Transcript Experimental Studies Chapter 6 1

Chapter 6
Experimental Studies
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Chapter 6 Outline
6.1
6.2
6.3
6.4
Introduction
Historical perspective
General concepts
Data analysis
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Epi Experiments (“Trials”)
Trials - from the French trier (to try)
• Clinical trial – test therapeutic
interventions applied to individuals
(e.g., chemotherapy trial)
• Field trial – test preventive
interventions applied to individuals
(e.g., vaccine trial)
• Community trial – test
interventions applied at the
aggregate level (e.g., fluoridation of
public water trial)
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Illustrative Example 6.1
WHI Clinical Trial
• 40 US clinical centers
• Recruitment: 1993-1998
• Exposure randomized, double blinded: estrogen +
progestin vs identical looking placebo
• Average follow-up 5.2 years
• 1˚ outcome = Coronary Heart Disease
Risk of CHD in the exposed cohort
164
R1 
 0.01928 =19.3 per 1000
8506
Risk of CHD in the nonexposed cohort
R0 
122
 0.01506 = 15.1 per 1000
8102
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Survival curves
WHI estrogen trial
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Illustrative Example 6.2
Vitamin A Community Trial
• 450 Sumatran villages w ith high
childhood mortality rates
• Exposure = Vitamin A
supplementation program vs. no
intervention
• Random allocation of intervention:
229 treatment villages, 221 control
villages
Childhood mortality rate in exposed village
R1 
53
 4.9 per 1000
10,919
Childhood mortality rate in control villages
75
R0 
 7.3 per 1000
10, 231
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Historical perspective
Read in text
• Biblical reference
• Van Helmont’s
proposal (1662)
• James Lind’s scurvy
experiment (1753)
• Modern trials
– Polio trail (1954)
– MRFIT (1982)
– WHI (2002)
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“Natural Experiments”
• Natural conditions that
mimic an experiment
• Example: French surgeon
Paré (1510–1590) ran out
of boiling oil to treat
wounds → forced to use
an innocuous lotion for
treatment → noticed
Not a true experiment
vastly improved results
because the intervention
was not allocated by study
protocol
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Selected Concepts
Experimental Design
1. The control group (and the
placebo effect)
2. Randomization &
comparability
3. Follow-up and outcome
ascertainment
4. Intention-to-treat vs. perprotocol analysis
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The effects of an exposure can only be judged in
comparison to what would happen in its absence
Treatment Group
Control Group
Exposed to the intervention
Not exposed to intervention
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Illustration: “MRFIT”
• Multiple Risk Factor Intervention Trial (1982)
• 12,855 high risk men, 35- to 57-years-old
• Randomly assigned multi-factor Intervention (“special
intervention”) group or usual care group
• Study endpoints: Coronary Heart Disease (CHD) mortality
and overall mortality
• Results described here:
http://www.ncbi.nlm.nih.gov/pubmed/7050440
• No significant difference in endpoint rates
• Also, lower than expected rates in both groups
• Had no control group had been used, the intervention
might have unjustifiably been declared a success
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Polio Field Trial (1954)
Polio rates (per 100,000)
Placebo group
69
Refusers
46
Vaccinated group 28
Had Refusers been used as the
control group  effects of the
intervention would have been
underestimated
Am J Pub Health, 1957, 47: 283-7
Dr. Jonas Salk, 1953
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The placebo effect
Improvements attributed to an inert intervention
Despite popular belief, placebos have no real effect
False impressions of placebo effects can be explained by spontaneous
improvement, fluctuation of symptoms, regression to the mean, additional
treatment, conditional switching of placebo treatment, scaling bias, irrelevant
response variables, answers of politeness, experimental subordination,
conditioned answers, neurotic or psychotic misjudgment, psychosomatic
phenomena, misquotation, etc (Kienle & Kiene, 1997 )
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The Hawthorne Effect
Improvements in behavior
because subjects know
they are being observed 
effects unrelated to the
intervention
Initially observed in
industrial psychology
experiments in the 1930
A comparable attention
bias effect is seen in trials
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Randomization and
Comparability
Randomization works by balancing
potential confounding factors in the
treatment & control group
→ “like-to-like” comparisons
→ differences observed at completion
of trial due to the treatment or to
“chance”
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Checking Group Comparability
WHI Trial
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Follow-up & Outcome
Ascertainment
• Follow-up  screening for
study outcomes and
confirming the outcomes as
true (adjudication)
• Study outcomes based on
case definitions (uniform
and valid criteria for case
ascertainments)
• The importance of blinding
– Single blinding
– Double blinding
– Triple blinding
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Intention-to-treat vs. per-protocol
analysis
• Intention-to-treat (ITT) =
“analyze as randomized”
(regardless of
compliance)
• Per protocol (PP) =
analyze only those that
completed the protocol
• Effectiveness = “real
world” effectiveness
(including noncompliance)
• Efficacy = effect under
ideal conditions (e.g.,
complete compliance)
Human Subjects Ethics
now covered in Ch 5
• The Belmont Report
– Respect for
individuals
– Beneficence
– Justice
• IRB oversight
• Data Safety Monitoring
Board (DSMB)
• Informed consent
• Equipoise
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Equipoise
• Equipoise ≡
balanced doubt
• Cannot knowingly
expose a participant
to harm
• Cannot withhold
known benefit to
study subjects
• What’s left? (ANS:
equipoise)
Is equipoise the over-riding
principles of trial ethics?
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Advocacy vs. Scientific Ethics
• Advocacy, partisan, corporate,
advertising, and political ethics: “Plan
with the end result in mind.”
• Scientific ethics: A bending over
backwards to prove oneself wrong.
“I cannot give any scientist of any age any better
advice than this: The intensity of the conviction that a
hypothesis is true has no bearing on whether it is
true or not.”
Sir Peter Medewar
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Simple Analysis: Relative Effect
•
•
•
•
Data = WHI trial
E = HRT vs. placebo
D = CHD (yes or no)
Average follow-up: 5.2 years
164
R1 
 0.01928  19.28 per 1000
8506
122
R0 
 0.01506  15.06 per 1000
8102
R1 19.28 per 1000
RR 

 1.28
R0 15.06 per 1000
How to say it: HRT increased the risk of CHD by 28%
in relative terms.
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Simple Analysis: Absolute Effect
•
•
•
•
Data = WHI trial
E = HRT vs. placebo
D = CHD (yes or no)
Average follow-up: 5.2 years
164
R1 
 0.01928 = 19.28 per 1000 women
8506
122
R0 
 0.01506 = 15.06 per 1000 women
8102
RD  R1  R0  19.28 /1000  15.06 /1000  4.22 per 1000
How to say it: In absolute terms, there was an additional
4.22 CHD cases for every thousand women using HRT
over 5.2 years.
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Simple Analysis: Efficacy
same as RRD but without the minus sign
450 Sumatra villages randomly assigned to
either a vitamin A supplementation or not
R1 
53
 .004853  4.853 per 1000
10,919
75
R0 
 .0007329  7.329 per 1000
10, 231
RR 
4.853 per 1000
7.329 per 1000
 0.66
Efficacy = 1  RR  1  0.66  0.34
How to say it:
Vitamin A
supplementation
was 34% effective
in preventing
childhood mortality.
This provides a suitable taking-off point for the discussion of Rothman, K. J., Adami, H. O., & Trichopoulos, D.
(1998). Should the mission of epidemiology include the eradication of poverty? Lancet, 352(9130), 810-813.
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Simple Analysis: Absolute Effect
450 Sumatra villages randomly assigned to
either a vitamin A or control
75 deaths
53 deaths
 7.33 per 1000
R1 
 4.85 per 1000R0 
10, 231children
10,919 children
RD  R1  R0
 4.85 per 1000  7.33 per 1000
 2.47 per 1000
How to say it:
The effect was to reduce
mortality by 2.47 deaths
per 1000 children over
the period of observation.
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OpenEpi.com for data analysis
• “Counts” menu for
incidence proportions,
prevalences, and casecontrol data
• “Person Time” menu for
rate data
• Descriptive and inferential
(confidence intervals and
P-values) statistics
• Can be used as a
learning tool
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6.1 Bicycle helmet campaign
You want to test whether a public awareness campaign about bicycle safety at elementary
schools will increase bicycle helmets use among school-aged children. To test this
intervention, you identify 12 elementary schools, half of which will be randomly assigned
to participate in a school-wide bicycle helmet awareness program. The other 6 schools will
serve as controls and will receive no special intervention. Research assistants will
determine the percentage of bicyclists wearing helmets at standard locations in
neighborhoods of each of the schools before and after the intervention.
(A) What is the unit of intervention in this study? (The ‘‘unit of intervention’’ refers to the
level at which the intervention is randomized. This may differ from the ‘‘unit of
observation,’’ which is the unit upon which the outcome is measured.)
(B) What is the unit of observation in this study?
(C) Even though the intervention was randomized in this study, there were only 6
treatment schools and 6 controls schools. Therefore, there is a good chance that
treatment and control schools will differ with respect to important characteristics such as
socioeconomic status. Can you think of a way to control for socioeconomic status through
a randomization or study design approach?
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