system error biases in epidemiological studies fetp india
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System error
Biases in epidemiological studies
FETP India
Competency to be gained from this lecture
Prevent biases that may be avoided and understand the effect of others
Key elements
• Definition of biases• Selection biases• Information biases• Controlling the effect of biases
Misclassification
• Non-differential misclassification Error on outcome status independent from
exposure status Error on exposure status independent from
outcome status
• Differential misclassification (bias) Error influenced by outcome or exposure
status
Biases
Effect of misclassification
• Non-differential misclassification Usually reduces the strength of association
between outcome and exposure (RR or OR closer to 1)
Is not a sufficient reason to dismiss the results of a study reporting an association
• Differential misclassification (bias) May commonly increase the strength of the
association May also reduce the strength of the
associationBiases
Definition of a bias
Systematic error in the protocol of a study that leads to a distortion of measurement
affecting internal validity
How to understand the term systematic?• Error systematically done in the same way
Differential misclassification
• Error of the system Built in misconception of the protocol
Biases
Types of biases
• Selection biases Biases in the way subjects enter a study
• Information biases Biases in the way information is collected
after inclusion in a study
Biases
Level of operation of biases
Systemat ic recruitmentof cases / controls
w ith specifi c exposure status
Systemat ic col lect ion ofinformat ion leaning towards
specifi c exposure status
Col lect ion of informat ionabout exposure
Inclusionon the basis of outcome
Case control
Systemat ic recruitmentof exposed / unexposed
with specifi c r isk of outcome
Systemat ic col lect ion ofinformat ion leaning towards
specifi c outcome status
Col lect ion of informat ionabout outcome
Inclusionon the basis of exposure
Cohort
Type of study
Selection biases
Information biases
Biases
Selection biases
Type of studyInclusion into the
studySources of
selection biases
Case control•On the basis of outcome
•Selection of cases or controls with specific exposure status
Cohort•On the basis of exposure
•Selection of exposed or unexposed with specific risk
Selection biases
Examples of selection biases in a cohort study
• Subjects selected on the basis of exposure• Outcome not independently distributed
among exposed and unexposed Systematic selection of exposed subjects with:
• Higher risk of outcome • Lower risk of outcome
Systematic selection of unexposed subjects with:• Higher risk of outcome • Lower risk of outcome
A b
c d
a B
c d
a b
c D
a b
C d Selection biases
Examples of selection biases in a case control study
• Subjects selected on the basis of outcome• Exposure not independently distributed
among cases and controls Systematic selection of case-patients with:
• Higher frequency of exposure • Lower frequency of exposure
Systematic selection of control-subjects with:• Higher frequency of exposure • Lower frequency of exposure
A b
c d
a b
C d
a b
c D
a B
c d Selection biases
Sources of selection biases
• Surveillance• Screening / diagnosis• Admission to health care facilities • Selective survival• Non-response / loss to follow up
Selection biases
Sources of selection biases
• Surveillance Systematic notification of cases exposed
• Screening / diagnosis• Admission to health care facilities• Selective survival• Non-response / loss to follow up
Selection biases
Sources of selection biases
• Surveillance• Screening / diagnosis
Systematic case search among exposed
• Admission to health care facilities• Selective survival• Non-response / loss to follow up
Selection biases
Sources of selection biases
• Surveillance• Screening / diagnosis• Admission to health care facilities
Systematic admission of:• Case-patients exposed / unexposed• Control-subjects exposed / unexposed
• Selective survival• Non-response / loss to follow up
Selection biases
Sources of selection biases
• Surveillance• Screening / diagnosis• Admission to health care facilities • Selective survival
Systematic inclusion of cases who survived and who may be more or less exposed
• Non-response / loss to follow up
Selection biases
Sources of selection biases
• Surveillance• Screening / diagnosis• Admission to health care facilities • Selective survival• Non-response / loss to follow up
Systematic inclusion of subjects more likely to participate who may be:• More or less exposed• More or less at risk
Selection biases
Information biases
Type of studyCollection of information
Sources of information biases
Case control •About exposure
•Collection of information leaning towards specific exposure status
Cohort •About outcome
•Collection of information leaning towards specific outcome status
Information biases
Examples of information biases in a cohort study
• Subjects selected on the basis of exposure• Outcome not independently measured
among exposed and unexposed Measurement of a higher incidence of outcome:
• Among exposed• Among unexposed
Measurement of a lower incidence of outcome:• Among exposed• Among unexposed
A b
c d
a b
C d
a b
c D
a B
c d Information biases
Examples of information biases in a case control study
• Subjects selected on the basis of outcome• Exposure not independently measured
among cases and controls : Measurement of a higher frequency of exposure:
• Among cases• Among controls
Measurement of a lower frequency of exposure:• Among cases• Among controls
A b
c d
a B
c d
a b
c D
a b
C d Information biases
Sources of information biases
• Recall • Investigator• Data quality• Prevarication
Information biases
Sources of information biases
• Recall Cases may recall exposure more than
controls
• Investigator• Data quality• Prevarication
Information biases
Sources of information biases
• Recall • Investigator
Systematic collection of information supporting expected conclusions• Unconsciously• Consciously
May be examined in the analysis
• Data quality• Prevarication
Information biases
Sources of information biases
• Recall • Investigator• Data quality
Better exposure data available on cases Better outcome data available on exposed
• Prevarication
Information biases
Sources of information biases
• Recall • Investigator• Data quality• Prevarication
Systematic distortion of the truth by subjects
Information biases
Other limitations also referred to as biases
• Intervention biases Non-specific effect of interventions in a trial
• Analysis biases Non-systematic statistical analyses
• Interpretation biases Investigators with pre-conceived ideas
• Publication biases Negative studies do not get published
Checklist to prevent biases when preparing a protocol (1/2)
1. Is the sample representative? Are controls representative of the population
from which cases are drawn?
2. Are the exposure and outcome criteria: Standardized? Specific? Clear? Well measured? Applied consistently by trained field
workers?Controlling biases
Checklist to prevent biases when preparing a protocol (2/2)
3. Could exposure status affect the probability to detect the outcome?
4. Could outcome status affect the probability to recall the exposure?
5. What will be the proportion of response?
6. What will be the proportion of “lost to follow up”?
Controlling biases
Discussing biases critically
• Review critically selection and information biases that are likely to have operated
• Review the potential effect of each of these biases (e.g., under- or overestimation)
• Review what can be done to address these biases a posteriori (e.g., analysis plan)
• Analyze possible summary effect of all these biases on the final results
• Interpret data in light of this summary effect
Controlling biases
Take-home messages
• Have a strong design that prevents biases
• Examine how subjects enter a study• Collect information in a standardized
way• Prevent the biases you can avoid,
understand those you cannot avoid
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