Document Sample
Experiments Powered By Docstoc
					 1954 Salk polio vaccine trials
► Biggest public health
  experiment ever
► Polio epidemics hit
  U.S. in 20th century
► Struck hardest at
► Responsible for 6% of
  deaths among 5- to 9-
        Number of polio cases in the U.S.
        1930 to 1955






    1930          1934          1938          1942          1946          1950          1954
           1932          1936          1940          1944          1948          1952

   Salk vaccine trial: Background
► Polio is rare but the virus itself is common
► Most adults experienced polio infection without
  being aware of it.
► Children from higher-income families were more
  vulnerable to polio!
► Children in less hygienic surroundings contract
  mild polio early in childhood while still protected
  from their mother’s antibodies. They develop
  immunity early.
► Children from more hygienic surroundings don’t
  develop such antibodies.
   Salk trial: The need for testing
► By  1954, Salk’s research with a vaccine looked
► Government agencies were ready to try the
  vaccine in the general population but some
  scientists feared the vaccine was unsafe or
► There was enormous fear and desperation
  throughout the country.
► Why not just distribute the vaccine to some and
  see if it lowered the polio rate?
    A yearly drop might mean the drug was
     effective, or that that year was not an
     epidemic year
► Vaccine could not be distributed without testing
                Salk vaccine trial:
              The need for controls
►   An experiment requires controls.
►   To test if the vaccine was effective the only variable that
    should be considered is the vaccine itself
►   This means that some children would get the vaccine and
    some would not.
►   This raises enormous ethical questions:
      Is it ethical to not give children the vaccine?
      Imagine yourself as a parent in these desperate times.
       Would you participate in such an experiment.
      Ultimately, does the benefit to society outweigh the risk
       to those children who would not get the vaccine?
             Salk vaccine:
       The need for massive trials
► Polio rate of occurrence is about 50 per100,000
► Suppose the vaccine was 50% effective   and
  10,000 subjects were recruited for each of the
  control and treatment groups
    You would expect 5 polio cases in control group and
     2-3 in treatment group
    Such a difference could be attributed to random
                were needed on a massive scale
► Clinical trials
► The ultimate   experiment involved over 1.6
  million children, with over 600,000 children
           Controversy over the
         design of the experiment
► In order to isolate the vaccine as the only variable to be
  considered, the treatment and control groups need to
  be as similar as possible
► But how should subjects be recruited?
► Fact: volunteers tend to be better educated and more
  well-to-do than those who don’t participate
► In the context of the polio disease, relying on volunteers
  could potentially bias the results
     Subjects would tend to have higher rates of polio
     Subjects are not representative of the population
     Results would be biased against the vaccine
►   After much debate, the trials proceeded with two different
        “Observed Control” approach

►   Administer the experiment to 1st, 2nd, and 3rd graders
►   Offer the vaccination to 2nd graders
     This group would rely on volunteers (parental consent)
►   Use 1st and 3rd graders as control group
     These children would be observed for incidences of polio
► Supporters of this approach argued that there would not
  be much variability between grades so treatment and
  control groups would be similar
► And the control group would be “observed controls”
► But there were objections . . .
NFIP Observed Control study
►   Volunteers would result in more children from higher
    income families in treatment group
     Treatment group is thus more vulnerable to disease than
      control group
     Would expect more incidences of polio in the treatment
      group than in the control group
     Biases the experiment against the vaccine
►   How would incidents of the disease be diagnosed?
     Many forms of polio are hard to diagnose
     In making the diagnosis physicians would naturally ask
      whether a child was vaccinated or not
     Diagnosis for borderline cases could be affected by
      knowledge of what grade the child was in and whether the
      child was vaccinated or not
       Randomized control approach
►   This experiment relied on volunteer subjects overall.
►   But subjects were randomly assigned to treatment and
    control groups
►   Control group was given a placebo
►   Placebo material was prepared to look
    exactly like the vaccine so subjects didn’t
    know what treatment they were getting
►   Placebo-control group guards against the
    “placebo effect”
►   Many objected to the design on ethical
►   Jonas Salk himself called it “A `beautiful’ experiment over
    which the epidemiologist could become quite ecstatic but
    which would make the humanitarian shudder.”
      Randomized control approach

► Subjects were “blind”: they did not know to which
  group they were assigned
► Also, those doing the evaluation
  didn’t know which treatment
  any subject received
► Each vial was identified by a code
  number so no one involved in the
  vaccination or the diagnostic
  evaluation could know who got
  the 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 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.
       Comparing the two studies
►   Results show that the observed control study was biased
    against vaccine
     Treatment group got the vaccine but was more prone to higher
      polio rates
     Control group didn’t get the vaccine but was more prone to lower
      polio rates
► It’s impossible to determine what’s the effect of the
  vaccine and what’s the effect of socio-economic status
► This is called confounding—the inability to distinguish the
  separate impacts of two or more variables on a single
► In a randomized controlled experiment, by making the
  treatment and control groups as similar as possible (by
  randomization), we are able to isolate the variable of
  interest and eliminate confounding
       Comparing the two studies:
       are the results “significant”?
► In the “observed control” approach, chance
  enters the study in an unplanned and
  haphazard way based on what families will
► By contrast, for the randomized controlled
  experiment chance enters the study in a
  planned and simple way
   Each child has 50-50 chance to be in the
    treatment or control group
► Thisallows for the use of probability to
 analyze the results
    Are the results significant?
► Twocompeting positions—which side would you
 be on?
   Pro: “The vaccine is effective. There were less cases
    of polio in the treatment group than in the control
    group. We should undertake a massive vaccination
    program throughout the general population.”
   Con: “We are not convinced. The two groups were
    randomly divided. There may have been fewer polio-
    prone people in the treatment group. It was all done
    by chance. We can’t be sure and we’re not willing to
    commit millions of dollars of taxpayer’s money on a
    vaccination program that might not be effective.”
     Are the results significant?
►   Assume the cons are right and that the
    vaccine is worthless. What are the
    chances of seeing such a large
    difference in the two groups?
►   Imagine a “polio” coin where the
    chance of heads is equal to the
    chance that a person gets polio.
    Flip the coin in Room A for 200,000 times. Then flip it
    in Room B for 200,000 times. What’s the chance that
    we would get such a large difference as 28 heads in A
    and 71 heads in B?
►   They are over a billion to one against!
►   In the face of such odds, we say that the outcome is
    statistically significant. The effect is so large that it
    would rarely occur by chance.
     Salk vaccine trials aftermath
► The results, announced in 1955, showed good statistical
  evidence that Jonas Salk's vaccine was 80-90% effective in
  preventing paralytic poliomyelitis.
► Postscript: Polio was virtually eliminated from the
  Americas in 1994, but still circulates in Asia and Africa,
  paralyzing the world’s most vulnerable children.
► The Global Polio Eradication Initiative was begun in 1988.
  That year, an estimated 350,000 children were paralyzed
  with polio worldwide.
► In 2004, polio cases had fallen to just over 1,200 cases
The language of experimental design

   In an experiment, we have at least one explanatory
    variable, called a factor, to manipulate and at least
    one response variable to measure
   The specific values that the experimenter chooses
    for a factor are called the levels of the factor.
   A treatment is a combination of specific levels from
    all the factors that an experimental unit receives.
   The ability to manipulate factors, apply treatments,
    and compare the responses is what differentiates an
    experiment from an observational study
Observational studies

   Nurses Health Study often in the news
    –   Over 100,000 registered nurses aged 30 to 55 have been
        followed for more than 30 years
    –   Detailed questionnaires sent out every two years on a wide
        variety of health and nutrition issues
    –   90% response rate
    –   “One of the most significant studies ever conducted on the
        health of women.” -- Donna Shalala, Former Secretary of
        the U.S. Department of Health and Human Services
   This is a prospective study. Subjects were identified
    in advance and data collected as events unfolded.
   Many observational studies are retrospective.
    Subjects are selected and their previous conditions
    or behaviors are determined.

   Observational studies can suffer from
    confounding and lurking variables
   You’ll read about this over the weekend in
    “Hormone Studies: What Went Wrong?”
   The ability to control and manipulate
    variables and compare groups allows for
    eliminating confounding and the effect of
    lurking variables
Double-blind, placebo-controlled
randomized comparative experiment:
The “gold standard” of statistics
   Massive clinical trials industry
   Complex ethical questions for experiments involving human
     –   Informed Consent, Institutional Review Board, Confidentiality
   Placebo effect is a fascinating area of research
     –   In conditions such as pain, the percent of patients responding to
         placebos has been shown to be 20% to 50%.
     –   Reflects the amount that the body can be coaxed/empowered to
         heal itself, in the absence of other active agents.
   Today, few clinical trials compare against placebo. Most new
    drugs are improvements over existing therapies. If an existing
    medicine exists it would be unethical to deny it to subjects
Other experimental design issues:

   When groups of experimental units are similar, it’s
    often a good idea to gather them together into
   Blocking isolates the variability due to the differences
    between the blocks so that we can see the
    differences due to the treatments more clearly.
   When randomization occurs only within the blocks,
    we call the design a randomized block design
   By contrast, a completely randomized design, all
    subjects have an equal chance of receiving any
Diagram of a blocked experiment
Hypertension pharmacogenetics study

 • Hypertension is most prevalent risk factor for diseases
   of the heart, brain and kidneys, affecting 43 million in
 • Complex disease affected by physical, physiological
   and environmental factors
 • State-of-the-art for treatment is trial-and-error
 • Less than 40% of treated patients achieve blood
   pressure control (systolic blood pressure < 140)
 • Ultimate goal of this study is to identify unknown
   genes that influence drug response with the potential of
   tailoring antihypertensive therapy for individuals
             GERA Clinical Trial
• Black and white patients react differently to blood pressure
• Blocked experimental design
• Mayo Clinic – Rochester, MN
   – 300 white subjects with hypertension (150 women and 150 men, ages
     30 to 60)
• Emory University – Atlanta, GA
   – 300 Black subjects with hypertension (150 women and 150 men, ages
     30 to 60)
• Subjects had previous medications discontinued for 4 weeks;
  blood pressure rose and stabilized in hypertensive range
• Hydrochlorothiazide administered for 4 weeks
• Blood pressure measured at the beginning of therapy and after
  4 weeks
• In each group, identify 100 “best” responders and 100 “worst”
  responders by change in blood pressure
     BP decrease           BP increase

Yr       N Race          Drug            Race N
1       100   B    Hydrochlorothiazide    B   100
2       100   W                           W   100
              GERA clinical trial
• DNA collected for each patient
• Data consists of 100,000 genetic markers called Single-
  Nucleotide Polymorphisms (SNPs)
• Goal: to find an association between blood pressure response
  and genetic makeup
• Ultimate goal: to find those genes that affect blood pressure
• What makes this complicated is that we have only 400
  observations (the patients) and over 100,000 variables (the
  genetic markers)
• Classically in statistics we had a “few” variables and “many”
  observations. As datasets become larger and more complex,
  this classic paradigm is shifting and the challenges are

Shared By:
xiaohuicaicai xiaohuicaicai