Bias advanced presentation.pptx

Bias in epidemiology

1. Understanding Bias SBPM Makkah 2 nd Oct, 2023

2. Lecture Learning Outcomes  By the end of this lecture, you should be able to:  Definition of bias  Types of bias  Understanding & Handling bias  Practice & examples of bias

3. Bias (Bias) Bias

4. Bias (Definition)  Systematic deviation of results or inferences from truth.  Systematic error (as opposed to a random error) that skews the observation to one side of the truth.  Processes leading to such deviation.  An error in the conception and design of a study—or in the collection, analysis, interpretation, reporting, publication, or review of data—leading to results or conclusions that are systematically (as opposed to randomly) different from truth.

5. Concept of Bias  Thus, if we use a scale that is not calibrated to zero, the weights we obtain using this scale will be biased.  Similarly, if a sample is biased (for example, more males in the sample than the proportion of males in the population, or selecting cases from a hospital and controls from the general community in a case-control study), the results tend to be biased.  Bias are often inevitable  Since it is often difficult to correct for biases once the data have been collected, it is always advisable to avoid bias when designing a study.

6. Bias As Deviation From the Truth Ways in which deviation from the truth can occur include:  Systematic variation of measurements or estimates from the true values.  Variation of statistical estimates (means, rates, measures of association, etc.) from their true values as a result of statistical artifacts or flaws in study design, conduct, or analysis.  Deviation of inferences from truth as a result of conceptual or methodological flaws in study conception or design, data collection, or the analysis or interpretation of results.  A tendency of procedures to yield results or conclusions that depart from the truth (in study design, data collection, analysis, interpretation, review, or publication)  Prejudice leading to the conscious or unconscious selection of research hypotheses or procedures that depart from the truth in a particular direction or to one-sidedness in the interpretation of results.

7. Types of Bias  Aggregation bias (ecological bias)  Allocation bias  Ascertainment bias  Attrition bias  Auxiliary hypothesis bias  Berksonian bias & Berkson ’ s bias  Bias due to instrument error  Bias due to withdrawals  Bias in assumptions  Bias in handling outliers  Bias of an estimator  Cognitive dissonance bias  Collider bias  Disconfirmation bias  Disease progression bias  Information bias  Interpretive bias.  Lead-time bias.  Length bias.  Measurement bias.  Mechanistic bias  Performance bias.  Publication bias.  Recall bias.  Reporting bias  Rescue bias.  Response bias.  Reverse causation bias.  Selection bias.  Spectrum bias.  Verification bias.  Workup bias.  Confirmation bias  Confounding bias  Consent bias  Detection bias  Diagnostic suspicion bias

8. Allocation Bias Allocation bias:  An error in the estimate of an effect caused by failure to implement valid procedures for random allocation of subjects to intervention and control groups in a clinical trial or in another type of randomized study (randomized field trials, randomized community trials).

9. Ascertainment Bias Ascertainment bias:  Systematic failure to represent equally all classes of cases or persons supposed to be represented in sample  This bias may arise because of the nature of the sources from which persons come (e.g., a specialized clinic);  From a diagnostic process influenced by culture, or idiosyncrasy; or,  In genetic studies, from the statistical chance of selecting from large or small families.

10. Attrition Bias Attrition bias:  A type of selection bias due to systematic differences between the study groups in the quantitative and qualitative characteristics of the processes of loss of their members during study conduct; i.e., due to attrition among subjects in the study.  Different rates of losses to follow-up in the exposure groups may change the characteristics of these groups irrespective of the studied exposure or intervention, or losses may be influenced by the positive or adverse effects of the exposures.

11. Auxiliary Hypothesis Bias Auxiliary hypothesis bias:  A form of rescue bias and thus of interpretive bias , which occurs in introducing ad hoc modifications to imply that an unanticipated finding would have occurred otherwise had the experimental conditions been different.  Because experimental conditions can easily be altered in many ways, adjusting a hypothesis is a versatile tool for saving a cherished theory.

12. Berksonian & Berkson ’ s Bias Berksonian bias:  A general term to indicate all types of bias that have the structure of selection bias, based on the assumption that Berkson originally described a bias with that structure. Berkson ’ s bias: ( Syn: Berkson ’ s fallacy ):  A form of selection bias that arises when the variables whose association is under study affect selection of subjects into the study.  It is a particular concern in hospital-based studies, especially when prevalent or previously diagnosed cases are not excluded.

13. Berkson ’ s Bias Berkson ’ s bias: ( Syn: Berkson ’ s fallacy ):  Joseph Berkson (1899-1982) described an imaginary hospital- based case-control study wherein the controls are patients with other diseases, and the “exposure” is also a disease; he noted that in such a study the association between the disease prevalences is expected to differ from the corresponding association in the general population.  This difference in the association has historically been referred to as Berkson ’ s bias.

14. Berkson ’ s Bias Berkson ’ s bias: ( Syn: Berkson ’ s fallacy ):  The selection process into the hospital is such that a hospital- based case-control study inevitably yields an association between prevalent diseases.

15. Bias Due to Withdrawals Bias due to withdrawals:  A difference between the true effect and the association observed in a study due to characteristics of subjects who choose to withdraw.

16. Bias Due to Instrument Error Bias due to instrument error:  Systematic error due to faulty calibration, inaccurate measuring instruments, contaminated reagents, incorrec t dilution or mixing of reagents, etc . See also contamination, data; information bias; measurement bias.

17. Bias in Epidemiologic Studies Bias in Epidemiologic Studies

18. Bias in Epidemiologic Studies (Types)  Several types of bias exist in research.  Sackett et al. have listed 19 types of bias commonly encountered in epidemiological studies.  Choi has expanded this list further to 65.

19. Bias in Epidemiologic Studies (Types)  Selection bias  Information bias  Confounding

20. Bias in Epidemiologic Studies (Selection Bias)  It is a systematic error resulting from participant selection procedures or factors influencing participation.  Occurs in all types of study ( observational & experimental ).  Cannot be corrected analytically ( must be prevented )

21. Bias in Epidemiologic Studies (Selection Bias) Prevalence-incidence bias:  The high case-fatality rate in the early stages of clinically manifested coronary artery disease may invalidate the study of possible etiological factors, since the persons available for study as cases are the survivors (severe cases are absent). Likewise, myocardial infarction may be silent. Clinical features may be absent, and the biochemical and electrocardiographic changes in myocardial infarction may return to normal after an infarct (these mild cases will not appear among cases for study). The type of bias introduced into the study may be clear by contrasting a cohort study (where the disease is identified in all its forms).

22. Bias in Epidemiologic Studies (Selection Bias)  Sampling bias.  Ascertainment bias: • Surveillance • Referral, admission • Diagnostic  Participation bias: • Self-selection ( volunteerism ) • Non-response, refusal • Healthy worker effect, survival

23. Bias in Epidemiologic Studies (Selection Bias)  Minimising selection bias:  Clear definition of study population  Explicit case and control definitions  Cases and controls from same population

24. Bias in Epidemiologic Studies (Information Bias)  Systematic error in the measurement of information on exposure or outcome.  Differences in accuracy:  of exposure data between cases and controls.  of outcome data between different exposure groups.  Study subjects are classified in the wrong category.

25. Types of Information Bias  Interviewer bias  Recall bias  Reporting bias  Publication bias  Follow up bias

26. Types of Information Bias  Interviewer bias:  Investigator asks cases and controls differently about exposure Cases of listeriosis Controls b Eats soft cheese a Does not eat soft cheese c d  Investigator may probe listeriosis cases about consumption of soft cheese  Overestimation of “a” → Overestimation of OR

27. Types of Information Bias  Recall bias:  Cases remember exposure differently than controls Mothers of Children with malformation Controls b Took tobacco, alcohol, drugs a Did not take c d  Mothers of children with malformations will remember past exposures better than mothers with healthy children.  Overestimation of “a” → Overestimation of OR

28. Types of Information Bias  Follow up bias:  Unexposed are less likely diagnosed for disease than exposed  Example: • Cohort study to investigate risk factors for mesothelioma. • Difficult histological diagnosis. • Histologist more likely to diagnose specimen as mesothelioma if asbestos exposure known.

29. Berkisonian Bias  A special example of bias after D r. Joseph Berkson who recognized this problem.  The bias arises because of the different rates of admission to hospitals for people with different diseases (i.e., hospital cases and controls)

30. Berkisonian Bias Example:  Household interviews were performed on random samples of the general population asking about musculoskeletal and respiratory diseases and recent hospitalizations. In the general population, there appeared to be no association between these two disorders (OR = 1.06), but in the subset of the population who had been in hospital during the previous six months, individuals with musculoskeletal disorders were more likely to have respiratory disease than not (OR = 4.06).  This occurred because individuals with both disorders were more likely to be hospitalized than those with only one of the disorders.

31. Misclassification of diagnosis Available at: http://www.sph.emory.edu/activepi/Instructors/Kevin_MSword/Lesson_9boh.htm. Accessed on Oct 18, 2011 . Why misclassification of disease status? Incorrect Diagnosis Limited knowledge Diagnostic process complex Inadequate access to technology Laboratory error Disease subclinical Detection bias (e.g. more thorough exam in exposed) Subject Self report Incorrect recall Reluctant to be truthful Records incorrectly coded in data-base

32. Misclassification of exposure Available at: http://www.sph.emory.edu/activepi/Instructors/Kevin_MSword/Lesson_9boh.htm. Accessed on Oct 18, 2011 . Why misclassification of exposure status? Imprecise measurement Subject Self report Interviewer bias Incorrect coding of exposure data

33. Differential misclassification: When the misclassification results in exposure is incorrectly more in cases than in controls. Or vice versa; like one group has a lot more incorrect information than the other group Non-differential misclassification: When the misclassification is not related to exposure status or disease status. And is occurring at the same proportion in both groups; e.g. if 20% of cases are classified incorrectly on exposure in cases and about 20% in controls too Types of misclassification (differential and non-differential)

34. An obstetrician wanted to study the association between congenital malformations and history of infections during pregnancy. He interviewed women (a group who delivered children with malformations, and a group of women with normal children). He asked about history of all types of infections during pregnancy. 1. What type of misclassification is this?

35. After finishing the interviews, he also wanted to go through the women's’ medical records, in order to minimize recall bias. He discovered that women who had a baby with malformation tended to remember all infections during pregnancy more than the mothers with normal babies. What kind of misclassification is this? 1. What type of misclassification can occur in this type of study? Differential misclassification. (differential recall of exposure)

36. Differential misclassification The “exposure” to infection during pregnancy was remembered more in the “case” group than the “control” group. In reality, women with normal children were exposed to infections during pregnancy too, but they did not remember it. This makes the reported ”exposure ” inaccurately differ between cases and controls.

37. Bias in Prospective Cohort Studies  Loss to follow up:  The major source of bias in cohort studies  Assume that all do / do not develop outcome?  Ascertainment and interviewer bias:  Some concern: Knowing exposure may influence how outcome determined.  Non-response, refusals:  Little concern: Bias arises only if related to both exposure and outcome.  Recall bias:  It is not a problem: Exposure determined at time of enrolment.

38. Bias in Retrospective Cohort & Case-control Studies  Ascertainment bias, participation bias, interviewer bias:  Exposure and disease have already occurred → differential selection / interviewing of compared groups possible.  Recall bias:  Cases (or ill) may remember exposures differently than controls (or healthy)

39. Controlling for Information Bias - Blinding prevents investigators and interviewers from knowing case/control or exposed/non-exposed status of a given participant - Form of survey mail may impose less “white coat tension” than a phone or face-to-face interview - Questionnaire use multiple questions that ask same information acts as a built in double-check - Accuracy multiple checks in medical records gathering diagnosis data from multiple sources

40. Bias Due to Confounding  Bias due to confounding factors.  Confounding factors: associated with both exposure and out come  Differs from selection and information bias because it can be evaluated and controlled to some extent in the analysis phase of the study.  It can be removed by matching cases and controls.

41. References Main Textbook: 1. K. Park's (2015): Textbook of Preventive and Social Medicine. Banarsidas Bhanot-Jabalpur. 23rd edition. Other references: 1. Text Book of Public Health and Community Medicine. RajVir Bhalwar, Department of Community Medicine, Armed Forces Medical College, Pune, in collaboration with WHO, India Office, New Delhi (2009). 2. Lucas, A. and Gilles, H. (2003): Short Textbook of Public Health Medicine for the tropic, 4th edition, Oxford University Press Inc., New York, USA. 3. Portney, L. G. and Watkins, M. P. (2008): Foundation of Clinical Research. Applications to Practice. 3rd edition. 4. Kumar, R. (1996): Research methodology. A step by step guide for beginners. 3rd edition. 5. Miller, D. C. (1991): Handbook of Research Design and Social Measurement. 5th edition. 6. Altman, D. G. (1991):Practical statistics for medical research. Boca Ratón, Chapman & Hall/ CRC; Websites: 1. World Health Organization (WHO): http://www.who.ch 2. Centers for Disease Control and Prevention (CDC), USA: http://www.cdc.gov 3. The Johns Hopkins Bloomberg School of Public Health, OPENCOURSEWARE (OCW) project: http://ocw.jhsph.edu 4. The WWW Virtual Library (Medicine and Health): Epidemiology (http://www.epibiostat.ucsf.edu/epidem/epidem.html).