STAR - PowerPoint PPT Presentation

1 / 40
About This Presentation
Title:

STAR

Description:

STAR - bias and confounding. 1. STAR. Bias and Confounding. Knut Borch-Johnsen ... as the observed effect, provided that the nill-hypothesis (no effect) is true ... – PowerPoint PPT presentation

Number of Views:266
Avg rating:3.0/5.0
Slides: 41
Provided by: kbjo
Category:
Tags: star | nill

less

Transcript and Presenter's Notes

Title: STAR


1
STAR
  • Bias and Confounding
  • Knut Borch-Johnsen

2
Epidemiology Natural History
  • Birth Genetically determined (often unknown)
    variability
  • of susceptibility
  • Exposure Environment
  • (family, social environment, macro
    environment
  • occupation etc.) (rarely objective)
  • Disease Diagnostic threshold
  • (not objective)
  • Death Heterogeneity with respect to lethality
    (unknown)
  • Cause of death Heterogeneity
  • Autopsy Heterogeneity

3
Where do we get the evidence from?
  • Epidemiology/observational studies
  • Intervention studies
  • Structured
  • unstructured experiments

4
INTERVENTION STUDIES Clinical trials 
  • RANDOMIZED (DOBBELT BLIND)
  • CONTROLED CLINICAL TRIAL
  •  
  • RANDOMIZED SINGLE-BLIND CONTRILED CLINICAL TRIAL
  • OPEN TRIAL
  •  
  •  
  • AIM
  • To Compare the effect of different treatment
    regiments

5
RCCT
  • Blinding with respect to exposure
  • Allocation by chance
  • Control for unknown prognostic factors (versus
    stratification)
  • "Simple" interpretation of results

6
RCCT vs. Epidemiology
7
Some key problems in epidemiology
  • Study population
  • Methods
  • Measurements
  • Multifactorial diseases
  • Can all risk factors be measured

8
Validity of the association
  • Due to chance
  • Bias
  • Confounding

9
Did this occur by chance Statistical test
p-value/confidence intervals
  • Retinopathy in sample 60 in males 40 in
    females
  • How to test this what do you need ?

10
Did this occur by chance Statistical test
p-value/Confidence Intervals
  • Retinopathy in sample 60 in males 40 in
    females
  • The probability of observing en effect at least
    as extreme as the observed effect, provided that
    the nill-hypothesis (no effect) is true

11
Sample size
Each collor is one socioeconomic group How many
needed in the sample to obtain a valid estimate?
12
2 minutes
13
Types of problems
  • Bias
  • Any systematic error in data collection an
    epidemiological study that results in an
    incorrect estimate of the association between
    exposure and risk of disease
  • Confounding
  • Mixing of the effect of the exposure under study
    on the disease with that of a third factor
    associated with the exposure and independent of
    that exposure be a risk factor for the disease

14
Bias
  • Any systematic error in en epidemiological study
    that results in an incorrect estimate of the
    association between exposure and risk of disease

15
Bias
  • Selection bias
  • Observation or information bias

16
Selection bias
  • Relates to selection of study-population
  • Descriptive studies
  • Sample representative for the population
  • Analytical studies/C-C studies
  • Study populations from the same populations

17
Descriptive studies
Sampling strategy Representative sample
18
Descriptive studies
Sampling strategy Representative sample HOW TO
MAKE A REPRESENTATIVE SAMPLE
19
2 minutes
20
Descriptive studies
  • Prevalence of complications among patients with
    type 2 diabetes
  • Screened population
  • Population based sample
  • Primary care
  • Secondary care
  • Tertiary care

21
Descriptive studies
  • Representative ness of sample
  • Population based sampling
  • Responders
  • Non-responders
  • Non-responders differs with respect to
  • Socioeconomic status
  • Morbidity
  • Mortality
  • Life style

22
Selection biasprobability of sampling
  • Will all with the disease have the same
    probability of being diagnosed/sampled
  • Women and gallstones
  • Men and gastric/duodenal ulcers
  • Asbestos exposure and COLD/Cancer

23
Selection biasprobability of sampling
  • Will all with the disease have the same
    probability of being diagnosed/sampled
  • Women and gallstones
  • Men and gastric/duodenal ulcers
  • Difference in diagnostic threshold
  • Asbestos exposure and COLD/Cancer
  • Economic incentive for diagnosis
  • Organic solvents and dementia
  • Asbestos and COLD/Cancer

24
Selection bias analytical studies
  • Case-control studies
  • Oral contraceptives and thromboembolism
  • Hospital based C-C studies
  • Doctors aware of the possible link
  • Women with symptoms and using OC more likely to
    be hospitalised
  • Leads to selection bias

25
Observation/information bias
  • Is a consequence of systematic differences in the
    way data on exposure or outcome are obtained from
    the various study groups
  • Recall bias
  • Interviewer bias

26
Recall bias
  • The diseased individual remember and report their
    previous exposure experience differently from
    non-diseased
  • Or
  • The exposed individual reports events differently
    from unexposed

27
Recall bias
  • Birth defects among laboratory technicians
    working with organic solvents OS
  • Case-control study
  • Case birth defect, control normal child
  • Exposure self reported exposure to OS
  • C-C-study OR gt 1.5 (plt0.01)
  • Cohort study RR 1.02 (ns)
  • WHY

28
2 minutes
29
Interviewer bias
  • Soliciting, recording or interpretation of
    information may differ between cases and controls

30
Interviewer bias - solutions
  • Blinding of interviewer
  • Structured interviews
  • Interview guides
  • dummy questions (exposures known to be
    unrelated to condition under study)

31
Question
  • Compare and contrast the likelihood of selection
    and observation/information bias in case-control
    and cohort study

32
2 minutes
33
Confounding
  • Mixing of the effect of the exposure under study
    on the disease with that of a third factor
    associated with the exposure and independent of
    that exposure be a risk factor for the disease

34
CONFOUNDING
CONFOUNDER
DISEASE
STUDY FACTOR
35
Confounder characteristics
  • Associated to exposure
  • Risk factor in it self
  • If 1 not 2 Intermediate variable
  • If 2 not 1 Independent Risk Factor

36
Confounders
  • Diabetes and macrovascular disease
  • Hypertension
  • Dyslipidaemia
  • Smoking
  • Low physical activity

37
Bias and Confoundingwhat to do ?
  • Bias
  • In general terms error in data leading to
    incorrect estimation of association between
    exposure and outcome
  • Solution improve data collection
  • Confounding
  • In general terms incorrect estimation of
    association between exposure and outcome due to a
    third factor associated to exposure and disease
  • Solution restriction matching stratification
    multivariate analysis

Bias design problem Confounding analysis
problem
38
Other important terms
  • Misclassification
  • By exposure
  • By event
  • Systematic
  • Random

39
Other important terms
  • Misclassification
  • By exposure often systematic (recall bias)
  • By event often systematic (by exposure)
  • Systematic any result possible
  • Random underestimates the effect

40
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com