confounding lecture
DESCRIPTION
bias and confounders in medical studiesTRANSCRIPT
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Preparatory lecture
Confounding and Bias
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Random Error 2
Introduction
• Most epidemiological studies measure disease frequency in two (or more) groups that differ only on the exposure of interest. The two measures of disease frequency are combined into a single measure of association – risk or rate ratio, odds ratio, risk or rate difference
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Random Error 3
Introduction
• The next step is to evaluate whether the result that has been observed in the data is true, or whether the observed result is false and there is an alternate explanation. This is the process of assessing validity of a study result.
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Accuracy vs. precision
Accuracy: obtaining results close to truth
Survey 1
Survey 2
Survey 3
Real population
value
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Accuracy vs. precision
Precision: obtaining similar results with repeated measurement (may or may not be accurate)
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Accuracy vs. precision
Poor precision (from small sample size) with reasonable accuracy (without bias):
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Accuracy vs. precision
Good precision (from small sample size) with reasonable accuracy (without bias):
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Accuracy vs. precision
Good precision (from large sample size), but with poor accuracy (with bias):
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In sum…• Sampling error
– Difference between survey result and population value due to random selection of sample
– Greater with smaller sample sizes– Induces lack of precision
• Bias– Difference between survey result and population value due to error
in measurement, selection of non-representative sample or other factors
– Due to factors other than sample size– Therefore, a large sample size cannot guarantee absence of bias– Induces lack of accuracy, even with good precision
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Definitions ERROR: 1. A false or mistaken result obtained in a study
or experiment2. 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
3. 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)
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Bias• Deviation of results or inferences from the
truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth. (Last)
• A process at any stage of inference tending to produce results that depart systematically from true values (Fletcher)
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What is meant by bias inresearch?
• Bias is the term used to describe differences between the study findings and truth
• “Any effect at any stage of investigation orinference tending to produce results that departsystematically from the true values (to bedistinguished from random error)”
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Bias
• Bias is a systematic error in an epidemiologic study that results in an incorrect estimation of the association between exposure and outcome
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What can be wrong in the study?
Random error
Results in low precision of the epidemiological measure measure is not precise, but true
1 Imprecise measuring2 Too small groups
Systematic errors(= bias)
Results in low validity of the epidemiological measure measure is not true
1 Selection bias2 Information bias
3 Confounding
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Random errors
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Errors in epidemiological studies
Error
Study size
Systematic error (bias)
Random error (chance)
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Estimation
• When we measure OR, we estimate a point estimate– Will never know the true value
• Confidence interval indicates precision or amount of random error– Wide interval low precision– Narrow interval high precision
• OR = 4.5 (2.0 – 10)
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Classification of bias
There are three broad categories of bias:• • selection bias• • confounding• • measurement bias
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Systematic error
•Does not decrease with increasing sample size
• Selection bias• Information bias• Confounding
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BiasSystematic deviations in study findings
from the truth–Results from errors in the collection,
analysis, interpretation, publication, or review of data
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Selection BiasError due to systematic difference between the characteristics of the people selected for a study and
those who are not.
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Selection bias
• Errors due to systematic differences in characteristics between those who are selected for study and those who are not.
(Last; Beaglehole)• When comparisons are made between groups
of patients that differ in ways other than the main factors under study, that affect the outcome under study. (Fletcher)
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What is Selection Bias?
“ Error due to systematic differences in characteristics between those who are selected for study and those who are not.”
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Examples of Selection bias• Subjects: hospital cases under the care of a
physician• Excluded: 1. Die before admission – acute/severe disease.2. Not sick enough to require hospital care3. Do not have access due to cost, distance etc.• Result: conclusions cannot be generalized• Also known as ‘Ascertainment Bias’
)Last(
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Ascertainment Bias
• Systematic failure to represent equally all classes of cases or persons supposed to be represented in a sample. This bias may arise because of the nature of the sources from which the persons come, e.g., a specialized clinic;
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Case ascertainment• Who is your case?
– Patient?– Deceased person?
• What is the definition of the case?– Cancer (clinically? Pathologically?)– Virus carriers (Asymptomatic patients)
→ You need to screen the antibody
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Who will be controls?• Control ≠ non-case
– Controls are also at risk of the disease in his(her) future.
– In a case-control study of gastric cancer, a person who has received the gastrectomy cannot be a control.
– In a case-control study of car accident, a person who does not drive a car cannot be a control.
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Selection bias with ‘volunteers’
• Also known as ‘response bias’ • Systematic error due to differences in
characteristics b/w those who choose or volunteer to take part in a study and those who do not
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Selection bias with ‘Survival Cohorts’
• Patients are included in study because they are available, and currently have the disease
• For lethal diseases patients in survival cohort are the ones who are fortunate to have survived, and so are available for observation
• For remitting diseases patients are those who are unfortunate enough to have persistent disease
• Also known as ‘Available patient cohorts’
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Selection bias due to ‘Loss to Follow-up’
• Also known as ‘Migration Bias’• In nearly all large studies some members of
the original cohort drop out of the study• If drop-outs occur randomly, such that
characteristics of lost subjects in one group are on an average similar to those who remain in the group, no bias is introduced
• But ordinarily the characteristics of the lost subjects are not the same
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Healthy worker effect
• A phenomenon observed initially in studies of occupational diseases: workers usually exhibit lower overall death rates than the general population, because the severely ill and chronically disabled are ordinarily excluded from employment. Death rates in the general population may be inappropriate for comparison if this effect is not taken into account.
)Last(
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Example…. ‘healthy worker effect ’
• Question: association b/w formaldehyde exposure and eye irritation
• Subjects: factory workers exposed to formaldehyde
• Bias: those who suffer most from eye irritation are likely to leave the job at their own request or on medical advice
• Result: remaining workers are less affected; association effect is diluted
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Information Bias(Observation Bias,
Measurement Bias)Error due to systematic differences in the way data on exposure or outcome are obtained from various groups leading to misclassification of
study subjects
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Measurement bias• Systematic error arising from inaccurate
measurements (or classification) of subjects or study variables. (Last)
• Occurs when individual measurements or classifications of disease or exposure are inaccurate (i.e. they do not measure correctly what they are supposed to measure)
(Beaglehole)• If patients in one group stand a better chance of
having their outcomes detected than those in another group. (Fletcher)
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Measurement / (Mis) classification
• Exposure misclassification occurs when exposed subjects are incorrectly classified as unexposed, or vice versa
• Disease misclassification occurs when diseased subjects are incorrectly classified as non-diseased, or vice versa
)Norell(
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Causes of misclassification
1. Measurement gap: gap between the measured and the true value of a variable
- Observer / interviewer bias- Recall bias- Reporting bias2. Gap b/w the theoretical and empirical
definition of exposure / disease
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Example… ‘gap b/w definitions’
Theoretical definition•Exposure: passive
smoking – inhalation of tobacco smoke from other people’s smoking
•Disease: Myocardial infarction – necrosis of the heart muscle tissue
Empirical definition•Exposure: passive
smoking – time spent with smokers (having smokers as room-mates)
•Disease: Myocardial infarction – certain diagnostic criteria (chest pain, enzyme levels, signs on ECG)
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Exposure misclassification – Non-differential
•Misclassification does not differ between cases and non-cases
•Generally leads to dilution of effect, i.e. bias towards RR=1 (no association)
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Example…Non-differential Exposure Misclassification
+nt-ntTotal+nt4080120-nt
100004000050000
+nt-ntTotal+nt6060120-nt
200003000050000
EXPOSUREX-ray exposure
EXPOSUREX-ray exposure
DIS
EASE
Bre
ast C
ance
r
RR= 40/10000 80/40000 = 2
RR= 60/20000 60/30000 = 1.5
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An example of non-differential misclassification in an exposure variable
We want to compare mean of blood pressure levels between cases and controls.
The blood pressure checker has a problem and always gives 5mmHg-higher than true values.
All subjects were examined by the same blood pressure checker.
→ no problem for internal comparison
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Exposure misclassification - Differential
• Misclassification differs between cases and non-cases
• Introduces a bias towards RR= 0 (negative / protective association), or
RR= α (infinity)(strong positive association)
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Example…Differential Exposure Misclassification
+nt-ntTotal+nt4080120-nt99603992049880
100004000050000
+nt-ntTotal+nt4080120-nt199402994049880
199803002050000
EXPOSUREX-ray exposure
EXPOSUREX-ray exposure
DIS
EASE
Bre
ast C
ance
r
RR= 40/10000 80/40000 = 2
RR= 40/19980 80/30020 = 0.75
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Causes of Differential Exposure Misclassification
• Recall Bias:Systematic error due to differences in accuracy or completeness of recall to memory of past events or experience.
For e.g. patients suffering from MI are more likely to recall and report ‘lack of exercise’ in the past than controls
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Causes of Differential Exposure Misclassification
• Measurement bias:e.g. analysis of Hb by different methods
(cyanmethemoglobin and Sahli's) in cases and controls.
e.g.biochemical analysis of the two groups from two different laboratories, which give consistently different results
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Causes of Differential Exposure Misclassification
• Interviewer / observer bias: systematic error due to observer variation (failure of the observer to measure or identify a phenomenon correctly)
e.g. in patients of thrombo-embolism, look for h/o OCP use more aggressively
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Confounding 1. A relationship b/w the effects of two or
more causal factors as observed in a set of data such that it is not logically possible to separate the contribution that any single causal factor has made to an effect
(Last)
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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(
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Confounding
Confounders are risk factors for the outcome.
Confounders are related to exposure of your interest.
Confounders are NOT in the process of causal relationship between the exposure and the outcome of your interest.
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Example of “not” confounder- pineal hormone is not a confounder-
Breast cancer
Down regulationof pineal hormoneCausation?
EMF
EMF: electro-magnetic field
EMF exposure induces down
regulation of pineal hormone
Decrease of pineal hormonemay be the risk of breast ca.
If EMF exposure cause breast cancer only through down regulation of pineal hormone, this is not a confounder.
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Examples … confounding
SMOKING LUNG CANCER
AGE)If the average ages of the smoking and
non-smoking groups are very different(
)As age advanceschances of lungcancer increase(
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Examples … confounding
COFFEE DRINKING HEART DISEASE
SMOKING
)Coffee drinkers are more likely to smoke(
)Smoking increasesthe risk of heart ds(
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Examples … confounding
ALCOHOLINTAKE
MYOCARDIALINFARCTION
SEX
)Men are more at risk for MI(
)Men are more likelyto consume alcohol
than women(
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Why do we have to consider confounding?
We want to know the “real” causal association but a
distorted relationship remains if you do not adjust
for the effects of confounding factors.
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Example … multiple biases• Study: ?? Association b/w regular exercise and
risk of CHD• Methodology: employees of a plant offered an
exercise program; some volunteered, others did not
coronary events detected by regular voluntary check-ups, including a careful history, ECG, checking routine heath records
• Result: the group that exercised had lower CHD rates
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Biases operating
• Selection: volunteers might have had initial lower risk (e.g. lower lipids etc.)
• Measurement: exercise group had a better chance of having a coronary event detected since more likely to be examined more frequently
• Confounding: if exercise group smoked cigarettes less, a known risk factor for CHD
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Methods for controlling Selection Bias
During Study Design1. Randomization2. Restriction3. MatchingDuring analysis1. Stratification2. Adjustmenta) Simple / standardizationb) Multiple / multivariate adjustment
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Randomization
• The only way to equalize all extraneous factors, or ‘everything else’ is to assign patients to groups randomly so that each has an equal chance of falling into the exposed or unexposed group
• Equalizes even those factors which we might not know about!
• But it is not possible always
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Restriction
• Subjects chosen for study are restricted to only those possessing a narrow range of characteristics, to equalize important extraneous factors
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Example… restriction
• Study: effect of age on prognosis of MI• Restriction: Male / White / Uncomplicated
anterior wall MI• Important extraneous factors controlled
for: sex / race / severity of disease• Limitation: results not generalizable to
females, people of non-white community, those with complicated MI
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• For example: “Babies who are breast-fed have less illness
than babies who are bottle-fed.”
Which illnesses? How is feeding type defined? How large a difference in risk?
• A better example: “Babies who are exclusively breast-fed for
three months or more will have a reduction in the incidence of hospital admissions for gastroenteritis of at least 30% over the first year of life.”
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Matching - definition •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)
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Types of Matching• Caliper matching: process of matching
comparison group to study group within a specific distance for a continuous variable (e.g., matching age to within 2 years)
• Frequency matching: frequency distributions of the matched variable(s) be similar in study and comparison groups
• Category matching: matching the groups in broad classes such as relatively wide age ranges or occupational groups
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•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
-If categories for matching are relatively crude, there may be room for substantial differences b/w matched groups
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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)
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Examples … confounding
+nt-nt+nt140100-ntTotal 3000030000
+nt-ntmalefemalemalefemale
+nt120206040-ntTotal20000100001000020000
Exposure-alcohol
Exposure-alcohol
Dis
ease
M
ID
i se a
se
M
I
RR = 140/30000 100/30000 = 1.4
RR = 120/20000(M) 60/10000
= 1RR = 20/10000
(F) 40/20000 = 1
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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 averaging of rates specific for age, sex, or some other potentially
confounding variable(s), according to some specified distribution of these variables
)Last(
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Example … direct standardization
PreopPts Deaths %High 500306
Medium400164Low 3002.67
Total 1200484PreopPts RateExp.deathsHigh 400624
Medium400416Low 400.672.68
Total 120042.68 (3.6%)
HOSPITAL ‘A’
HOSPITAL ‘Std’
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Multivariate adjustment• Simultaneously controlling the effects of
many variables to determine the independent effects of one
• Can select from a large no. of variables a smaller subset that independently and significantly contributes to the overall variation in outcome, and can arrange variables in order of the strength of their contribution
• Only feasible way to deal with many variables at one time during the analysis phase
![Page 69: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/69.jpg)
Examples… Multivariate adjustment
• CHD is the joint result of lipid abnormalities, HT, smoking, family history, DM, exercise, personality type.
• Start with 2x2 tables using one variable at a time
• Contingency tables, i.e. stratified analyses, examining the effect of one variable changed in the presence/absence of one or more variables
![Page 70: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/70.jpg)
Dealing with measurement bias
1. Blinding- Subject- Observer / interviewer- Analyser 2. Strict definition / standard definition for
exposure / disease / outcome3. Equal efforts to discover events equally in all
the groups
![Page 71: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/71.jpg)
Controlling confounding
•Similar to controlling for selection bias•Use randomization, restriction, matching,
stratification, standardization, multivariate analysis etc.
![Page 72: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/72.jpg)
How can we solve the problem of confounding?
“Prevention” at study design LimitationRandomization in an
intervention studyMatching in a cohort
study But not in a case-control study
![Page 73: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/73.jpg)
How can we solve the problem of confounding?
“Treatment “ at statistical analysis
Stratification by a confounderMultivariate analysis
![Page 74: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/74.jpg)
Error & Bias• Error: random error
• Bias : systematic error–differential misclassification
–non-differential misclassification This is a problem!
![Page 75: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/75.jpg)
EXAMPLES OF RANDOM ERROR, BIAS, MISCLASSIFICATION AND
CONFOUNDING IN THE SAME STUDY:
STUDY: In a cohort study, babies of women who bottle feed and women who
breast feed are compared, and it is found that the incidence of gastroenteritis, as recorded in medical records, is lower in
the babies who are breast-fed.
![Page 76: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/76.jpg)
EXAMPLE OF RANDOM ERROR
By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample ,
Or, also by chance, no difference in risk was found ,
![Page 77: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/77.jpg)
EXAMPLE OF RANDOM MISCLASSIFICATION
Lack of good information on feeding history results in some breast-feeding mothers being randomly classified as bottle-feeding, and vice-
versa ..
![Page 78: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/78.jpg)
EXAMPLE OF BIAS
The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only. This is called bias because the observation itself is in error.
![Page 79: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/79.jpg)
EXAMPLE OF CONFOUNDING The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding. because the observation is correct, but its explanation is wrong.
![Page 80: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/80.jpg)
•Sampling•Sample Size•Study design
•Sources of data collection•Methods of data collection
•Content of information
Prevention of Bias
![Page 81: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/81.jpg)
Selection bias
• Error because the associationexposure disease
is different for participants and non-participants in the study
• Errors in the– procedures to select participants– factors that influence participation
![Page 82: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/82.jpg)
Examples of selection bias
• Self-selection bias• Non-response• Loss to follow-up
![Page 83: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/83.jpg)
Self selection bias
• Selection bias is the distortion of statistics by the way in which a sample is selected.
• Self-selection bias is the distortion caused when the sample chooses itself — certain characteristics are over-represented because theycorrelate with willingness to be included.
![Page 84: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/84.jpg)
Non response
• non-response occurs when certain questions in a survey are not answered by a respondent.
• non-response takes place also when a randomly sampled individual cannot be contacted or refuses to participate in a survey.
![Page 85: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/85.jpg)
Sources of selection bias
Inappropriate selection of study subjects from the study population
• – non-random selection of subjects from the same population
• – selection of subjects from different or ill-defined study populations
• – failure to locate or unwillingness of people to participate
• – loss of persons from the study population because of the health outcome eg selective survival
![Page 86: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/86.jpg)
example selection biassuppose we would like to conduct a case–control study of the association
between liver cancer and smoking. Cases (those identified as having liver cancer) could be all available
individuals in all the hospitals in town during the year of the study.Controls (individuals without history of liver cancer) would berecruited by local mass media advertisements—hence they would be
volunteers. The study results would most probably show a strong association between smoking and liver cancer, not necessarily because smoking and liver cancer are related, but because the selection process was different for cases and controls. Although the cases were arguably sampled from the population at large, the controls were sampled from a population of volunteers!
![Page 87: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/87.jpg)
Preventing selection bias
• Same selection criteria• High response-rate• High rate of follow-up
![Page 88: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/88.jpg)
Information bias
• Error because the measurement of exposure or disease
is different between the comparison groups.• Errors in the
– procedures to measure exposure– procedures to diagnose disease
![Page 89: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/89.jpg)
Examples of information bias
• Diagnostic bias• Recall bias• Researcher influence
![Page 90: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/90.jpg)
Measurement bias
Inaccurate measurement of study variables can lead to bias
Sources of inaccurate measurement:• • subject error – error within the individual for any
reason, eg imperfect recall of past exposures• • Instrument error – eg equipment not properly
calibrated, wording of question• • Observer error – error in use of instrument or
recording
![Page 91: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/91.jpg)
Types of measurement error
Non-differential error• the inaccuracies of measurement are the same among
subgroups of subject• Non-differential measurement error in exposure and
outcome will always lead to bias towards finding no effect
Differential error• the inaccuracies of measurement are different among
subgroups of subject can lead to bias towards or away from no effect
![Page 92: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/92.jpg)
Misclassification
Dog No dog
TBE-cases 20 20 OR = ad/bc = 3,0
Controls 20 60
Dog No dog
TBE-cases 24 16 OR = ad/bc = 4,5
Controls 20 60
Dog No dog
TBE-cases 24 16 OR = ad/bc = 2,8
Controls 28 52
True
Differential
Non-differential
![Page 93: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/93.jpg)
Non-differential misclassification
• Same degree of misclassification in both cases and controls
• OR will be underestimated– True value is higher
• If no causal effect found, ask:– Could it be due to non-differential
misclassification?
![Page 94: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/94.jpg)
![Page 95: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/95.jpg)
Preventing information bias• Clear definitions• Good measuring methods• Blinding• Standardised procedures• Quality control
![Page 96: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/96.jpg)
Minimising measurement bias1. use valid reliable tools to measure all studysubjects2. train staff and monitor their use of researchtools3. regular quality checks of research tools4. blinding of study subjects and assessors5. subjects in C-C study unaware of studyhypothesis6. consider sub-study to determine validity andreliability of measurements
![Page 97: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/97.jpg)
Confounding
It occurs when there is a confounder, which is associated with both exposure
and disease independently .
Exposure Disease
Confounder
SLIDE 97
![Page 98: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/98.jpg)
Confounding
• defined as: a situation in which the measure of effect of exposure on disease is distorted because of the association of the study factor with other factors that influence the outcome.
• These other factors are called confounders
![Page 99: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/99.jpg)
A variable can be a confounder if all the following conditions are met:
• It is associated with the exposure of interest (causally or not).
• It is causally related to the outcome.
• AND ... It is not part of the exposure outcome causal pathway
![Page 100: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/100.jpg)
Alcohol Lung cancer
Smoking
Confounding: ExampleConfounding: Example
![Page 101: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/101.jpg)
Confounding: exampleConfounding: example
Drinker
Non-drinker
100 200
Lung cancer No lung cancer
50 50
50 150
50% of cases are drinkers, but only 25% of controls are drinkers.
Therefore, it appears that drinking is strongly associated with lung cancer.
![Page 102: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/102.jpg)
Confounding: exampleConfounding: example
Drinker
Non-drinker
Lung cancer No lung cancer
45 15
30 10
Drinker
Non-drinker
Lung cancer No lung cancer
5 35
20 140
Smoker
Non-smoker
Among smokers, 45/75=60% of lung
cancer cases drink and
15/25=60% of controls drink.
Among non-smokers 5/25=20% of lung
cancer cases drink and
35/175=20% of controls drink.
75
25
25
175
![Page 103: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/103.jpg)
Stratification: “Series of 2x2 tables”
Idea: Take a 2x2 table and break it into a series of smaller 2x2 tables (one table at each of J
levels of the confounder yields J tables).
Example: in testing for an association between lung cancer and alcohol drinking (yes/no),
separate smokers and non-smokers.
![Page 104: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/104.jpg)
An Example
Maternal coffee consumption during
pregnancy
Delivery of low birth weight infant
?
![Page 105: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/105.jpg)
Example
Low Birth Weight
Normal Birth Weight
Coffee17096
No Coffee9088
Crude OR = (170)(88) / (96)(90) = 1.73Crude OR = (170)(88) / (96)(90) = 1.73
![Page 106: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/106.jpg)
SmokersLow Birth Weight
Normal Birth Weight
Coffee16016
No Coffee808
Stratum-specific OR = (160)(8) / (16)(80) = 1.00Stratum-specific OR = (160)(8) / (16)(80) = 1.00
![Page 107: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/107.jpg)
Non-smokersLow Birth Weight
Normal Birth Weight
Coffee1080
No Coffee1080
Stratum-specific OR = (10)(80) / (80)(10) = 1.00Stratum-specific OR = (10)(80) / (80)(10) = 1.00
![Page 108: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/108.jpg)
Evidence of ConfoundingORcrude = 1.73
ORsmokers = 1.00
ORnon-smokers = 1.00
The association between coffee consumption and having a low birth weight baby is
confounded by smoking. This is demonstrated by the lack of effect in each stratum.
![Page 109: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/109.jpg)
Strategy #1: Does the variable meet the criteria to be a confounder?
Hypothetical case-control study of risk factors for malaria. 150 cases, 150 controls; gender distribution.
Cases ControlsMales 88 68
Females 62 82150 150
Question:Is male gender causally related to the risk of malaria?
Yes
No
Further study is needed
OR= [88 x 82] ÷ [68 x 62] = 1.71
![Page 110: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/110.jpg)
Malaria
Malegender
?
Confounder for a male gender-malaria association?
?
![Page 111: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/111.jpg)
Malaria
Malegender
?
Confounder for a male gender-malaria association?
Outdooroccupation
![Page 112: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/112.jpg)
Malaria
Malegender
?Outdooroccupation
?
First criterion: Is the putative confounder associated with exposure?
![Page 113: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/113.jpg)
. Males Females N (%) N (%)
Outdoor 68 (43.5) 13 (9.0) Indoor 88 131
156 (100) 144 (100)
Question:Is outdoor occupation associated with male gender?
Yes
No
OR=7.8
First criterion: Is the putative confounder associated with exposure?
![Page 114: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/114.jpg)
Malaria
Malegender
?Outdooroccupation
?
Second criterion: Is the putative confounder associated with the outcome (case-control status)?
![Page 115: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/115.jpg)
. Cases Controls N (%) N (%)
Outdoor 63 (42.0) 18 (12.0) Indoor 87 132
150 (100) 150 (100)
Question:Is outdoor occupation (or something for which this variable is a marker of --e.g., exposure to mosquitoes) causally related to malaria?
Yes
No
OR=5.3
Malaria
Second criterion: Is the putative confounder associated with case-control status?
![Page 116: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/116.jpg)
Third criterion: Is the putative confounder in the causal pathway exposure outcome?
.
Malaria
Malegender
?Outdoor
occupation
?
Yes, it could be
Probably not
Note: Judgment and knowledge about the socio-cultural context are critical to answer
this question
![Page 117: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/117.jpg)
Question :Provided that:• Crude association between male gender and malaria: OR=1.71
and • ... Outdoor occupation is more frequent among males, and• ... Outdoor occupation is associated with greater risk of malaria …
What would be the expected magnitude of the association between male gender and malaria after controlling for occupation (i.e., assuming the same degree of outdoor occupation in males and females)?
The (adjusted) association estimate will be smaller than 1.71
The (adjusted) association estimate will =1.71
The (adjusted) association estimate will greater than 1.71
![Page 118: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/118.jpg)
Controlling confounding
In the design• Restriction of the study• Matching
In the analysis•Restriction of the analysis•Stratification•Multivariable regression
![Page 119: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/119.jpg)
StrategyAdvantagesDisadvantagesSpecification“Include only non-smokers.”
• Easily understood• Limits generalizability• May limit sample size
Matching“Match smoking status of cases and controls”
• Useful for eliminating influence of strong constitutional confounders like age and sex
• Decision to match must be made when designing and can have irreversible adverse effects on analysis• Time consuming• Can not analyze associations of matched variables with the outcome
SLIDE 119
Control confounding at the designing stage
![Page 120: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/120.jpg)
StrategyAdvantagesDisadvantagesStratification“Conduct analysis separately for smokers and non-smokers.”
• Easily understood• Reversible
• May be limited by sample size for each stratum• Difficult to control for multiple confounders
Statistical adjustment“Conduct multivariate analysis controlling (adjusting) for smoking status.”
• Multiple confounders can be controlled.• Reversible
• Need advanced statistical techniques• Results may be difficult to understand
SLIDE 120
Control confounding at the analysis stage
![Page 121: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/121.jpg)
Restriction
We study only mothers of a certain age
Many children Downs’
35 year old mothers
![Page 122: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/122.jpg)
Matching
“Selection of controls to be identical to the cases with respect to distribution of one or
more potential confounders”.
Many children Downs’
Maternal age
![Page 123: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/123.jpg)
Multivariable regression
• Analyse the data in a statistical model that includes both the presumed cause and possible confounders
• Measure the odds ratio OR for each of the exposures, independent from the others
• Logistic regression is the most common model in epidemiology
![Page 124: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/124.jpg)
Example miners exposure and lung cancer
• one group of miners exposed to the underground environment and the other group not exposed
• Hence any differences in lung cancer rate would be due to exposure to working underground
![Page 125: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/125.jpg)
ExampleSelection bias and confounding
Bias occurs when the exposed and nonexposed groups have different risks ofdeveloping the outcome of interest forreasons other than being exposed.This can be due to selection bias orconfoundingeg. more underground workers smoke
![Page 126: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/126.jpg)
Confounding variables
In our study of miners:1. .smoking is an independent risk factor(cause) of the disease (lung cancer)2. .more underground miners smoke – iesmoking is unevenly distributed amongthe exposed and non-exposed3. .smoking is not on the causal pathwaybetween exposure and disease
![Page 127: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/127.jpg)
•When examining the relationship between an explanatory factor and an outcome, we are interested in identifying factors that may modify the factor's effect on the outcome (effect modifiers). We must also be aware of potential bias or confounding in a study because these can cause a reported association (or lack thereof) to be misleading. Bias and confounding are related to the measurement and study design. Let 's define these terms:
![Page 128: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/128.jpg)
•If the method used to select subjects or collect data results in an incorrect association. ,
•THINK >> Bias !•If an observed association is not correct because a different
(lurking) variable is associated with both the potential risk factor and the outcome, but it is not a causal factor itself ,
•THINK >> Confounding !
•If an effect is real but the magnitude of the effect is different for different groups of individuals (e.g., males vs females or blacks vs whites).
•THINK >> Effect modification!
![Page 129: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/129.jpg)
A confounding factors
• is one that affects both the exposure and the disease-that is (has an association with both the disease and the risk factor under study) that may distort relationships between the two and confound (confuse) the study results.
![Page 130: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/130.jpg)
Exposure Outcome
Third variable
ConfoundingConfounding
![Page 131: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/131.jpg)
Coffee CHD
Smoking
ConfoundingConfounding
Smoking is correlated with coffee drinking and a risk factor even for those who do not drink coffee
![Page 132: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/132.jpg)
Confounding factor :
– Drinking coffee causes CHD
– Drinking coffee may not be the cause of CHD, but rather the fact that smokers are also coffee drinkers.
![Page 133: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/133.jpg)
133
Confounding
Risk FactorIndependent
VariableCoffee
Disease Dependent Variable
CHD
CovariableConfounder
Smoking
![Page 134: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/134.jpg)
Example:
• In a study of the association between tobacco smoking and lung cancer, age would be a confounding factor if the average ages of the non-smoking and smoking groups in the study population were very different, since lung cancer incidence increases with age.
•
![Page 135: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/135.jpg)
Another example:
• the possible association between meat consumption and cancer colon may be due to other accompanying factors such as decreased intake of vegetables or increased intake of fat rather than the meat consumption itself.
![Page 136: Confounding Lecture](https://reader034.vdocuments.site/reader034/viewer/2022050710/577cb4f11a28aba7118ccdf5/html5/thumbnails/136.jpg)
problem•The annual report of POF Hospital for the year 2006 shows
200 cases of Myocardial Infarction, 35 cases of Cholecystitis, 105 cases of Pneumonia and 350 cases of Acute Gastroenteritis. The result of this report cannot be generalized on the total population of Faisalabad on account of: a. Confounding bias b. Memory bias c. Selection bias d. Berkesonian bias e. Interviewer’s bias
Key: True: d
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•Mother’s education is therefore a potential confounding variable.
•In order to give a true picture of the relationship between bottle-feeding and diarrhea of under-twos, the influence of mother’s education should be controlled.
•This could either be addressed in the research design, e.g., by selecting only mothers with a specific level of education, or it could be taken into account during the analysis of the findings by analyzing the relation between bottle-feeding and diarrhea separately for mothers with different levels of education.
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A study was done to compare the lung capacity of coal miners to the lung capacity of farm workers. The researcher studied 200 workers of each type. Other factors that might affect lung capacity are smoking habits and exercise habits. The smoking habits of the two worker types are similar, but the coal miners generally exercise less than the farm workers
1 .Which of the following is the explanatory variable in this study?a. Exerciseb. Lung capacityc. Smoking or notd. Occupation
2 .Which of the following is a confounding variable in this study?a. Exerciseb. Lung capacityc. Smoking or notd. Occupation
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• Essential principles (features) of properly designed clinical trials:
• Control of variables surrounding the experimental subjects
• The investigator has control of the subjects, the intervention, outcome measurements, and sets the conditions under which the experiment is conducted. In particular, the investigator determines who will be exposed to the intervention and who will not. This selection is done in such a way that the comparison of outcome measure between the exposed and unexposed groups is as free of bias as possible.
•
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• Randomization refers to the practice of assigning subjects to experimental or treatment groups in a completely random manner. Thus each subject has an equal chance of being placed in the experimental group. This avoids the potential bias of the researcher choosing subjects s/he feels would be most likely to benefit from the intervention for the intervention group, and a similar possible bias if the choice were left up to the subjects.
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• Blindness refers to the practice in which the researcher remains uninformed and unaware
of the identities of experimental and control groups throughout the period of
experimentation and data gathering. Thus, the researcher can remain unbiased in judging the
responses of any particular subject or group.•
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•When studies involve human subjects, it is important that the subjects also remain
uninformed as to whether they have been placed in the experimental group (receiving
the treatment) or control group (receiving the placebo). Such procedure is referred to as
double-blind (neither researcher nor subjects know who is receiving the treatment) .
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• ). This is important because some people begin to feel better if they believe they have received a treatment. Only at the end of the study would the ‘code’ (known by the statistician) be broken and the results analyzed according to who had been taking the drug and who had not.
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•So, the gold standard design for clinical trials, i.e., the least prone to bias, is the randomized
double-blind controlled trial.