1954 Salk vaccine field trials

1. 1954 Salk vaccine field trials Biggest public health experiment ever Polio epidemics hit U.S. in 20th century Struck hardest at children Responsible for 6% of deaths among 5-9 year olds

3. Salk vaccine field trial • Polio is rare but virus itself is common • Most adults experienced polio infection without being aware of it. • Children from higher-income families more vulnerable to polio! Paradoxical, but • Children in less hygienic surroundings contract mild polio early in childhood while still protected from mother’s antibodies. Develop immunity early. • Children from more hygienic surroundings don’t develop such antibodies.

4. Salk vaccine field trial • By 1954, Salk poliomyelitis vaccine was promising • Public Health Service and National Foundation for Infantile Paralysis (NFIP) ready to try the vaccine in population • Vaccine could not be distributed without controls • A yearly drop might mean the drug was effective, or that that year was not an epidemic year. • Some children would get vaccine, some would not • Raises question of medical ethics

5. Salk vaccine trial • Polio rate of occurrence about 50 per 100,000 • Clinical trials needed on massive scale • Suppose vaccine was 50% effective and 10,000 subjects in control group, 10,000 subjects in treatment group • Would expect 5 polio cases in control group and 2-3 in treatment group • Difference could be attributed to random variation • Clinical trials needed on massive scale • Ultimate experiment involved over 1 million

6. How to design the experiment • Treatment and control groups should be as similar as possible • Any difference in response should be due to the treatment rather than something else • Taking volunteers biases the experiment • Fact: volunteers tend to be better educated and more well-to-do than those who don’t participate • Relying on volunteers biases the results because subjects are not representative of the population • Definition: A study is biased if it systematically favors certain outcomes

7. NFIP study: Observed Control approach • Offer vaccination (treatment) to 2nd graders • Control group: 1st and 3rd graders • Three grades drawn from same geographical location • Advantage: Not much variability between grades • Objections: • Uncertainty of the diagnostic process • Selective use of volunteers

8. NFIP Observed Control study In making diagnosis physicians would naturally ask whether child was vaccinated Many forms of polio hard to diagnose Borderline cases could be affected by knowledge of whether child was vaccinated Volunteers would result in more children from higher income families in treatment group Treatment group is more vulnerable to disease than control group Biases the experiment against the vaccine

9. Randomized control approach • Subjects randomly assigned to treatment and control groups • Control group given placebo • Placebo material prepared to look exactly like vaccine • Each vial identified only by code number so no one involved in vaccination or diagnostic evaluation could know who got vaccine • Experiment was double-blind, neither subjects nor those doing the evaluation knew which treatment any subject received

10. Results of vaccine trials The randomized, controlled experiment The NFIP/Observed Control study Source: Thomas Francis, J r., “An evaluation of the 1954 Poliomyelitis vaccine trials---summary report,” American Journal of Public Health vol 45 (1955) pp. 1-63.

11. Are the results significant? • Results show NFIP study biased against vaccine • Chance enters the study in a haphazard way: what families will volunteer, which children are in grade 2, etc. • For randomized controlled experiment chance enters the study in a planned and simple way: each child has 50-50 chance to be in treatment or control • Allows for use of probability to determine if the results are significant

12. Are the results significant? • Two competing positions • 1: The vaccine is effective. • 2: (Devil’s Advocate) The vaccine has no effect. The differences between the two groups is due to chance. • Probability to the rescue: Suppose vaccine has no effect. What are the chances of seeing such a large difference in the two groups? • We’ll do the calculations in a few weeks. But they are a billion to one against! • Definition: An outcome is statistically significant if the effect is so large that it would rarely occur by chance

13. Basic principles of statistical design of experiments • Randomization • Uses chance to assign subjects to treatments • Tends to prevent bias, or systematic favoritism, in experiments • Replication • Repeating the treatment on many subjects reduces role of chance variation • Comparison and Control • Compare treatments to prevent confounding effect of treatment with other influences • Also tends to prevent bias

14. More complex designs • Blocking allows for greater control of influential variables • Perhaps vaccine works differently on men and women • Set up separate “blocks” of men and women • Carry out randomization separately within each block • Allows for separate conclusions about each block

15. Matched pairs design • Special case of blocking • Pair up individuals or apply two treatments to same individual • Often used for before-and-after studies • Example: Effectiveness of a diet using weights of subjects measured before and after the diet treatment

16. Randomization • How to assign subjects at random • Pick from a hat, computer generator, tables of random numbers • http://bcs.whfreeman.com/ips4e/pages/bcs-main.asp?v=category&s=00010&n=99000&i=99010.01&o= • Table B: Line 101 19223 95034 05756 28713 12531 42544 . . . 102 73676 47150 99400 01927 27754 42648 . . . 103 45467 71709 77558 00095 32863 29485 . . . . • Simulates random digits • Every position has “the same probability” of being 0, 1, . . . , 9 • The digits in any position have “no influence” over the digits in any other position (e.g., they are “independent” of each other) • Doesn’t matter whether you pick a row, a column or a block as long as you do so consistently and without peeking

17. Using Table B • Assign numerical labels to population • Start anywhere in Table B and read off groups of numbers • Example: Pick random sample of 5 students • Label students from 00 to 30 • We used 2 digits to label so pick digits in pairs. • E.g., from Line 116 pick . . .