Transcript 1954 Salk vaccine field trials

1954 Salk vaccine field trials
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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
Number of polio cases in the U.S.
1930 to 1955
60000
50000
40000
30000
20000
10000
0
1930
1934
1932
YEAR
1938
1936
1940
1942
1946
1944
1950
1948
1954
1952
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.
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
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
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
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
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
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
Results of vaccine trials
The randomized, controlled experiment
Size
Rate (per 100,000)
Treatment
200,000
28
Control
200,000
71
No consent
350,000
46
The NFIP/Observed Control study
Size
Rate (per 100,000)
Grade 2 (vaccine)
225,000
25
Grade 1, 3 (control)
725,000
54
Grade 2 (no consent)
125,000
44
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.
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
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
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
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
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
Randomization
How to assign subjects at random
Pick from a hat, computer generator, tables of random numbers
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http://bcs.whfreeman.com/ips4e/pages/bcsmain.asp?v=category&s=00010&n=99000&i=99010.01&o=
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Line
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Table B:
19223 95034 05756 28713 12531 42544 . . .
73676 47150 99400 01927 27754 42648 . . .
45467 71709 77558 00095 32863 29485 . . .
Simulates random digits
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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
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
...
00 Alex Baum
16 Alicia Mazzara
01 Tim Blaha
17 Martha Mont.
02 Rachel Br.
18 Jesse Moreno
03 Kari Chr.
19 Oyeyinka Oy.
04 Danielle D.
20 Ginger Price
05 Alex Eis.
21 Claire Rich.
06 Patricia Glab
22 Leon Schneider
07 Danny Gr.
23 Richard South.
08 Vince Huang
24 Hikaru Ter.
09 Nita Jain
25 Jacob Titus
10 Peter Juul
26 Jesse Trent.
11 Mou Khan
27 Andrew Tulloch
12 Asad Khawar
28 Bryce Turner
13 Lyndsey Kl.
29 Jonathan White
14 Jamie Ly.
30 Matthew Yelle
15 Dan Masello