LESSON 7
VALIDITY
71 Validity
Primary objective of most epidemiologic research  obtain valid
estimate of an effect measure of interest.
In this Lesson
·
Three general types of validity problems
·
Distinguish validity from precision
·
Introduce term bias
·
How to adjust for bias.
Examples of Validity
Problems
Potential problems with studies
· Imperfections in
study design
· Imperfections in
data collection
· Imperfections in
analysis
No imperfections = Valid
Imperfections = Bias
Bias results in distortion of results
Validity versus Precision
Validity & precision  influenced by 2 different errors
·
Systematic error affects validity
· Random error affects precision.
Valid = No systematic error (i.e., unbiased)
The bull’s eye usually not known, therefore difficult to determine
extent of bias.
Precision: a lot of spread = poor
precision “imprecise”
little spread = good
precision “precise”
Precision reflects sampling variability.
A Hierarchy of Populations
v The sample  collection
of individuals from which study data have been obtained.
v
The study population  the individuals that our sample actually
represents (typically those we can feasibly study).
v
The source population  group of interest about which the
investigator wishes to assess an exposuredisease relationship.
v The external population
 group to which the study has not been restricted but to which the
investigator wishes to generalize.
We would often like to generalize our conclusions to a different external
population.
Internal versus External
Validity
Target shooting illustrates difference between internal and external
validity.
· Internal validity considers
whether or not we are aiming at the center of the target. If shooting off target, then study is not
internally valid.


Internal validity  drawing conclusions about source population based on study
population.
External validity – conceptually concerns a different target. We might imagine this external target being
screened from our vision.
External validity  applying conclusions to an external population beyond the study's
restricted interest; subjective, less quantifiable than internal validity.
72 Validity
(continued)
Quantitative Definition of
Bias
A bias can be defined quantitatively in terms of the target
parameter of interest and measure of effect actually being estimated in the
study population.
A study that is not internally valid is said
to have bias.
Target parameter  Greek letter θ (“theta”). We want to estimate the value
of θ in the source population.
θ^{0} the measure of effect in the study population.
_{} (“thetahat”) denotes
the estimate of our measure of effect obtained from the sample actually
analyzed.
Differences between _{} and θ^{0}
the result of random error.
A difference between θ^{0} and θ^{ }is
due to systematic error.
v Bias (_{},θ) = θ^{0}  θ
The not equal sign should be interpreted as a meaningful
difference from zero.
Direction of the Bias
The precise magnitude of bias can never really be quantified,
however, the direction of bias can often be determined.
· The target parameter can be overestimated
(bias away from the null)
· The target parameter can be underestimated
(bias towards the null).
Examples
Switchover bias
Positive vs. negative bias – do not worry about these terms
What Can be Done About Bias?
Three general approaches for addressing bias:
1. Design stage 
minimize or avoid bias. Avoid selection bias by including/excluding
eligible subjects, by
·
Choice of source population
·
Choice of the comparison group
2. Analysis stage 
determine presence or direction of possible bias
Also, account for confounding in analysis.
3. Publication stage 
Potential biases typically described in "Discussion" section. For selection
and information bias, is subjective, judgment expected given inherent
difficulty in quantifying biases.