3. Objectives
At the end of this session each student should be able
to;
Define error
Mention sources of error
Describe types of error
State the ways of minimizing error
4. Definition
Error
Defined as a mistake which can occur during the study.
Sources of error
• Selective survival
• Selective recall
• Incorrect classification of subjects with regard to their
disease and/or exposure status.
5. Types of error
Random error
Systematic error
A. random error
Define as that part of our experience that we cannot
predict.
From a statistical perspective, random error can also
be conceptualized as sampling variability .
6. The major strategies for reducing random error are:
Increase sample size
A larger sample, other things being equal, will yield
more precise estimates of population parameters .
Improve sampling procedures
A more refined sampling strategy .
Reduce measurement variability by using strict
measurement protocols, or averages of multiple
measurements.
7. Use more statistically efficient analytic methods
Statistical procedures vary in their efficiency, i.e., in the
degree of precision obtainable from a given sample size.
B. Systematic error (bias)
Is a difference between an observed value and the true
value due to all causes other than sampling variability.
It can arise from different sources, including factors
involved in the choice or recruitment of a study
population and factors involved in the definition and
measurement of study variables
8. Types of bias
I. Selection bias
Occurs as a result of errors in identifying the study
population.
Sources of selection bias
Sampling bias
Systematically excluding or over-representing certain
groups.
9. Allocation bias
Systematic differences in the way which subjects are
recruited into different groups for a study.
For example, a study may fail to use random
sampling, the first 20 patients who arrive at a clinic
are allocated to a new treatment, and the next 20
patients are allocated to an existing treatment.
However, the patients who arrive early may be better-
off, on the other hand the doctor may have asked to
see the most seriously ill patients first.
10. Minimizing selection bias
• Clear definition of study population
• Choose cases and controls from same population
• Selection of exposed and non-exposed without
knowing disease status.
11. II. Information bias (also called misclassification bias)
Is caused by systematic differences in data collection,
measurement or classification.
Sources of Information bias
Recall bias
People suffering from a disease may have spent more
time thinking of possible links between their past
behavior and their disease than no sufferers.
12. Interviewer bias.
Interviewers may phrase questions differently for
different subjects, or write down their own
interpretations of what subjects have said.
Follow-up bias
In studies that follow up subjects at intervals, people
from certain groups may tend to be lost to follow-up,
or a disproportionate number of exposed subjects may
be lost to follow-up compared with non-exposed
subjects.
13. Validity
Is the extent to which a measurement measures what it
is supposed to measure.
• Internal validity refers to absence of systematic error
that causes the study findings (parameter estimates) to
differ from the true values as defined in the study
objectives.
• External validity refers to the extent to which a study's
findings apply to populations other than the one that
was being investigate.
15. For example, a study designed to estimate the
prevalence of smoking in a population may select
subjects for interview in a number of locations.
If the interviews are only conducted on weekdays, the
study is likely to under-represent people who are in
full-time employment, and include a higher
proportion of those who are unemployed, off work or
mothers with children.
16. Recording bias.
Medical records may contain more information on
patients who are 'cases‘.
Minimizing information bias
• Standardise measurement instruments
• Administer instruments equally to cases and controls
(exposed/unexposed)
• Use multiple sources of information
– Questionnaires
– Case records
17. Confounding
Confounding occurs when a separate factor (or
factors) influences the risk of developing a disease,
other than the risk factor being studied.
To be a confounder, the factor has to be related to the
exposure, and it also has to be an independent risk
factor for the disease being studied. (Third variable
problem).
18. To be a confounding factor, 2 conditions must be met
For example, if a study assesses whether high alcohol
consumption is a risk factor for coronary heart disease,
smoking is a confounding factor (also called a
confounder) .
This is because smoking is known to be related to
alcohol consumption, and it is also a risk factor for
coronary heart disease.
20. Conclusion
Identification of possible bias is a difficult exercise
but is crucial to improve validity. Bias can’t usually
be totally eliminated.
It must be to keep it to a minimum, to identify those
biases that cannot be avoided, to assess their potential
impact and to take this into account when interpreting
the results.
21. References
Schoenbach, J. V. (2001). Sources of error.
Stewart, A. (2002). Basic statistics and epidemiology.
A practical guide.
22. Validity
Is the extent to which a measurement measures what it
is supposed to measure.
• Internal validity refers to absence of systematic
error that causes the study findings (parameter
estimates) to differ from the true values as
defined in the study objectives.
• External validity refers to the extent to which a study's
findings apply to populations other than the one that
was being investigate.
