Transcript Design and Analysis of Clinical Study 2. Bias and Confounders Dr. Tuan V.

Design and Analysis of Clinical Study
2. Bias and Confounders
Dr. Tuan V. Nguyen
Garvan Institute of Medical Research
Sydney, Australia
Biases & Confounding
• Bias means “difference from the truth”
• There are 3 types of bias:
– Selection bias
– Information bias
– Confounding
Selection Bias
• Non-representativeness
– Patients referred for specialist care are different from those in the
community
– Used hospitalized smokers as the exposed and healthy volunteer
non-smokers as the unexposed
• Migration bias.
– People with chronic lung disease tend to move out of urban areas;
those with psychiatric problems seek the anonymity of cities
• High dropout rates.
– Those who drop out of a study tend to be different from those
continuing
Selection bias - Berkson
(a) General population – odds ratio = 1.06
Diseases of bones and joints
Total
Respiratory
disease
Yes
No
Yes
17
207
234
No
184
2376
2560
(b) Hospitalized population – Odds ratio = 4.06
Diseases of bones and joints
Respiratory
disease
Total
Yes
No
Yes
5
15
20
No
18
219
237
Ref: Roberts RS, et al. J Chron Dis 31:119-28
“Bias by Indication”
• Whenever we compare a group of patients who use a drug to
those who don’t in a non experimental observational study (cohort,
not randomized).
• The 2 groups differ in many respects: “Bias by indication”.
• Comparison of hypertensive patients who are on minoxidil or
hydralazine and those on other agents:
– That patients on those agents have higher BP
– Is it because they don’t work as well ?
– No, the opposite. They are reserved for those with severe
resistant hypertension.
• That is the indication for those agents.
“Survivor Treatment Bias”
• Patients who received statin during admission for MI had
much lower in-hospital mortality.
– Statin?
• The ones who died are different.
– Some died very soon after admission (no statin).
– Some were so sick that they were treated with multiple
drugs, modalities, ICU etc.
– No statin
Information Bias
• Response Bias occurs when subjects give inaccurate
responses.
• Measurement Bias occurs when instruments are faulty
• Observer error
• A process tends to show improvement when being
observed. (Hawthorne Effect)
Confounders
• Confounders act by being associated with both a risk
factor and outcome in a way that makes the two seem
related.
Poor
Maternal
Nutrition
Low Birth
Weight
Low
Socioecono
mic Class
Example of Confounder - Sex
Treatment
Outcome
Poor
Good
New Rx
30
1970
Standard Rs
150
1850
Males
Females
Treatment
RR = 0.2
Outcome
Poor
Good
New Rx
20
980
Standard Rs
50
950
Treatment
RR = 0.4
Outcome
Poor
Good
New Rx
10
990
Standard Rs
100
900
RR = 0.1
Strategies for Reducing Biases
• Have clear and precise definitions (e.g. for cases;
controls;exposure;criteria for inclusion/exclusion)
• “Blinding” where appropriate
• Reduce measurement error by ‘quality control”
• Careful check of study design; choice of subjects;
ascertainment of disease and exposure;planning of
questionnaires; methods of data collection.
How to Deal with Confouders 1
• Think about possible confounders at the design stage, and
gather data on all possible confounders.
• A quick test about a possible confounder is to check
whether it is unevenly distributed between study and
comparison groups.
• Suspect confounding if the odds ratio gets altered after
adjusting for another factor.
How to Deal with Confouders 2
• Design stage
– Strict inclusion criteria
– Matching
– Randomization
• Analysis stage
– Do analysis by adjusting for several strata of the
confounding variable
– Multiple regression analysis
How to Check for Confouders
• First calculate Odds Ratio for the exposure
variable.
• Next calculate odds ratio for different strata of the
confounding variable
• If the odds ratios are not materially different then there is
no confounding.
Validity
• Are the conclusions true?
• Common threats to validity
–
–
–
–
–
–
Selection bias
Measurement bias
Differential loss of subjects
Confounders
Unexpected events
Hawthorne effect
How to Ensure Validity
• Have a control group. Helps against confounding, unexpected
events, Hawthorne effect.
• Random assignment of subjects to different groups.
• Before / After measurements.
• Carefully prepared research designs.
• Quality control of equipment
• Knowledge of environmental events especially if the study is of long
duration.
• Unobtrusive methods of observation.
Cause-and-Effect Relationship
Strength of Research Design is most important
1. Well - conducted randomized controlled trials
(adequate sample size; blinding; standardized
methods of measurement and analysis)
2. Cohort studies - next best
(minimize selection & measurement bias; check for
confounders)
Evidence for cause-and-effect
• Temporal sequence (cause
must precede effect)
• Reversible association
(removal of cause decreases
risk)
• Strength of association
(Relative risk or odds ratio)
•
• Dose-Response
relationship
Consistency (several
studies come up with same
findings)
• Biological plausibility
• Specificity
• Analogy
Flow chart for cause-and-effect inference
Association (O.R. R.R. Pearson’s r)
Yes
Bias
Not likely
Chance
Excluded
Error
No
CAUSE
Possible
No
Likely
Possible