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Transcript of Department of O UTCOMES R ESEARCH. Daniel I. Sessler, M.D. Professor and Chair Department of O...
![Page 1: Department of O UTCOMES R ESEARCH. Daniel I. Sessler, M.D. Professor and Chair Department of O UTCOMES R ESEARCH The Cleveland Clinic Clinical Research.](https://reader036.fdocuments.in/reader036/viewer/2022062511/551c2e5f550346a34f8b620c/html5/thumbnails/1.jpg)
Department of OUTCOMES RESEARCH
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Daniel I. Sessler, M.D.Professor and Chair
Department of OUTCOMES RESEARCH
The Cleveland Clinic
Clinical Research Design
Types of Clinical ResearchSources of Error
Study DesignClinical Trials
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Case-Control Studies
Identify cases & matched controls
Look back in time and compare on exposure
Time
Case Group
Control Group
Exposure
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Cohort StudiesIdentify exposed & matched unexposed patients
Look forward in time and compare on disease
Time
Exposed
Unexposed
Disease
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Timing of Cohort Studies
Time
Initial exposures Disease onset or diagnosis
PROSPECTIVE COHORT STUDY
AMBIDIRECTIONAL COHORT STUDY
RETROSPECTIVE COHORT STUDY
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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
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Statistical Association
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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
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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
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Example of Measurement Bias
Reported parental history Arthritis (%) No arthritis (%)
Neither parent 27 50
One parent 58 42
Both parents 15 8
From Schull & Cobb, J Chronic Dis, 1969
P = 0.003
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Measurement & Selection Bias
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Confounding
Association between two factors caused by third factor
For example, mortality greater in Florida than Alaska•But average age is much higher in Florida•Increased mortality results from age, rather than geography of FL
For example, transfusions associated with mortality•But patients having larger, longer operations require more blood•Increased mortality may be a consequence of larger operations
Largely prevented by randomization
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External Threats to Validity
Population of interest
Eligible Subjects
Subjects enrolled
Selection bias
Measurement biasConfounding
Chance
Conclusion
Internal validityExternal validity
???
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Study Design
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
Randomization•Best prevention for selection bias and confounding•Concealed allocation
– No a priori knowledge of randomization
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Subject Selection
Appropriate for question•Maximize incidence of desired outcome•Technically, results apply only to population studied
– Broaden enrollment to improve generalizability
As restrictive as practical; trade-off between:•Variability•Ease of recruitment
Sample-size estimates •Usually from preliminary data•Defined stopping points
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Blinding / Masking
Only reliable way to prevent measurement bias•Essential for subjective responses
– Use for objective responses whenever possible
•Careful design required to maintain blinding
Use double blinding whenever possible•Avoid triple blinding!
Maintain blinding throughout data analysis•Even data-entry errors can be non-random•Statisticians are not immune to bias!
Placebo effect can be enormous
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Importance of Blinding
Mackey, Personal communication
Chronic Pain
Analgesic
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More About Placebo Effect
Kaptchuk, PLoS ONE, 2010
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Conclusion: Good Clinical Studies…
Test a specific a priori hypothesis•Evaluate clinically important outcomes
Use appropriate statistical analysis
Make conclusions that follow from the data•And acknowledged substantive limitations
Randomize treatment & conceal allocation if possible•Best protection against selection bias and confounding
Blind patients and investigators if possible•Best protection against measurement bias
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Department of OUTCOMES RESEARCH