of outcome
Placebo allows study to be blind
Why don’t we always blind?

14. Maintenance/assessment of compliance
A) Study requires active participation and cooperation of participants but deviations from the protocol will occur related to side effects, illness, level of interest, and length of follow-up
B) Noncompliance makes the compared groups more alike
Why is this a problem?
Should you move a non-compliant person to the control arm?
Should you move someone who starts the intervention on their own in the control arm to the treatment arm?
Does this happen often?

15. Maintenance/assessment of compliance
Design phase strategy
Pick an interested group and design a simple protocol.
Problem with this?
Throughout study
frequent contact with subjects
incentives to continue
such as free check-ups
compensation for their time

16. 9. Ascertaining the Outcome
A) Goals
High follow-up rates: don’t lose people
Why?
Uniform follow-up for compared groups: must be equally vigilant in follow-up in all compared groups

17. 10. Analysis of Data from Experimental Studies
Data set up: familiar 2 x 2 table
Measure of treatment effect: RR or RD
YES No
Total
Treatment a b
a+b
Placebo c d
c+d
a+c
b+d
a+b+c+d

18. 11. Important Issues in Experimental Studies: Ethical Considerations
Equipoise
Must be genuine doubt about efficacy of treatment yet sufficient belief that it may work
Stopping rules
What if it becomes apparent, before the trial is over, that the new treatment is beneficial (and should not be withheld from the placebo group) or is toxic (and treatment should be withdrawn)?

19. 11. Important Issues in Experimental Studies: Planning for an informative result
If the study finds no difference between compared treatments, do you believe it?
Or was there a difference but the study was not powerful enough to detect it?
Type II Error – failed to find a true difference
Initial consideration is study size.

20. 11. Important Issues in Experimental Studies: Analyzing by intention to treat
As the saying goes… once randomized, always analyzed.
Why?

21. PRACTICE FOR THE EXAM

22. Analysis of Cohort Studies
Example: Tuberculosis treatment and breast cancer study
Followed 1,047 women who were treated with air collapse therapy and exposed to numerous fluoroscopic examinations (radiation) and 717 who received other treatments.
A total of 47,036 woman-years of follow-up were accumulated during which 56 breast cancer cases occurred.

23. Analysis of Cohort Studies
Breast Cancer Cases
Woman-Years of follow-up
Exposed 41
28,001
Unexposed 15
19,025
Total 56
47,036
IR1 =
IR0 =
RR =
Interpretation:

24. Analysis of Cohort Studies
Breast Cancer Cases
Woman-Years of follow-up
Exposed 41
28,001
Unexposed 15
19,025
Total 56
47,036
IR1 = 41/28,011 = 1.5/1,000 woman-years
IR0 = 15/19,025 = 0.8/1,000 woman-years
RR = IR1/IR0 = 1.9
Interpretation: Women exposed to fluoroscopies had 1.9 times the risk of breast cancer compared to unexposed women.

25. Find out RR, AR in the exposed and Population AR
Smoking
Lung cancer
Total
YES NO 70
6930
7000 3
2997
3000 73
9927
10000
Find out RR, AR in the exposed and Population AR

26. Incidence of lung cancer among smokers
70/7000 = 10 per 1000
Incidence of lung cancer among non-smokers
3/3000 = 1 per 1000
RR = 10 / 1 = 10
(lung cancer is 10 times more common among smokers than non smokers)
AR = R (exposed) – R (unexposed)
= 10/1000 – 1/1000= 9/1000
The excess risk of lung cancer associated with smoking is 9/1000 persons among smokers. 9 cases per 1000 individuals who smoke would be prevented if they did not engage in smoking.

27. PAR = R (total) – Risk (unexposed) Risk (total)= 73/10,000=7.3/1000
The excess risk of lung cancer associated with smoking is 6.3/1000 persons in the general population cases per 1000 individuals in the general population would be prevented if no one smoked in the population.
Can also calculate as PAR=AR*P(exposure)
=9/1000*(7000/10000)= 6.3/1000

28. Smoking
Lung cancer
Total
YES NO 70
6930
7000 3
2997
3000 73
9927
10000
Calculate the AR Proportion in the Exposed, and the Population AR Proportion

29. Incidence of lung cancer among smokers
70/7000 = 10 per 1000
Incidence of lung cancer among non-smokers
3/3000 = 1 per 1000
AR Proportion exposed = R (exposed) – Risk(unexposed)
Risk (exposed)
= 10/1000 – 1/1000
10/1000
= 9/10 or 90%
90% of the cases of lung cancer among smokers are attributed to their habit of smoking

30. Incidence of lung cancer among smokers
70/7000 = 10 per 1000
Incidence of lung cancer among non-smokers
3/3000 = 1 per 1000
Incident of lung cancer total population
73/10,000=7.3 per 1000
Population AR Proportion = R (total) – Risk(unexposed)
Risk (total)
= 7.3/1000 – 1/1000
7.3/1000
= 8.6/10 or 86%
86% of the cases of lung cancer in the general population are attributed to smoking