3. DEFINITION
Bias is defined as “any systematic error in the design, conduct
or analysis of a study that results in a mistaken estimate of an
exposure’s effect on the risk of disease.”
THE CONCEPT
It is the lack of internal validity or incorrect assessment of the
association between an exposure and an effect in the target
population.
4. WHAT IS TO BE KEPT IN MIND
• Bias should be distinguished from random error or lack of
precision.
• Biases can be classified by the research stage in which they
occur or by the direction of change in a estimate.
• The most important biases are those produced in the
definition and selection of the study population, data
collection, and the association between different determinants
of an effect in the population.
5. CLASSIFICATION OF BIAS
• Sackett and
Choi (stages
of research):
selection of
study sample,
data,
experimental
design,
measurement
of
exposures/ou
• Maclure and
Schneeweiss,
applying the
causal
diagram
theory,
an interesting
explanation of
the main
sources of
• Kleinbaum
et al:
selection
bias,
information
bias, and
confoundin
g; specific
biases in
trials
• Steineck
and
Ahlbom:
confoundin
g,
misclassific
ation,
misreprese
ntation,
and
7. SELECTION BIAS
Inappropriate
definition of the
eligible
population
Competing risks, Healthcare access
bias,
Length-bias sampling, Neyman
bias,
Spectrum bias, Survivor treatment
selection bias,
Berkson’s bias, Exclusion bias
Friend control bias, Inclusion bias
Matching bias, Relative control
Lack of accuracy
of sampling
frame
Citation bias
Dissemination
bias
Post hoc analysis
Publication bias
Uneven
diagnostic
procedures in
the target
population
During study
implementation
Losses/withdrawals
to follow up
Missing information
in multivariable
analysis
Non-response bias
8. INFORMATIONBIAS
Misclassification
bias
Differential misclassification bias
Non-differential misclassification bias
Detection bias
Observer/interviewer bias
Recall bias
Reporting bias
Ecological
fallacy
Regression to
the mean
Other
information
biases
Hawthorne effect
Lead time bias
Protopathic bias
Temporal ambiguity
Will Rogers phenomenon
Work up bias (verification
bias)
10. SPECIFIC BIASES IN TRIALS
Allocation of intervention
bias
Compliance bias
Contamination bias
Differential maturing
Lack of intention to treat
analysis
11. SELECTION BIAS
• The error introduced when the study population does not
represent the target population.
• Selection bias can be controlled when the variables influencing
selection are measured on all study subjects and either
– (a) they are antecedents of both exposure and outcome or
– (b) the joint distribution of these variables (plus exposure and outcome)
is known in the whole target population, or
– (c) the selection probabilities for each level of these variables are known.
12. • It can be introduced at any stage of a research study:
– design
• bad definition of the eligible population,
• lack of accuracy of sampling frame,
• uneven diagnostic procedures in the target population) and
– implementation.
SELECTION BIAS (CONTINUED)
13. when two or more outputs are mutually exclusive, any of them competes
with each other in the same subject.
It is more frequent when dealing with causes of death: as any person only
dies once, the risk for a specific cause of death can be affected by an
earlier one.
For example, early death by AIDS can produce a decrease in liver failure mortality in
parenteral drug users. A proper analysis of this question should take into account the
competing causes of death; for instance, estimating the probability of death by a specific
cause of death if any other risk of death is removed (the so called net probability of
SELECTION BIAS (CONTINUED)
COMPETING RISKS
14. When the patients admitted to an institution do not represent the cases
originated in the community.
This may be due:
– to the own institution if admission is determined by the interest of
health personnel on certain kind of cases (popularity bias),
– to the patients if they are attracted by the prestige of certain clinicians
(centripetal bias)
– to the healthcare organisation if it is organised in increasing levels of
complexity (primary, secondary, and tertiary care) and ‘‘difficult’’ cases
are referred to tertiary care (referral filter bias),
– to a web of causes if patients by cultural, geographical, or economic
reasons show a differential degree of access to an institution (diagnostic
/ treatment access bias).
SELECTION BIAS (CONTINUED)
HEALTHCARE ACCESS BIAS
15. Cases with diseases with long duration are more easily included in surveys.
This series may not represent the cases originated in the target population.
These cases usually have a better prognosis.
Example: Suppose you went over to a hospital last Thursday and surveyed every hospitalized
patient. Many of the people you met would have been in the middle of a long-term stay.
Why? Because people who only needed to stay one or two nights walked out before you
could meet them. The stock or cross-section of hospitalized patients you met yesterday was
a very different, much sicker sample than you would have found, had you specifically
surveyed every patient who began their hospital stay on that same day.
SELECTION BIAS (CONTINUED)
LENGTH-BIAS SAMPLING
16.
17. Synonyms: incidence-prevalence bias, selective survival bias.
When a series of survivors is selected, if the exposure is related to prognostic
factors, or the exposure itself is a prognostic determinant, the sample of cases
offers a distorted frequency of the exposure.
This bias can occur in both cross sectional and (prevalent) case-control
studies.
It has been shown that this bias occurs only if the risk factor influences
mortality from the disease being studied.
Lets suppose that a case-control study is carried out to study the relation between tobacco
smoking and AMI, being cases interviewed one week after the coronary attack. If smoker
patients with AMI die more frequently, the leaving cases will show lower frequency of
SELECTION BIAS (CONTINUED)
NEYMAN BIAS
18. The lines on this graph represent the
duration of disease with the left endpoint
representing the date that the disease was
first diagnosed & the right endpoint
representing the date that the patient died.
This graph represents a selection of
prevalent cases, and the green lines
represent those patients who were alive on
19. This graph represents incident cases, and the green
lines represent those patients newly diagnosed with
the disease between January 1, 2001 and December
31, 2003.
The prevalent cases include very few patients with
In this graph, the patients with the shortest survival times
appear at the bottom of the graph and the patients with
the longest survival times appear at the top. Notice how
rarely the patients with short survival times appear
among the prevalent cases.
20. This graph shows the incident cases with the patients
again sorted by survival time. Notice that the incident
cases include a fair number of patients with short
This can make a critical
difference for a case control
design where you have risk
factors that are associated not
with the disease itself, but with
mortality.
Any risk factor that makes a
person die quickly is going to be
underrepresented among
prevalent cases and could lead to
a spurious finding.
This is called Neyman's bias.
21. • In the assessment of validity of a diagnostic test this bias is produced
when researchers included only ‘‘clear’’ or ‘‘definite’’ cases, not
representing the whole spectrum of disease presentation, and/or ‘‘clear’’
or healthy controls subjects, not representing the conditions in which a
differential diagnosis should be carried out.
• Spectrum bias exists when the population under investigation does not
reflect the general population or the clinically relevant population. It is
also termed case-mix bias.
SELECTION BIAS (CONTINUED)
SPECTRUM BIAS
22. • A particular case is the purity diagnostic bias, when selecting cases of a
certain disease those with other comorbidities are excluded and the final
sample does not represent the cases originated.
For example, B type natriuretic peptide can contribute to the diagnosis of heart failure,
but might also be raised in several other conditions such as renal failure and pulmonary
hypertension.
SELECTION BIAS (CONTINUED)
SPECTRUM BIAS (CONTINUED)
23. In observational studies patients who live longer have more probability to
receive a certain treatment. A retrospective analysis can therefore yield a
positive association between that treatment and survival.
Patients destined to live longer have more opportunity for treatment, so
the treated patients appear to have better survival than the untreated
patients.
The heart transplant example is the clearest: patients who got heart transplants had to
live long enough to get a donor heart, so if they are compared to the non-transplant
group they will appear to have better survival. The solution is to include eventually
SELECTION BIAS (CONTINUED)
SURVIVOR TREATMENT SELECTION BIAS
24. First described by Berkson in 1946 for case control studies.
It is produced when the probability of hospitalisation of cases and
controls differ, and it is also influenced by the exposure.
Suppose a researcher came up with the idea to study whether or not water pills
(diuretics) were a risk factor for bladder cancer. The researcher finds N=500 cases of
bladder cancer in from a cancer registry. For controls, he decides to use N=500 patients
from the hospital he works at, admitted for any reason but cancer.
SELECTION BIAS (CONTINUED)
BERKSON’S BIAS
25. Past or Current Diuretic Use
Bladder
Cancer
Yes
(exposed)
No
(unexposed)
Row
Total
Yes 75 (15%) 425 (85%) 500
No
SELECTION BIAS (CONTINUED)
BERKSON’S BIAS (CONTINUED)
Suppose that in the general population, the incidence of bladder cancer is equal among
those who use diuretics and those who don’t (odds ratio = 1, or no association).
Designing a case-control study, now, the researcher begins with the 500 bladder cancer
cases.
Starting with the cases (row totals shown)
26. Past or Current Diuretic Use
Bladder
Cancer
Yes
(exposed)
No
(unexposed)
Row
Total
Yes 75 (15%) 425 (85%) 500
No 75 (15%) 425 (85%) 500
SELECTION BIAS (CONTINUED)
BERKSON’S BIAS (CONTINUED)
If there really is no association, a random sample of N=500 controls
from the general population (not hospitalized controls) would have the
same distribution of Diuretics.
Odds Ratio = (75 425)/(75 425) = 1.0
If Had Used General Population Controls (row totals
shown)
27. • Hypertension is an early stage of heart disease.
• Diuretics are usually given as the initial treatment for hypertension, and
many patients are still using diuretics when later hospitalized for heart
disease events.
• Since heart disease makes up a large share of hospitalizations, we should
expect a random sample of hospital controls to be more frequent users of
diuretics. Thus, our data might look like:
BERKSON’S BIAS (CONTINUED)
Using Hospital Controls (row totals shown)
Past or Current Diuretic Use
Bladder
Cancer
Yes (exposed) No (unexposed) Row Total
Yes 75 (15%) 425 (85%) 500
No 100 (20%) 400 (80%) 500
Odds Ratio = (75 400)/(425 100) = 0.71
Since no association exists in the population, this observed protective effect is attributable to
Berkson’s bias.
28. When controls with conditions related to the exposure are excluded,
whereas cases with these diseases as comorbidities are kept in the study.
This was the explanation given for the association between reserpine and
breast cancer: controls with cardiovascular disease (a common
comorbidity and related to the use of reserpine) were excluded but this
criterion was not applied to cases, thus yielding a spurious association
between reserpine and breast cancer.
SELECTION BIAS (CONTINUED)
EXCLUSION BIAS
29. The cases were 257 women with breast cancer; and the controls were 257
hospitalized women matched according to date of admission, age, and
race.
The overall data showed no association between reserpine and breast
cancer (odds ratio [OR] = 1.1), but when we excluded 101 women with
cardiovascular disease from the control group, the OR rose to 2.5.
The results suggest that exclusion bias played an important role in
creating the false association between reserpine and breast cancer.
SELECTION BIAS (CONTINUED)
EXCLUSION BIAS [CONTINUED]
30. • It was assumed that the correlation in exposure status between cases
and their friend controls lead to biased estimates of the association
between exposure and outcome.
• In a matched study, with a matched analysis, there is no bias if the
exposure induced risks of disease are constant over time and there are
not gregarious subjects, individuals elected by more than one case.
SELECTION BIAS (CONTINUED)
FRIEND CONTROL BIAS
31. Produced in hospital based case-control studies when one or more
conditions of controls are related with the exposure.
The frequency of exposure is higher than expected in the reference group,
producing a toward the null bias.
SELECTION BIAS (CONTINUED)
INCLUSION BIAS
32. • It is well known that matching, either individual or characteristics
matching, introduces a selection bias, which is controlled for by
appropriate statistical analysis: matched analysis in studies with
individual matching and adjusting for the variables used to match in
frequency matching.
• Overmatching is produced when researchers match by a non-
confounding variable (associated to the exposure but not to the disease)
and can underestimate an association.
SELECTION BIAS (CONTINUED)
MATCHING BIAS
33. • It was assumed that the correlation in exposure status between cases
and their relative controls yield biased estimates of the association
between exposure and outcome.
• In a matched study, with a matched analysis, there is no bias if the
exposure induced risks of disease are constant over time.
SELECTION BIAS (CONTINUED)
RELATIVE CONTROL BIAS
34. • Articles more frequently cited are more easily found and included in
systematic reviews and meta analysis.
• Citation is closely related to the impact factor of the publishing
journal.
• In certain fields, citation has been related to statistical significance.
SELECTION BIAS (CONTINUED)
CITATION BIAS
35. Example:
PubMed currently includes more than 26 million citations.
With such an enormous number of available articles to choose from, it is often
easy to find individual studies which support directly contradictory views on a
single clinical topic.
This is problematic because it allows both researchers and laypeople to make
claims that are supported by individual published studies, but which do not reflect
the total available scientific evidence on a given topic.
This is called citation bias.
When citation bias occurs, positive results receive more and more emphasis,
while negative or neutral results are downplayed.
36. • The biases associated to the whole publication process, from biases in
the retrieval of information (including language bias) to the way the
results are reported.
SELECTION BIAS (CONTINUED)
DISSEMINATION BIAS
37. • The fishing expeditions with data dredging originates post hoc
questions and subgroup analysis with misleading results.
• Given that the reports based on post hoc analysis are frequently
reported when significant results are observed, this bias is relevant for
meta-analysis of published studies as a form of publication (selection)
bias.
SELECTION BIAS (CONTINUED)
POST HOC ANALYSIS
38. • Regarding an association that is produced when the published reports
do not represent the studies carried out on that association.
• Several factors have been found to influence publication, the most
important being statistical significance, size of the study, funding,
prestige, type of design, and study quality.
SELECTION BIAS (CONTINUED)
PUBLICATION BIAS
39. • In case-control studies, if exposure influences the diagnosis of the
disease, detection bias occurs.
• Particular types of this bias are: exposure can be taken as another
diagnostic criterion (diagnostic suspicion bias).
• Exposure can trigger the search for the disease; for instance, benign anal
lesions increases the diagnosis of anal cancer.
• Exposure may produce a symptom/ sign that favours diagnosis
(unmasking-detection signal-bias) or a benign condition close
to the disease (mimicry bias).
SELECTION BIAS (CONTINUED)
UNEVEN DIAGNOSTIC PROCEDURES IN THE TARGET POPULATION
40. • Losses/withdrawals to follow up: in both cohort and experimental
studies when losses/withdrawals are uneven in both the exposure and
outcome categories, the validity of the statistical results may be affected.
• Missing information in multivariable analysis: multivariable analysis
selects records with complete information on the variables included in
the model. If participants with complete information do not represent
target population, it can introduce a selection bias.
SELECTION BIAS (CONTINUED)
DURING STUDY IMPLEMENTATION
41. • Non-response bias: when participants differ from nonparticipants, for
example, Melton et al.
– The healthy volunteer effect is a particular case: when the
are healthier than the general population.
– This is particularly relevant when a diagnostic manoeuvre, such as a
screening test, is evaluated in the general population, producing an
away from the null bias; thus the benefit of the intervention is
spuriously increased.
42. Differential misclassification bias:
• when misclassification is different in the groups to be compared;
• The estimate is biased in either direction, toward the null or away from
the null
• For example, the accuracy of blood pressure measurement may be
lower for heavier than for lighter study subjects, or a study of elderly
persons may find that reports from elderly persons with dementia are
less reliable than those without dementia. The effect(s) of such
misclassification can vary from an overestimation to an
INFORMATION BIAS
Misclassification bias
43. Nondifferential misclassification
• The frequency of errors is approximately the same in the groups being
compared.
• A study may be biased by misclassification of either exposure or outcome
status, or both.
• Nondifferential misclassification can occur in a number of ways.
• Records may be incomplete, e.g., a medical record in which none of the healthcare workers
remember to ask about tobacco use.
• There may be errors in recording or interpreting information in records, or there may be
errors in assigning codes to disease diagnoses by clerical workers who are unfamiliar with a
INFORMATION BIAS (CONTINUED)
Misclassification bias
44. Suppose a case-control study was conducted to examine the association
between a high fat diet and coronary artery disease.
Subjects with heart disease and controls without heart disease might be
recruited and asked to complete questionnaires about their dietary habits
in order to categorize them as having diets with high fat content or not.
It is difficult to assess dietary fat content accurately from questionnaires,
so it would not be surprising if there were errors in classification of
exposure. However, it is likely that in this scenario the misclassification
would occur with more or less equal frequency regardless of the eventual
disease status.
The figure above depicts a scenario in which disease status is correctly
classified, but some of the exposed subjects are incorrectly classified as
non-exposed.
This would result in bias toward the null. Rothman gives a hypothetical
example in which the true odds ratio for the association between a high fat
diet and coronary heart disease is 5.0, but if about 20% of the exposed
subjects were misclassified as 'not exposed' in both disease groups, the
biased estimate might give an odds ratio of, say, 2.4. In other words, it
resulted in bias toward the null.
However, now consider what would happen in the same example if 20% of
the exposed subjects were misclassified as 'not exposed' in both outcome
groups, AND 20% of the non-exposed subjects were misclassified as
'exposed' in both groups - in other words a scenario that looked something
like this
45. • The knowledge of the hypothesis, the disease status, or the exposure
status (including the intervention received) can influence data recording
(observer expectation bias).
• The means by which interviewers can introduce error into a questionnaire
include administering the interview or helping the respondents in different
ways (even with gestures), putting emphases in different questions, and so
on.
• In one group, ("Group A"), experimenters were told to expect positive ratings while in
another group, ("Group B"), experimenters were told to expect negative ratings.
Data collected from Group A was a significant and substantially more optimistic
appraisal than the data collected from Group B. The researchers suggested that
INFORMATION BIAS (CONTINUED)
OBSERVER / INTERVIEWER BIAS
46. • Recall bias is a systematic error caused by differences in the accuracy
or completeness of the recollections retrieved ("recalled") by study
participants regarding events or experiences from the past.
• This bias is more common in case-control studies, in which participants
know their diseases, although it can occur in cohort studies (for
example, workers who known their exposure to hazardous substances
may show a trend to report more the effects related to them), and
trials without participants’ blinding
INFORMATION BIAS (CONTINUED)
RECALL BIAS
47. • In epidemiology, reporting bias is defined as "selective revealing or
suppression of information" by subjects (for example about past
medical history, smoking, sexual experiences).
• In artificial intelligence research, the term reporting bias is used to refer
to people's tendency to under-report all the information available.
• Underreporting bias is common with socially undesirable behaviours,
such as alcohol consumption.
INFORMATION BIAS (CONTINUED)
REPORTING BIAS
48. • It is a bias produced when analyses realised in an ecological
(group level) analysis are used to make inferences at the
individual level.
INFORMATION BIAS (CONTINUED)
ECOLOGICAL FALLACY
49. Consider the following numerical example:
Group A: 80% of people got 40 points and 20% of them got 95
points.
The mean score is 51 points.
Group B: 50% of people got 45 points and 50% got 55 points.
The mean score is 50 points.
If we pick two people at random from A and B, there are 4 possible
outcomes:
A – 40, B – 45 (B wins, 40% probability – 0.8 × 0.5)
A – 40, B – 55 (B wins, 40% probability – 0.8 × 0.5)
A – 95, B – 45 (A wins, 10% probability – 0.2 × 0.5)
A – 95, B – 55 (A wins, 10% probability – 0.2 × 0.5)
50. INFORMATION BIAS (CONTINUED)
REGRESSIONAL FALLACY
• It is the phenomenon that a variable that shows an extreme value on
its first assessment will tend to be closer to the centre of its
distribution on a later measurement.
51. little. Therefore, he benefited from the doctor's treatment.
The pain subsiding a little after it has gotten worse is more easily explained
by regression towards the mean. Assuming the pain relief was caused by the
doctor is fallacious.
The student did exceptionally poorly last semester, so I punished him. He did
much better this semester. Clearly, punishment is effective in improving
students' grades.
Often exceptional performances are followed by more normal performances,
so the change in performance might better be explained by regression
towards the mean.
Incidentally, some experiments have shown that people may develop a
52. Hawthorne effect:
Described in the 1920s in the Hawthorne plant of the Western Electric
Company (Chicago, IL).
It is an increase in productivity—or other outcome under study—in
participants who are aware of being observed.
For example, laboratory physicians increase their agreement rate after
knowing that they participate in a research on reliability of diagnostic
tests.
INFORMATION BIAS (CONTINUED)
53. Lead time bias:
The added time of illness produced by the diagnosis of a condition
its latency period.
This bias is relevant in the evaluation of the efficacy of screening, in
the cases detected in the screened group has a longer duration of
than those diagnosed in the non-screened one.
INFORMATION BIAS (CONTINUED)
54. • Here the disease starts in 1985, is diagnosed in 1992 and the person
dies of that disease in 1995.
• How long is his survival? Three years
55. • Now we institute an effective screening program.
• The disease starts in 1985 and is detected by the screening program in
1989.
• The person dies of the disease in 1995.
• How long was the survival? Six years. Screening seems to have increased
their survival time, correct?
56. • You have also noted that in either situation, it is 10 years from the time the
disease started until the person dies.
• If our measure is survival time, we can easily produce a lead time bias. In
this example, there is actually no benefit of the screening process, in terms
of survival.
• The person still died in 1995.
• They know about the disease for three years longer; that is the effect of the
screening. This example demonstrates a lead-time bias of three years
57. Protopathic bias:
• When a exposure is influenced by early (subclinical) stages of disease.
For instance, preclinical pancreatic cancer can produce
diabetes mellitus, and thus an association between diabetes and cancer
can occur.
It is also produced when a pharmaceutical agent is
prescribed for an early manifestation of a disease that has not been yet
diagnosed.
• The sick quitter bias is related to protopathic bias:
people with risky behaviours (such as heavy alcohol
consumption) quit their habit as a consequence of disease; studies
INFORMATION BIAS (CONTINUED)
58. Will Rogers phenomenon:
• Named in honour of the philosopher Will Rogers by Feinstein et al.
• This bias is relevant when comparing cancer survival rates across
or even among centres with different diagnostic capabilities.
• for example, tertiary compared with primary care hospitals.
INFORMATION BIAS (CONTINUED)
59. Work up bias (verification bias):
• In the assessment of validity of a diagnostic test, it is produced when
execution of the gold standard is influenced by the results of the
assessed test, typically the reference test is less frequently performed
when the test result is negative.
• This bias is aggravated when the clinical characteristics of a disease
influence in the test results.
INFORMATION BIAS (CONTINUED)
60. CONFOUNDING
• Susceptibility bias is a synonym: when people who are particularly
susceptible to development of a outcome are also prone to be exposed;
• for example, women with threatened abortion have a high probability of
delivering a malformed fetus but also have a high probability of
receiving hormone treatment.
• This can yield a spurious association between hormones and congenital
malformations.
61. CONFOUNDING BY GROUP
• It is produced in an ecological study, when the exposure prevalence of
each community (group) is correlated with the disease risk in non-
exposed of the same community. It can be a mechanism for producing
ecological fallacy.
• For example, lets suppose three communities (A, B, C) with prevalence
exposures of 10%, 20%, and 30%, rates of disease in non-exposed of
2%, 3%, and 4%, and rates of disease in the exposed of 2%, 3%, 4%,
respectively.
• There is no association between the exposure and the disease as the
62. CONFOUNDING BY INDICATION
• This is produced when an intervention (treatment) is indicated by a
perceived high risk, poor prognosis, or simply some symptoms.
• Here the confounder is the indication, as it is related to the intervention
and is a risk indicator for the disease.
• For example, in the study of the association between cimetidine and
gastric cancer, the indication peptic ulcer is considered the potential
confounder.
• This kind of bias occurs in observational studies (mainly retrospective)
analysing interventions. Sometimes confounding by indication is
mistaken for protopathic bias.
63. SPECIFIC BIASES IN TRIALS
Allocation of intervention bias:
• when intervention is differentially assigned to the population. It is more
common in non-randomised trials.
• In randomised trials it is recommended concealment of the allocation
sequence of intervention.
• If the sequence is known in advance may produce selection bias.
• It has been shown that trials in which concealment was unclear or
inadequate, compared with trials with adequate concealment, report
larger estimates of treatment effects.
64. Compliance bias:
• In trials requiring adherence to intervention, the degree of adherence
(compliance) influences efficacy assessment of the intervention.
• For example, when high risk patients quit exercise programmes.
65. Contamination bias:
• When intervention-like activities find their way into the control group.
• It biases the estimate of the intervention effect toward the null
hypothesis.
• It occurs more frequently in community intervention trials because of
the relationships among members of different communities and
interference by mass media, health professionals, etc.
66. Differential maturing:
• In group randomised trials differential maturing reflects uneven secular
trends among the groups in the trial favouring one condition or
another.
67. Lack of intention to treat analysis:
• In randomised studies the analysis should be done keeping
in the group they were assigned to.
• The goals of randomisation are to avoid confounding and selection
• If non-compliant participants or those receiving a wrong intervention
are excluded from the analysis, the branches of a randomised trial may
not be comparable. There are exceptions to the rule of intention to
analysis.
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