Methods in · PDF file02/11/2014 6 Methods in epidemiology – Medical statistics – Bias Selection bias Patients’ selection Sampling is based on convenience rather than onPublished

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Methods in epidemiology – Medical statistics – Bias

Systematic errors (biases)

Methods in Epidemiology


At the end of the lecture students should be able

� to detail the main types of bias and how they can affect

the interpretation of results

Medical statistics

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Actual study

Implementation

Data

Study sample

Planning

Target population

Study population

Study question

Study protocol

Study object Variables to be measured

Structure of clinical research

Inference

Parameter Estimate

iθθθθ̂

Study conclusions

Tθθθθ ( )

iSEθθθθθθθθ ˆ=


Accuracy of conclusions

( )si

E θθθθθθθθ =ˆ iθ̂

ES

� Any conclusions on is

based on the sample estimatei

θθθθ̂

� Actually the ‘true’ effect we are

looking for is Tθθθθ

� We use information from sampling

distribution to infer conclusions on the

‘true’ effect based on the single estimates

actually observed

( )TSi

E θθθθθθθθθθθθ ==ˆ

� Our conclusions will be accurate if

( )si

E θθθθθθθθ =ˆ

iθ̂

ES

( )TSi


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( )Si

E θθθθθθθθ =ˆiθ̂

SE

� is the assumption

for a correct inference

� Conversely, if ,

the assumption is unmet and the

conclusion is inaccurate

Tθθθθ

Random error

Systematic error


( )si


ES

( )TSi


( )TSi

E θθθθθθθθθθθθ ≠=ˆ

� when both

systematic and random errors

are present

( )TSi

E θθθθθθθθθθθθ ≠=ˆ


True effect +

Systematic error +

Random error =

Observed effect

True effect +

Random error =

Observed effect

( )Si

E θθθθθθθθ =ˆiθ̂

SE

Tθθθθ

Random error

( )si


ES

Systematic error


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Systematic errors (bias)

Bias results in false understanding about true differences between groups and generates misleading patterns of disease.

May occur in all steps: planning and implementation of studies, data analysis and interpretation of results


Actual study

Implementation

Data

Study sample

Planning

Target population

Study population

Study question

Study protocol

Study object Variables to be measured

Structure of clinical research

Inference

Parameter Estimate

iθθθθ̂

Study conclusions

Tθθθθ ( )

iSEθθθθθθθθ ˆ=

Systematic errors

Randomand

Systematic errors

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� Selection bias

� Information bias

� Confounding

Are systematic errors more

likely in observational or

experimental studies?

Bias results in false understanding about true differences between groups and generates misleading patterns of disease.

May occur in all steps: planning and implementationof studies, data analysis and interpretation of results

Systematic errors (bias)


Selection bias


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Selection bias

� Patients’ selection

Sampling is based on convenience rather than on representativeness, e.g. volunteers, specific sources of subjects, captive populations, health records.

A systematic errors that stems from the procedures used to select patients and from factors that influence study participation.

The association between exposure and outcome differs between participating and not participating subjects


Hutchins et al. 1999

US (≥ 65)

Hutchins et al. 1999

SWOG (≥ 65)

15

Langer et al. 2002

ECOG (≥70)

Selection bias?

Perrone et al. 2002

(=>70)

53

Eld

erl

yp

azie

nts

Elderly patients in clinical trials

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Selection bias

� Patients’ selection

Sampling is based on convenience rather than on representativeness, e.g. volunteers, specific sources of subjects, captive populations, health records.

Is related to people. Stems from the procedures used to select patients and from factors that influence study participation.

The association between exposure and outcome differs between participating and not participating subjects

� Study participation

� Compliance with participation (patient’s preferences, drop-out)

� Different chances of being admitted to hospital (Berkson’s bias)

� Different chances of being included in the analysis


Berkson’s bias

General population Subjects admitted to hospital

in the last 6 months

Respir

Dis

Musculo skeletal

dis

YES NO Total

YES 17 207 224

NO 184 2376 2560

Total 201 2583 2784

Association between respiratory diseases and musculoskeletal disorders

Respir

Dis

Musculo skeletal

dis

YES NO Total

YES 5 15 20

NO 18 219 237

Total 23 234 257

8.5% 8.0% 8.0% 21.7% 6.4% 7.8%

29.4% 7.2%

9.8% 9.2%

P = 0,009

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585 (26,8%) children did not undergo the tubercolin test:

107 because were not at school the day of test and 478

because their parents did not give consent

Maselli et al. (Monaldi Arch Chest Dis 1997) studied the

prevalence of tuberculosis infection in a random sample of

2.182 children in the city of Napoli.

Missing data:

� not informative (do not affect conclusions)

� informative (may affect conclusions)


Information bias


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Information bias

� Inaccurate measurements devices

� Inequalities in healthcare or in efforts or time to collect data between the compared groups (biased follow up)

� Discrepancy in obtaining exposure information after disease has occurred (recall bias)

Is related to variables. A subject is misclassified when is placed in an incorrect category of exposure or outcome. It may be:

• differential when misclassification of exposure (outcome) is different for those with and without disease (exposure)

• nondifferential when misclassification of exposure (outcome) is unrelated to the presence of disease (exposure)


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Information bias



� Discrepancy in obtaining exposure information after disease hasoccurred (recall bias)

Is related to variables. A subject is misclassified when is placed in an incorrect category of exposure or outcome.

� Interpretation and reporting of results guided by the researcher’s interests (interpretation bias)


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Information bias



� Discrepancy in obtaining exposure information after disease hasoccurred (recall bias)

Is related to variables. A subject is misclassified when is placed in an incorrect category of exposure or outcome.

� Interpretation and reporting of results guided by the researcher’s interests (interpretation bias)

� Selective reporting of results (publication bias)

Metodologia clinica 5.4 Le persone

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Confounding


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Confounding

Is related to group comparison.


The effect of the exposure on a given outcome is

the difference between the observed progression

of the disease as a consequence of the exposure

and the progression that would have been

observed if subjects would not have been exposed

What is an exposure effect?

This is a counterfactual argument and the exposure effect may not be directly assessed

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EXPOSURE

(indipendent of researcher)

STUDY POPULATION

(exposed + not exposed)

STUDY POPULATION

(not exposed)

EXPOSURE

(determined by researcher)

Observational and experimental studies

exposed not exposed exposed not exposed

The exposure effect is estimated by the comparison of the observed effects in exposed and not exposed subjects

To ensure that estimates be accurate and effect be attributable toexposure the compared groups must have similar characteristics


Is related to group comparison. The effect of the exposure is

mixed together with the effect of one or more other variables

leading to bias.

Confounding

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Exposure DiseaseThe exposure is

associated with disease

Confounding variable

Confounding

Is related to group comparison. The effect of the exposure is

mixed together with the effect of one or more other variables

leading to bias.


Smoking

(true determinant)

Alcohol

assumptionLung cancer

Smoking is a confounder for alcohol assumption

Smoking (the true determinant) is associated with both alcohol

assumption (exposure) and lung cancer (outcome)

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Smoking

(true determinant)

Alcohol

assumption

Lung cancer

Alcohol assumption is not a confounder for smoking

Alcohol assumption is associated with smoking (the true

determinant) but not with lung cancer (outcome)


Confounding

� must be inbalanced between the exposure groups to be

compared

� must be associated with the disease

� must be associated with the exposure

� must not be an effect of the exposure

A confounder may be a true determinant or only a proxy or a

marker an unknown true cause

May be prevented by randomization or controlled by

stratification

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Healthy-user effect

Women assuming hormone

replacing therapy had a higher

cultural and social level

> propensity to

use HRT< risk of

CHD


Inconsistent results

between observational

and experimental

studies may follow a

not complete

adjustment by social

level in the analysis

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Kigozi et al. (BMC Health Serv Res 2011) estimated the prevalence of HIV in

600 TB patients. Only 405 consented to be tested. Baseline patients’

characteristics are reported in the table by HIV testing. Could missing data

affect the HIV prevalence estimate?

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Methods in · PDF file02/11/2014 6 Methods in epidemiology – Medical statistics – Bias Selection bias Patients’ selection Sampling is based on convenience rather than onPublished