Error, Bias and Confounding

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Error, Bias and Confounding

• Research Methods for Promotion of Lung Health
• Cairo, 13-22 October 2002

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Types of Error

• Random error
• Systematic error
• Selection bias
• Information bias

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Random Error
Per Cent
Size of induration, mm
4
Systematic Error
Per Cent
Size of induration, mm
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Sources of Selection Bias

• Inappropriate population studied
• Inadequate participation
• Change of classification of the determinant
• Selection of most accessible or of volunteers

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Inadequate Participation

• We want to study the association of stigma with
  diagnosis of TB
• We select a sample of the population
• 20 of the sample agree to participate
• We find that there is no association of stigma
  with TB
• Is this true?

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Inappropriate Population

• We wish to measure the impact of HIV on
  tuberculosis
• We study the trend of tuberculosis in Egypt from
  1997 to 2001
• We find no change in notification rate
• We conclude that HIV has no impact on TB
• Were we right?

8
Classification of Determinant

• We want to study the impact of poverty on the
  trend of tuberculosis in Damascus
• We select a poor district and a rich district and
  compare the notification of TB from 1991 to 2000
• In the meantime, there is an urban renewal
  project in the poor district
• We find no difference between the districts
• Can we conclude that poverty is not related to TB?

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Participation of Volunteers

• We want to determine the prevalence of HIV
  infection in Syria
• We ask for volunteers for testing
• We find no HIV
• Is it correct to conclude that there is no HIV in
  Syria?

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Minimizing Selection BiasStudy Design

• Appropriate population selection
• High participation rate
• Demonstration of lack of difference between
  participants and non participants

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Minimizing Selection BiasAnalysis

• Exclude from numerator and denominator
• Analyze by time at risk
• Worst and best case scenarios

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Source of Information Bias

• Subject variation
• Observer variation
• Deficiency of tools
• Technical errors in measurement

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Subject Variation

• We want to determine the association of knowledge
  about TB and notification of TB
• We interview TB patients in a public clinic and
  those in a private practice
• The public clinic has a program of health
  education
• We find that those who know about TB are notified
  and those who do not are not
• Is it correct that there is an association
  between knowledge about and notification of TB?

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Observer Variation

• We carry out a case control study of poverty and
  tuberculosis
• We accept any case diagnosed by a doctor
• The doctor knows that poor people are more likely
  to have TB
• Can this knowledge bias the result?

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Technical Errors

• We want to test a new antigen for the diagnosis
  of tuberculosis
• We select a case control study
• By chance, the batch of the antigen we use for
  the cases has been left unrefrigerated
• We find no difference in response to the antigen
  between cases and controls

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Minimizing Information Bias

• Specify criteria in advance
• Analyze directly according to criteria
• Reduce numbers of observers
• Monitor performance of observers
• Use standardized tools for measurement

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ConfoundingA Special Type of Bias

• A factor associated with both the outcome and a
  determinant (an etiological factor)
• Therefore associated with outcome through its
  association with the determinant (etiological
  factor)

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ConfoundingKnowledge about and Notification of TB

• Recall the study of knowledge of and notification
  of TB
• TB patients are educated about TB in the public
  sector but not in the private
• Educated TB patients are notified and those not
  educated are not
• The real reason for notification is the type of
  practice and not the knowledge of TB

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Confounding
Confounder (Knowledge)
Determinant (Type of Practice)
Outcome (Notification)
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ConfoundingAge and tuberculosis

• We find a higher proportion of reported TB cases
  in rich countries are older men
• We conclude that advancing age is a risk factor
  for tuberculosis
• Is this correct?

21
Tuberculosis Notification RateNorway, by Age
Per 100 000
1927
1947
1980
Age, years
Nor Fore Lunge 198630
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Impact of Error or Bias

• Random error will obscure a real difference
• Random error will require a larger sample size
• Bias will result in false difference
• It cannot be overcome by statistics if present