Lecture 4 Study design and bias in screening and diagnostic tests - PowerPoint PPT Presentation

Loading...

PPT – Lecture 4 Study design and bias in screening and diagnostic tests PowerPoint presentation | free to download - id: 6836e9-MTJiO



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Lecture 4 Study design and bias in screening and diagnostic tests

Description:

Lecture 4 Study design and bias in screening and diagnostic tests Sources of bias : spectrum effects/subgroup analyses verification/workup bias – PowerPoint PPT presentation

Number of Views:16
Avg rating:3.0/5.0
Slides: 22
Provided by: JaneMc60
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Lecture 4 Study design and bias in screening and diagnostic tests


1
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

2
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?

3
Reducing bias
  • Studies of diagnostic tests give variable results
  • Biassed studies generally overestimate
    sensitivity/specificity
  • STARD criteria proposed to improve quality of
    these studies

4
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

5
Design implications
  • Investigate test performance in sub-groups
  • Report characteristics of study population

6
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?

7
Example of verification/work-up bias
  • VQ (ventilation/perfusion scanning to detect
    pulmonary embolism
  • Positive scan -gt angiography
  • Studies with selective referral of patients
    sensitivity 58
  • Study (PIOPED) with prospective investigation of
    all patients sensitivity 41

8
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)

9
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

10
Optimal design
  • Cohort vs case-control?
  • Prospective cohort with blind evaluation
  • Case-control
  • Sources of bias?

11
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 follow-up)
  • Methods?

12
RESULTS ISAR development
  • Adverse health outcome defined as any of
    following during 6 months after ED visit
  • gt10 ADL decline
  • Death
  • Institutionalization

13
(No Transcript)
14
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

15
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)

16
(No Transcript)
17
Predictive validity of ISAR scale
  • AUC and 95 CI
  • Overall (n1673) 0.71 (0.68 0.74)
  • Admitted to hospital (n509) 0.66 (0.61 0.71)
  • Discharged (n 1159) 0.70 (0.66 0.74)
  • Similar results by informant (patient vs proxy)
  • Next steps?

18
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?

19
(No Transcript)
20
(No Transcript)
21
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
    medication-based measures from administrative
    data)
  • AUC values of 0.58-0.60 for emergency
    hospitalization (Schneeweiss, 2001)
About PowerShow.com