26. Errors and bias in epidemiological studies

• Chance, bias, and confounding should be ruled out in order to talk about a valid statistical association
  • A valid statistical association does not imply causation!
  • For example, incidence of prostate cancer has increased recently, as have the sales of flat-screen TVs. But there is no causation
• The two types of errors in epidemiology
  • Type 1 error – when we conclude that there is a difference when in reality there is no difference
    • To avoid type 1 errors, we strengthen the statistical power, by using statistic significance, p < 0,05, confidence intervals, etc.
  • Type 2 error – when we conclude that there is no difference when in reality there is one
    • To avoid type 2 errors, we must use a large sample size and accurate measurements
• Bias
  • A bias is a systematic error which leads us to conclusions which are systematically different from the truth
  • Bias does not apply equally to the different groups measured
• Selection bias
  • Occurs when the sample group is not representative of the population from which it is drawn
  • Examples
    • Healthy worker effect – the working population is healthier than the general population, so a sample of working people does not represent the general population
    • Volunteer bias – people who volunteer to join a study have different characteristics than the general population
      • Volunteers are generally more healthy, have lower mortality and are more likely to comply with doctor’s orders
  • Prevented by randomizing instead of selecting people to the groups, and making sure the sample group is representative of the population
• Information bias
  • Occurs during collection, analysis, and interpretation of data
  • Examples
    • Recall bias – people who are diseased may recall their exposure to risk factors better than those who are healthy
    • Interviewer bias – different interviewing approaches towards different groups prompt different responses
    • Publication bias – when the outcome of a study influences the decision to publish the study
      • Studies which find no association are often not published, despite these results being as important as studies which find associations
• Misclassification = assigning someone to the wrong group
• Bias in screening
  • Volunteer bias
  • Length bias
    • Screening selectively identifies patients with a long preclinical and clinical phase and less frequently identifies patients with shorter phases
    • The patients with long phases would have a better prognosis regardless of the screening program
  • Lead-time bias
    • Screening causes cases to be diagnosed earlier in the natural history of the disease
    • This makes it seem like the patients live longer, but this is just because of the earlier diagnosis and not because of the earlier treatment
• Atomistic fallacy
  • Observations at the individual level are not necessarily true on the populational level
  • Examples
    • Infant mortality is associated with low birthweight on an individual level, but not on the populational level
    • Same with CHD and income, or suicide and income