LESSON 8
SELECTION BIAS
81 Selection
Bias
Selection bias  systematic error in how subjects are selected
into a study or loss of subjects
Selection Bias in Different
Study Designs
Examples of selection bias provided –
· Casecontrol example
– Ltryptophan and EMS, publicity may have increased likelihood that those
taking Ltryptophan would be diagnosed/reported
· Clinical trial
example – loss to followup differs in one treatment group compared to another
· Crosssectional
example – selective survival
Example of Selection Bias in
Cohort Studies
· The primary sources of such
selection bias are losstofollowup, withdrawal or nonresponse.
Consider table that describes the fiveyear followup for disease in a
population.
10% random sample from this population:
RR = 3.0; no bias
Assume initial cohort obtained from 10% sampling. However:
· 20% of exposed persons lost
to followup
· 10% of unexposed persons are
lost
· assume lost to followup not
affected by disease status
RR = 3; no bias
Suppose that a different pattern of lossto
followup results in the twoway table shown here.
Other Examples
v Selection bias – socalled
“healthy worker effect”  workers tend to be healthier than general population.
v Selection bias  using
volunteers, may have different characteristics from persons who do not
volunteer.
v Clinicbased studies
frequently suffer from selection bias because their patients tend to have more
severe illness than persons in a populationbased sample.
82 Selection
Bias (continued)
Selection Ratios and
Selection Probabilities
1. What are alpha, beta, gamma,
and delta for these data?
alpha
α = 12/150 = .08
beta
β = 4.5/50 = .09
gamma
γ = 88/9850 = .08
delta
δ = 895.5/9950 = .09
Selection
Probabilities


E 
_{} 


D 
α= 12/150 = .08 
β= .09 


_{} 
γ= .08 
δ=.09 

2. Is there selection bias in
either the odds ratio or the risk ratio?
No
3. Crossproduct of selection
probabilities?
_{}
OR=3.03 _{}=4.00


E 
_{} 


D 
α= .093 
β= .08 


_{} 
γ= .08 
δ=.09 

_{}
Why is this important? Need to
think about these probabilities in a study and how they would impact the
results.
Quantitative Assessment of
Selection Bias
Selection bias and the odds ratio.
Estimate of _{} is biased when study
population OR^{o} is meaningfully different from source
population OR.
Rule for assessing selection bias in the odds ratio
The bias is either > 0, = 0, or < 0 depending on whether the cross
product ratio of selection probabilities is > 1, = 1, or < 1.
Selection Bias for Risk
Ratio
The crossproduct of selection
ratios [(αδ)/(βγ)]
used for the OR can be used to assess bias for the RR when disease is
rare.
Works fine in this example:
_{}
Not so well in this example:
_{}
Some examples of Selection
given for CaseControl, Cohort, and CrossSectional studies
84 Selection
Bias (continued)
What Can Be Done About
Selection Bias Quantitatively?
Let’s not worry about the
math in this section.
If selection probabilities or
ratio of certain selection probabilities can be estimate, it is possible to
calculate an “adjusted” or “corrected” odds ratio and/or risk
ratio.
What Can Be Done About
Selection Bias Qualitatively?
Selection probabilities
usually not known
Prevent/minimize selection
bias when designing study
Casecontrol studies – select controls
representative of source that produced cases is an important issue. Some may select 2 or more control groups.
To minimize selection bias
in casecontrol studies:
Use incident cases (rather than prevalent)
Use populationbased design rather than hospitalbased
To minimize selection bias in cohort studies
High response
Minimize
losstofollowup
In Discussion section of manuscript, address possible selection bias