3.5.2 selection bias

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Selection bias• Selection bias

– Bias resulting from systematic error in ascertainment or participation of study subjects

– People having different probabilities of being included in the study sample based on exposure and outcome

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Selection bias• Selection bias

– Generally, when both exposure and outcome affect selection/participation, the exposure-outcome association in the sample is no longer representative of that association in the source population

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Selection bias• Selection bias in case-control studies

– Cases are generally more likely to participate in case-control studies than controls – the fact that they have a disease increases motivation to participate

– The bias arises when exposure status also affects probability of participation in a case-control study

– If among either the case or the control subjects, exposure makes subjects more or less likely to participate in the study, then selection bias is introduced

Selection bias• Illustration of case-control selection bias with 2x2 table

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Selection bias• Selection bias in cohort studies

– Selection bias related to differential participation by exposure and disease status is less likely to occur in cohort studies because subjects are selected and enrolled prior to disease onset

– If exposure status does affect selection into the study it is still unlikely that (future) outcome will affect selection


– Example: a concurrent occupational cohort study comparing textile factory floor workers with workers in less polluted parts of the factory in which textile factory floor workers are more likely to participate

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– Selection bias is still an issue in cohort studies

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– Differential loss to follow-up is a substantial concern in cohort studies• It is sometimes discussed as information bias,

sometimes as selection bias• If the outcome or some factor associated with the

outcome affects the probability of being lost, and exposure affects probability being lost as well, then selection bias will be present

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– Differential loss to follow-up has the same practical effect as differential selection into the study—it distorts the association of interest in the sample, relative to the source population

– This makes the exposure-outcome association in the study systematically different from the association in the source population—biased

Selection bias• Illustration of cohort loss to follow-up with 2x2 table

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Selection bias• Illustration of cohort loss to follow-up with 2x2 table

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Selection bias• Some specific selection biases

– Berkson’s bias: occurs in case-control studies of hospitalized patients

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Selection bias– Berkson’s bias:– If both exposure and outcome affect

hospitalization (and therefore selection into the study) a statistical association between exposure and outcome will be induced in the hospitalized population

– Particular concern when the exposure and the outcome are both health conditions that can cause hospitalization (e.g., studies of the effects of hypertension on skin cancer)

Selection bias• Illustration of Berkson’s bias:

– Joint distribution of E and D in the general population

– RR?– RR = (25/50)/(25/50) = 1

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Selection bias• Hospitalization

– Everyone exposed is hospitalized• Example: HTN as exposure

– Everyone with outcome is hospitalized• Example: skin cancer as outcome

– Fewer of those with neither exposure nor outcome are hospitalized

Selection bias• Illustration of Berkson’s bias:

– Joint distribution of E and D in the hospitalized population

– RR?– RR = (25/50)/(25/30) = 0.6

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– Healthy worker effect: occurs in occupational cohort studies or other studies comparing working to non- working or general population groups

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Selection bias– Healthy worker effect:– Workers have lower rates of disease than

comparison cohorts from the general population• Self-selection of hardier workers into more taxing

jobs• Attrition of sick workers from the workforce

– Comparison of occupational cohort of workers to non-workers, general population groups, or workers in less taxing jobs may cause the health effects of occupational exposures to be under-estimated

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– Self-selection bias: occurs in all studies in which individuals decide whether to participate

– When people who choose to enroll in studies are different from those who do not, selection bias may be introduced

– This becomes more problematic as response rate/participation rate decreases

Selection bias• Numerical examples of selection bias

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Selection bias• Hypothetical population with true exposure and

disease status

21Szklo Table 4-1


disease status

• If everyone were included in a study, sampling fraction would 1.0 for all cells

22Szklo Table 4-1


disease status

• OR?• OR = (500/1800)/(500/7200) = 4• OR = oddse/oddsu

23Szklo Table 4-1

Selection biasexposed

β

δ

c

a

d b

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If b/β = d/δ then the exposure distribution in the controls represents the exposure distribution in the total non-diseased population


PTe d b

c

a

PTu

If b/PTe = d/PTu then the exposure distribution in the controls represents the exposure distribution in the total cohort person time

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c

a

d b

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If b/Ne = d/Nu then the exposure distribution in the controls represents the exposure distribution in the total population at baseline

Nu

Ne

Selection bias• Sampling fraction 1.0 for cases, 0.1 for controls

(example modified from Szklo)

• OR?• OR = (500/180)/(500/720) = 4• OR = pseudo-oddse/pseudo-oddsu

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Selection bias• Selection bias will be present in the sample if

exposure distribution in the cases or controls is not the same as it is in the diseased and non- diseased in the study base

• This occurs if the sampling fraction is different depending on exposure for cases and/or controls

Selection bias• Sampling fraction different by exposure for

controls

• OR?• OR = (500/324)/(500/576) = 1.8

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Selection bias• Sampling fraction different by exposure for

cases

• OR?• OR = (500/180)/(400/720) = 5.0

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Selection bias• Different selection biases can end up canceling

each other out theoretically, however in practice it’s impossible to know whether the biases cancel

• Biases cancelled or equalized if magnitude of bias in selection of cases same as in selection of controls

Selection bias• Sampling fraction differential by exposure for

cases and controls – same magnitude

• OR?• OR = (500/180)/(400/576) = 4.0

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Selection bias

• Selection fractions cancel in OR calculation• OR = [(a*1)/(β*0.1)]/[(c*0.8)/(δ*0.08)]• OR = [10(a/β)]/[10(c/δ)]• OR = (a/β)/(c/δ)

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Selection bias• If selection/participation/loss is differential only

with respect to disease– Biased:

••

•

Measures of disease AR, CIR, IDR

– Unbiased:OR

• If selection/participation/loss is differential with respect to exposure and disease, all measures of disease and association will be biased

– Excepting the rare situations in which the biases cancel

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Selection bias• Selection biases types summary

– Self selection bias– Case-control studies

• Berkson’s bias• People with particular exposure/disease

combinations more or less likely to participate– Cohort studies

• Loss to follow-up• Healthy worker effect• People with particular exposure/(future)disease

combinations more or less likely to participate or to be lost to follow-up

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• A case-control study of postmenopausal hormone therapy and endometrial cancer

• Cases and controls are recruited from same medical practice

• Women with endometrial cancer are more likely to have symptoms that lead them to visit doctor

• Women taking postmenopausal hormone therapy are more likely to have symptoms that lead them to visit doctor

• Women are enrolled in the study from the medical practice

Selection bias

• Selection factor is visiting the medical practice where study recruitment is being carried out

• Alters any true association between HRT and endometrial cancer

HRT

Endometrialcancer

Visit medical practice,

recruited for study

Selection bias37

3.5.2 selection bias

Education

Transcript of 3.5.2 selection bias