Clinical Trial Design

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Department of OUTCOMES RESEARCH
Clinical Research Design
Sources of Error
Types of Clinical Research
Randomized Trials
Daniel I. Sessler, M.D.
Professor and Chair
Department of OUTCOMES RESEARCH
The Cleveland Clinic
Sources of Error
There is no perfect study
• All are limited by practical and ethical considerations
• It is impossible to control all potential confounders
• Multiple studies required to prove a hypothesis
Good design limits risk of false results
• Statistics at best partially compensate for systematic error
Major types of error
• Selection bias
• Measurement bias
• Confounding
• Reverse causation
• Chance
Statistical Association
Chance
Causal
A
B
Causal
B
A
Statistical
Association
Measurement
Bias
Selection
Bias
Confounding
Bias
Selection Bias
Non-random selection for inclusion / treatment
• Or selective loss
Subtle forms of disease may be missed
When treatment is non-random:
• Newer treatments assigned to patients most likely to benefit
• “Better” patients seek out latest treatments
• “Nice” patients may be given the preferred treatment
Compliance may vary as a function of treatment
• Patients drop out for lack of efficacy or because of side effects
Largely prevented by randomization
Confounding
Association between two factors caused by third factor
For example:
• Transfusions are associated with high mortality
• But larger, longer operations require more blood
• Increased mortality consequent to larger operations
Another example:
• Mortality greater in Florida than Alaska
• But average age is much higher in Florida
• Increased mortality from age, rather than geography of FL
Largely prevented by randomization
Measurement Bias
Quality of measurement varies non-randomly
Quality of records generally poor
• Not necessarily randomly so
Patients given new treatments watched more closely
Subjects with disease may better remember exposures
When treatment is unblinded
• Benefit may be over-estimated
• Complications may be under-estimated
Largely prevented by blinding
Example of Measurement Bias
Reported parental history
Arthritis (%)
No arthritis (%)
Neither parent
27
50
One parent
58
42
Both parents
15
8
P = 0.003
From Schull & Cobb, J Chronic Dis, 1969
Reverse Causation
Factor of interest causes or unmasks disease
For example:
• Morphine use is common in patients with gall bladder disease
• But morphine worsens symptoms which promotes diagnosis
• Conclusion that morphine causes gall bladder disease incorrect
Another example:
• Patients with cancer have frequent bacterial infections
• However, cancer is immunosuppressive
• Conclusion that bacteria cause cancer is incorrect
Largely prevented by randomization
External Threats to Validity
External validity
Internal validity
Subjects enrolled
Population of
interest
Selection bias
Measurement bias
Confounding
Chance
Eligible
Subjects
?
??
Conclusion
Types of Clinical Research
Observational
• Case series
– Implicit historical control
– “The pleural of anecdote is not data”
• Single cohort (natural history)
• Retrospective cohort
• Case-control
Retrospective versus prospective
• Prospective data usually of higher quality
Randomized clinical trial
• Strongest design; gold standard
• First major example: use of streptomycin for TB in 1948
Case-Control Studies
Identify cases & matched controls
Look back in time and compare on exposure
E
x
p
o
s
u
r
e
Time
Case Group
Control Group
Cohort Studies
Identify exposed & matched unexposed patients
Look forward in time and compare on disease
Exposed
Unexposed
Time
D
i
s
e
a
s
e
Timing of Cohort Studies
RETROSPECTIVE
COHORT STUDY
AMBIDIRECTIONAL COHORT STUDY
PROSPECTIVE COHORT STUDY
Time
Initial exposures
Disease onset or diagnosis
Randomized Clinical Trials (RCTs)
A type of prospective cohort study
Best protection again bias and confounding
• Randomization: reduces selection bias & confounding
• Blinding: reduces measurement error
• Not subject to reverse causation
RCTs often “correct” observational results
Types
• Parallel group
• Cross-over
• Factorial
• Cluster
Parallel Group
Enrollment Criteria
Randomize participants
to treatment groups
Intervention A
Intervention B
Outcome A
Outcome B
Cross-over Diagram
Enrollment Criteria
Randomize individuals
To sequential treatment
Treatment A
± Washout
Treatment B
Treatment B
± Washout
Treatment A
Pros & Cons of Cross-over Design
Strategy
• Sequential treatments in each participant
• Patients act as their own controls
Advantages
• Paired statistical analysis markedly increases power
• Good when treatment effect small versus population variability
Disadvantages
• Assumes underlying disease state is static
• Assumes lack of carry-over effect
• May require a treatment-free washout period
• Evaluate markers rather than “hard” outcomes
• Can’t be used for one-time treatments such as surgery
Factorial
Simultaneously test 2 or more interventions
• Balanced treatment allocation
Clonidine +ASA
Placebo + ASA
Clonidine + Placebo Placebo + Placebo
Advantages
• More efficient than separate trials
• Can test for interactions
Disadvantages
• Complexity, potential for reduced compliance
• Reduces fraction of eligible subjects and enrollment
• Rarely powered for interactions
– But interactions influence sample size requirements
Factorial Outcome Example
60
No
antiemetics
Incidence of PONV (%)
50
One
antiemetic
40
Ond
30
20
Dex Drop
Two
antiemetics
Three
antiemetics
Ond Ond Dex
& Dex &Drop &Drop
10
0
Apfel, et al. NEJM 2004
Subject Selection
Tight criteria
• Reduces variability and sample size
• Excludes subjects at risk of treatment complications
• Includes subjects most likely to benefit
• May restrict to advance disease, compliant patients, etc.
• Slows enrollment
• “Best case” results
– Compliant low-risk patients with ideal disease stage
Loose criteria
• Includes more “real world” participants
• Increases variability and sample size
• Speeds enrollment
• Enhances generalizability
Randomization and Allocation
Only reliable protection against
• Selection bias
• Confounding
Concealed allocation
• Independent of investigators
• Unpredictable
Methods
• Computer-controlled
• Random-block
• Envelopes, web-accessed, telephone
Stratification
• Rarely necessary
Blinding / Masking
Only reliable prevention for measurement bias
• Essential for subjective responses
– Use for objective responses whenever possible
• Careful design required to maintain blinding
Potential groups to blind
• Patients
• Providers
• Investigators, including data collection & adjudicators
Maintain blinding throughout data analysis
• Even data-entry errors can be non-random
• Statisticians are not immune to bias!
Placebo effect can be enormous
Importance of Blinding
Chronic Pain
Analgesic
Mackey, Personal communication
More About Placebo Effect
Kaptchuk, PLoS ONE, 2010
Selection of Outcomes
Surrogate or intermediate
• May not actually relate to outcomes of interest
– Bone density for fractures
– Intraoperative hypotension for stroke
• Usually continuous: implies smaller sample size
• Rarely powered for complications
Major outcomes
• Severe events (i.e., myocardial infarction, stroke)
• Usual dichotomous: implies larger sample size
• Mortality
Cost effectiveness / cost utility
Quality-of-life
Composite Outcomes
Any of ≥2 component outcomes, for example:
• Cardiac death, myocardial infarction, or non-fatal arrest
• Wound infection, anastomotic leak, abscess, or sepsis
Usually permits a smaller sample size
Incidence of each should be comparable
• Otherwise common outcome(s) dominate composite
Severity of each should be comparable
• Unreasonable to lump minor and major events
• Death often included to prevent survivor bias
Beware of heterogeneous results
Trial Management
Case-report forms
• Require careful design and specific field definitions
• Every field should be completed
– Missing data can’t be assumed to be zero or no event
Data-management (custom database best)
• Evaluate quality and completeness in real time
• Range and statistical checks
• Trace to source documents
Independent monitoring team
Interim Analyses & Stopping Rules
Reasons trials are stopped early
• Ethics
• Money
• Regulatory issues
• Drug expiration
• Personnel
• Other opportunities
Pre-defined interim analyses
• Spend alpha and beta power
• Avoid “convenience sample”
• Avoid “looking” between scheduled analyses
Pre-defined stopping rules
• Efficacy versus futility
Potential Problems
Poor compliance
• Patients
• Clinicians
Drop-outs
Crossovers
Insufficient power
• Greater-than-expected variability
• Treatment effect smaller than anticipated
Fragile Results
Consider two identical trials of treatment for infarction
• N=200 versus n=8,000
Trial
N
Treatment
Events
Placebo
Events
RR
P
A
200
1
9
0.11
0.02
B
4,000
200
250
0.80
0.02
Which result do you believe? Which is biologically plausible?
What happens if you add two events to each Rx group?
• Study A p=0.13
• Study B p=0.02
Four versus Five Rx for CML
Problem Solved?
How About Now?
Small Studies Often Wrong!
Multi-center Trials
Advantages
• Necessary when large enrollment required
• Diverse populations increase generalizability of results
• Problems in individual center(s) balanced by other centers
– Often required by Food and Drug Administration
Disadvantages
• Difficult to enforce protocol
– Inevitable subtle protocol differences among centers
• Expensive!
“Multi-center” does not necessarily mean “better”
Unsupported Conclusions
Beta error
• Insufficient detection power confused with negative result
Conclusions that flat-out contradict presented results
• “Wishful thinking” — evidence of bias
Inappropriate generalization: internal vs. external validity
• To healthier or sicker patients than studied
• To alternative care environments
• Efficacy versus effectiveness
Failure to acknowledge substantial limitations
Statistical significance ≠ clinical importance
• And the reverse!
Conclusion: Good Clinical Trials…
Test a specific a priori hypothesis
• Evaluate clinically important outcomes
Are well designed, with
• A priori and adequate sample size
• Defined stopping rules
Are randomized and blinded when possible
Use appropriate statistical analysis
Make conclusions that follow from the data
• And acknowledged substantive limitations
Department of OUTCOMES RESEARCH
Meta-analysis
“Super analysis” of multiple similar studies
• Often helpful when there are many marginally powered studies
Many serious limitations
• Search and selection bias
• Publication bias
– Authors
– Editors
– Corporate sponsors
• Heterogeneity of results
Good generalizability
Rajagopalan, Anesthesiology 2008
Design Strategies
Life is short; do things that matter!
• Is the question important?
Is it worth years of your life?
Concise hypothesis testing of important outcomes
• Usually only one or two hypotheses per study
• Beware of studies without a specified hypothesis
A priori design
• Planned comparisons with identified primary outcome
• Intention-to-treat design
General statistical approach
• Superiority, equivalence, non-inferiority
• Two-tailed versus one-tailed
It’s not brain surgery, but…
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