bias and validity
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Bias and validityTRANSCRIPT
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Bias & Validity in clinical research
Mohammad Shihata
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Clinical Epidemiology
Definition:
“ The study of the distribution and determinants of health-related states or events in a specified population, and the application of this study to control health problems ”
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Cl inical Epidemiology
Creating Evidence
Choosing Evidence
Using Evidence
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Clinical Epidemiology Is used to:
Describe the NATURAL HISTORY of diseases
Describe CAUSALITY and ASSOCIATION between exposure and outcome
Describe the PROGNOSIS of diseasesEvaluate and compare DIAGNOSTIC tests
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Epidemiologic Studies
Compare the frequency of an OUTCOME (e.g. disease rate, death, stroke,.....) in two (or more) groups according to a characteristic of interest (e.g. smoking, surgery, drug, ......). The characteristic is called the EXPOSURE.
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Epidemiologic Studies
When the study is completed, the investigators compare the frequency of the outcome of interestbetween the study groups.
This comparison is used to describe the strength of causal relationship (association) between the exposure and the outcome.
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Association
Apparent Association could be:
TRUE: NON-CAUSAL or CAUSAL in nature.
SPURIOUS: (Alternative Explanation)
Random Error
Confounding
Bias
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Validity
Internal Validity:
Indicates a good construct of the study. If bias, confounding, and random error, are ruled out (or properly handled), the study has a good internal validity.
External Validity:
Indicates generalizability of the study results to a wider target population. (representative study sample)
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Internal Val idity( Qual ity Control )( Qual ity Control )
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Random error
Random error leads to false association between
exposure and outcome that arises from “chance”
an uncontrollable force that seems to have no
assignable cause.
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How to assess random error
Hypothesis testing is a statistical method to assess the role of random error in the observed outcome of an epidemiologic study.
- p value
- Confidence intervals
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CONFOUNDING
A systematic error due to mixing effect between an
exposure, an outcome, and an additional variable(s)
Male Male GenderGender
StrokeStroke
SmokingSmokingHTNHTNA-fibA-fib
?
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How to control for Confounding
Design stage
Randomization
Restriction
Matching
Unequal distribution of confounders between comparison groups leads to inaccurate conclusions
Analysis stage
Stratification
Adjustment (Multivariable Analysis)
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BIAS
A systematic error in the design, conduct, analysis
and/or, interpretation of a study that leads to an
erroneous association between the exposure and the
outcome.
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BIAS
The only way to control for bias is to AVOID it with careful design and conduct of the study.
If bias is present in a data set it can not be corrected or adjusted for by any “fancy” statistical test.
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BIAS
Usually does not result from a prejudiced investigator.
Can pull an association either towards or away from the null (H0).
The amount of bias can vary in its impact on the study.
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Types of Bias
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Types of Bias• Selection Bias:
• Information Bias
• Reporting Bias
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Selection BiasA systematic error due to differences in characteristics between those who take part in a study and those who do not
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Selection Bias
Examples:- Self selection bias- Referral bias - Healthy-worker effect-Attrition
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Information BiasA systematic error in measuring exposure or outcome data for the study groups.
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Information Bias
Examples:- Misclassification- Recall bias- Interviewer bias- Measurement bias- Compliance bias
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Reporting BiasSelective revealing or suppression of information
Examples:- Publication bias- Citation bias- Language bias
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Precision vs. AccuracyPrecision Accuracy
Definition The degree to which a variable has the same value when measured repeatedly
The degree to which a variable actually represents what its supposed to
Best Way to assess
Comparison among repeated measures Comparison with a reference standard
Value to Study Increase power to detect effects Increase validity of conclusions
Threatened by Random error Systematic errors
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Precision vs. Accuracy
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Conclusion
• Any epidemiologic study should be internally valid before assessing external validity.
• Random and systematic errors can threaten the validity of a study.
• Random error has no direction and can be minimized with repetition and larger sample size.
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Conclusion
• Known confounders can be controlled for during study design or analysis.
• The only way to assure equal distribution of confounders (known and unknown) is randomization.
• Bias can only be avoided to assure the validity of a study.
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Thank you