Enhancing the Robustness of Observational Studies Flashcards

1
Q

Statistically significant but FALSE association due to:

A

Systemic error (bias)
Other relationships
- Must consider plausibility

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2
Q

Random error

A

Statistical fluctuations related to precision of measurements

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3
Q

How to minimize error

A

Take multiple measurements and average them

Increase the same size

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4
Q

Non-random error

A

“Any tendency which prevents unprejudiced consideration of a question”

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5
Q

Bias prior to implementation

A

Design or Selection Bias

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6
Q

Define Design Bias

A

Selecting extremes leads to regression to the mean

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7
Q

Define Selection bias

A

Sampling and exposure are linked
Medical surveillance bias (unmasking bias)
Berkson bias (source pop different from general pop)
Channeling bias (prefer use of one treatment over another)

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8
Q

How to minimize Selection bias

A
Matched case-controls
Prospective ascertainment of bases with blinding
Stratification
Population-based controls
Avoid convenience
Statistics
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9
Q

Match cases and controls

A

Match on factors associated with both disease and exposure but NOT with cause

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10
Q

During implementation bias

A
Interviewer
Chronology
Recall
Transfer
Measurement
Performance
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11
Q

Define Interviewer bias

A

Leading or unanswerable questions

Min: standardize interactions, interviewer blinding

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12
Q

Define Responder bias

A

Recall (can’t remember), overstatement, consistency(how they answered previous questions influence next), and acceptance (fit expectations) bias

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13
Q

Minimize responder bias

A

Prospective design, use objective measurements

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14
Q

Define Chronology bias

A

Changes in clinical practice and treatment guidelines over time

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15
Q

Minimize Chronology bias

A

Prospective design

Avoid historical controls

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16
Q

Define Transfer Bias

A

Unequal attrition and hence unequal information deficiencies

17
Q

Minimize transfer bias

A

A priori plan for pts lost to followup

18
Q

Minimize misclassification of exposure/outcome

A

A priori definitions of exposure/outcome
Use objective measures
Prospective design

19
Q

Define Performance bias

A

Systematic differences in care/exposure factors between groups

20
Q

Minimize performance bias

A

Cluster stratify

21
Q

After implementation bias

A

Publication
Citation
Data analysis (confounding factors)

22
Q

Confounding Factors

A

Associated with both exposure and outcome

If uncontrolled, can affect validity of results

23
Q

Control Confounding factors

A
Matching
Restriction
Stratification
Modelling using multivariate tech
Randomization
24
Q

Internal Validity

A

Did you measure what you set out to measure

25
Q

External validity

A

Is what you measured in your subjects representative of the real population?

26
Q

Criteria for evaluating causality

A
Temporal sequence
Strength, consistency, specificity of association
Biological gradient and plausibility
Coherence with existing knowledge base
Experimental evidence
Analogy