Transcript No Slide Title

Lecture 4
Study design and bias in
screening and diagnostic tests
• Sources of bias :
– spectrum effects/subgroup analyses
– verification/workup bias
– information (review) bias
• Critical assessment of studies:
– e.g., STARD criteria
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Bias
• What is it?
– Bias in a measurement vs bias in the result of a
study
• Selection vs information bias
• What does it mean in studies of screening
and diagnostic tests?
• Difference between bias and effect
modification?
2
Reducing bias
• Studies of diagnostic tests give variable
results
• Biassed studies generally overestimate
sensitivity/specificity
• STARD criteria proposed to improve
quality of these studies
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Spectrum effect: bias or
modification?
• Sensitivity and specificity are not innate
characteristics of a test, but vary by study
population
– e.g., by age, sex, comorbidity
– e.g, exercise stress testing: worse performance
in women than men
– Study population should be representative of
population in which test will be used
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Design implications
• Investigate test performance in sub-groups
• Report characteristics of study population
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Verification/work-up bias
• Results of test affect intensity of subsequent
investigation
– e.g., risky or expensive follow-up
• Selection or information bias?
• E.g. Exercise stress test and angiography
– effects?
– solutions?
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Example of verification/work-up
bias
• VQ (ventilation/perfusion scanning to
detect pulmonary embolism
– Positive scan -> angiography
– Studies with selective referral of patients:
sensitivity = 58%
– Study (PIOPED) with prospective investigation
of all patients: sensitivity = 41%
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Information/review bias
• Examples:
– Diagnosis is not blind to test result
– Diagnosis is made with access to other clinical
information
– Knowledge of results of follow-up used in
interpretation of screening test
• Effects?
• Solutions?
– (NB: raw test performance vs “real-world”
situation)
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Other sources of bias
• Indeterminate test results:
– How do they affect results?
– Solutions?
• Context:
– Interpretation varies with changes in disease
prevalence
• Criteria for positivity
– Technical advances, operator experience
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Optimal design
• Cohort vs case-control?
• Prospective cohort with blind evaluation
• Case-control:
– Sources of bias?
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Example for discussion
• Seniors in emergency department (ED):
– High risk of functional decline, death etc.
– Needs usually not recognized at ED visit
• Objective: Development and validation of
tool to identify “high-risk” seniors in ED
(need more careful assessment and followup)
• Methods?
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RESULTS: ISAR development
Adverse health outcome defined as any of
following during 6 months after ED visit
• >10% ADL decline
• Death
• Institutionalization
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A d ve rs e
%
6 0
5 0
D is c h a r g
Ad m itte d
4 0
3 0
2 0
1 0
0
AD
L
D d
e
In
e
as
c
th
An
t
lin
itue
y
ti
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Scale development
• Selection of items that predicted all adverse
health events
• Multiple logistic regression - “best subsets”
analysis
• Review of candidate scales with clinicians
to select clinically relevant scale
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Identification of Seniors At Risk
(ISAR)
1. Before the illness or injury that brought you to the Emergency, did you
need someone to help you on a regular basis? (yes)
2. Since the illness or injury that brought you to the Emergency, have you
needed more help than usual to take care of yourself? (yes)
3. Have you been hospitalized for one or more nights during the past 6
months (excluding a stay in the Emergency Department)? (yes)
4. In general, do you see well? (no)
5. In general, do you have serious problems with your memory? (yes)
6. Do you take more than three different medications every day? (yes)
Scoring: 0 - 6 (positive score shown in parentheses)
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A ny
a d ve
%
8 0
D is c h a r g e
Ad m itte d
6 0
4 0
2 0
0
0
1
2
3
4
ISAR
5 - 6
SC
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O
Predictive validity of ISAR scale
• AUC and 95% CI
– Overall (n=1673): 0.71 (0.68 – 0.74)
– Admitted to hospital (n=509): 0.66 (0.61 –
0.71)
– Discharged (n= 1159): 0.70 (0.66 – 0.74)
– Similar results by informant (patient vs proxy)
• Next steps?
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Second study
• Multi-site randomized controlled trial of a
2-step intervention using ISAR + nurse
assessment/referral
• Study 2 population had lower % ISAR +ve
than study 1 population
– implications for sensitivity, specificity, AUC,
LR, DOR?
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Area Under the curve (AUC) for concurrent validity criteria
Detection of depression
at baseline
Study 2
Severe functional
impairment
OARS: Study 1
OARS: Study 2
SMAF: Study 1
0.5
0.6
0.7
0.8
AUC (95% confidence interval)
0.9
1.0
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Area Under the Curve(AUC) for predictive validation criteria
among patients discharged from ED
Adverse health outcome
Study 1
Increase in depressive
symptoms
Study 2
10+ community health
center visits/5 months
Study 2
11+ hospital days/ 5 months
Study 1
Study 2
2+ ED visits/ 5 months
Study 1
Study 2
0.5
0.6
0.7
0.8
0.9
1.0
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AUC (95% confidence interval)
Other predictive measures in elderly
• Pra screening tool (widely used in US HMOs):
– AUC values of 0.61 - 0.71 for prediction of
hospital utilization or functional decline
(Coleman, 1998)
• Hospital Admission Risk Profile (HARP)
– AUC of 0.65 for prediction of nursing home
admission (Sager, 1996)
• Comorbidity indices (diagnosis and medicationbased measures from administrative data):
– AUC values of 0.58-0.60 for emergency
hospitalization (Schneeweiss, 2001)
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