1. BIASES AND ERRORS
Dr ARUN RAJ GR MD(Ped, Ayu), PDCR, MIPHA, FIAMR, PGDMRCH
Assistant Professor, Dept. of Kaumarabhritya
SDM College of Ayurveda & Hospital, Hassan, Karnataka
Email: drdrarunraj26@gmail.com; drarunraj@sdmcahhassan.org (work)
Ph: 9886292826
2. ERROR: Definitions
A false or mistaken result obtained in a study or
experiment
Random error is the portion of variation in
measurement that has no apparent connection to any
other measurement or variable, generally regarded as
due to chance
Systematic error which often has a recognizable
source, e.g., a faulty measuring instrument, or pattern,
e.g., it is consistently wrong in a particular direction
(Last)
3. These errors are generally produced by
one or more of the following:
• RANDOM ERROR
• RANDOM MISCLASSIFICATION
• BIAS
• CONFOUNDING
4. Cont….
Random error
• Deviation of results and inferences from the truth,
occurring only as a result of the operation of chance.
Can produce type 1 or type 2 errors.
Random (Non Differential Classification )
Misclassification
• Random error applied to the measurement of an
exposure or outcome. Errors in classification can
only produce type 2 errors, except if applied to a
confounder or to an exposure gradient.
5. Bias
• Error in design or execution of a study, which produces results that
are consistently distorted in one direction because of nonrandom
factors.
• Bias can produce either a type 1 or a type 2 error, but we usually
focus on type 1 errors due to bias.
Confounding
• It is defined as one which is associated with both the
exposure and the diseases, and is unequally distributed in
the study and the control groups
Bias can occur in RCTs but tends to be a much greater problem in
observational studies.
6. Bias and Confounding
• Bias creates an association that is not true, but
confounding describes an association that is
true, but potentially misleading.
7. Relationship b/w Bias and Chance
Chance
Bias
Diastolic Blood Pressure (mm Hg)
80 90
True BP
(intra-arterial cannula)
BP measurement
(sphygmomanometer)
No.
of
obs
erva
tion
s
9. Validity
The degree to which a measurement measures
what it purports to measure
(Last)
Degree to which the data measure what they
were intended to measure – that is, the results of
a measurement correspond to the true state of
the phenomenon being measured
(Fletcher)
Also known as ‘Accuracy’
10. Reliability
The degree of stability expected when a measurement is
repeated under identical conditions; degree to which the
results obtained from a measurement procedure can be
replicated
(Last)
Extent to which repeated measurements of a stable
phenomenon – by different people and instruments, at
different times and places – get similar results
(Fletcher)
Also known as ‘Reproduciblity’ and ‘Precision’
12. Types of bias
• Selection bias is a distortion in the estimate of association
between risk factor and disease that results from how the
subjects are selected for the study.
• Information bias is due to systematic measurement error or
misclassification of subjects on one or more variables, either
risk factor or disease status.
• Confounding -results when the risk factor being studied is so
mixed up with other possible risk factors that its single effect is
very difficult to distinguish.
13. Selection bias
Non-response bias occurs because individuals who do
not respond to a call to participate in research studies are
generally different from those who do respond.
Hospital admission rate bias- a selection bias that rears
its head when hospital-based studies, especially case–
control studies, are undertaken.
Berksonian bias - the problem is that hospitalized individuals
are more likely to suffer from many illnesses, as well as more
severe illnesses, and engage in less than healthy behaviors.
14. Cont…
Exclusion bias occurs when in certain circumstances
epidemiologic studies exclude participants to prevent
confounding.
Publicity bias (awareness bias) occurs when media
attention is drawn to a particular illness.
If the exclusion criteria are different for cases and controls or
different for the exposed and non-exposed, an exclusion bias may
be introduced.
Publicity bias can occur from snippets of celebrities or news
reports not related to individuals.
15. Information Bias
Interviewer Bias – an interviewer’s knowledge may
influence the structure of questions and the manner of
presentation, which may influence responses
Recall Bias – those with a particular outcome or
exposure may remember events more clearly or amplify
their recollections
16. Cont…
Observer Bias – observers may have preconceived
expectations of what they should find in an examination
Loss to follow-up – those that are lost to follow-up or
who withdraw from the study may be different from those
who are followed for the entire study
Reporting bias- occurs when a case emphasizes the
importance of exposures that he or she believes to be
important.
17. Cont…
• Hawthorne effect – an effect first documented at a
Hawthorne manufacturing plant; people act differently if
they know they are being watched
• Surveillance bias – the group with the known
exposure or outcome may be followed more closely or
longer than the comparison group
18. Lead time bias
• Lead time is the length of time between the
detection of a disease (usually based on new,
experimental criteria) and its usual clinical
presentation and diagnosis (based on traditional
criteria).
• It is the time between early diagnosis with screening
and the time in which diagnosis would have been
made without screening.
• It is an important factor when evaluating the
effectiveness of a specific test.
19. Lead time bias occurs if testing increases the perceived
survival time without affecting the course of the disease
20. How lead time affects survival time
Diag
Diag
Diag
Unscreened
Screened –
Early T/t not effective
Screened –
Early T/t is effective
Onset of Ds Death Survival after
diagnosis
21. Confounding
When another exposure exists in the study population
(besides the one being studied) and is associated both with
disease and the exposure being studied. If this extraneous
factor – itself a determinant of or risk factor for health outcome
is unequally distributed b/w the exposure subgroups, it can
lead to confounding
(Beaglehole)
Confounder must be…..
1. Risk factor among the unexposed (itself a determinant of
disease)
2. Associated with the exposure under study
3. Unequally distributed among the exposed and the
unexposed groups
22. Examples … confounding
COFFEE DRINKING HEART DISEASE
SMOKING
(Coffee drinkers are
more likely to smoke)
(Smoking increases
the risk of heart ds)
23. Methods for controlling Selection Bias
During Study Design
1. Randomization
2. Restriction
3. Matching
During analysis
4. Stratification
5. Standardization
24. Restriction
• Subjects chosen for study are restricted to only
those possessing a narrow range of characteristics,
to equalize important extraneous factors
• Limitation: generalisability is compromised; by
excluding potential subjects, cohorts / groups
selected may be unusual and not representative of
most patients or people with condition
• Example :OCP example - restrict study to women
having at least one child
25. Matching
• The process of making a study group and a comparison
group comparable with respect to extraneous factors
(Last)
• For each patient in one group there are one or more
patients in the comparison group with same
characteristics, except for the factor of interest
(Fletcher)
26. Cont…
• Matching is often done for age, sex, race, place of
residence, severity of disease, rate of progression
of disease, previous treatment received etc.
• Limitations:
- Controls for bias for only those factors involved in
the match
- Usually not possible to match for more than a few
factors because of the practical difficulties of finding
patients that meet all matching criteria
27. Stratification
• The process of or the result of separating a sample
into several sub-samples according to specified
criteria such as age groups, socio-economic status
etc. (Last)
• The effect of confounding variables may be
controlled by stratifying the analysis of results
• After data are collected, they can be analyzed and
results presented according to subgroups of
patients, or strata, of similar characteristics
(Fletcher)
28. Standardization
• A set of techniques used to remove as far as possible
the effects of differences in age or other confounding
variables when comparing two or more populations
• The method uses weighted averages of rates specific
for age, sex, or some other potentially confounding
variable(s), according to some specified distribution
of these variables
(Last)
29. Dealing with measurement bias
1. Blinding
- Subject
- Observer / interviewer
- Analyser
2. Strict definition / standard definition for
exposure / disease / outcome
3. Equal efforts to discover events equally in all
the groups
30. Controlling confounding
• Similar to controlling for selection bias
• Use randomization, restriction, matching,
stratification, standardization etc.
Editor's Notes
#4:a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is incorrectly retaining a false null hypothesis (a "false negative")
