Validity and bias in epidemiological study

Validity and bias are essential aspects of any research—a brief description of internal and external validity and different types of bias related to the epidemiological study.

1. Validity & Biases in epidemiological studies Dr. Abhijit Das PG resident Govt. Bundelkhand Medical College, Sagar, M.P.

2. If an association is observed, the first question asked must always be … “Is it REAL?” “Is it a VALID observation?”

3. Validity of epidemiologic study concerns whether or not there are imperfections in the study design, the methods of data collection, or the methods of data analysis that might distort the conclusions made about an exposure-disease relationship. No such imperfections Study is VALID .

4. If not valid (Internal/External) There are imperfections The extent of the distortion of the results from the correct conclusions is called Bias

5. bias “Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease.” Lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population. Schlesselman JJ. Case-Control Studies: Design, Conduct, and Analysis. New York: Oxford University Press; 1982.

6. Classified by the research stage in which they occur or by the direction of change in a estimate. Definition and selection of the study population, data collection, and the association between different determinants of an effect in the population.

7. 4 1 1 2 3 Sacket and Choi According to the stages of research Maclure and Schneeweiss Steineck and Ahlbom Applying the causal diagram theory Based on the concept of study base 4 Kleinbaum et al Three main groups Selection bias, Information bias, and Confounding CLASSIFICATION OF BIAS

8. Selection Bias Information Bias Confounding Biases in a trial

9. Selection bias The error introduced when the study population does not represent the target population. At any stage of a research Design (bad definition of the eligible population, lack of accuracy of sampling frame, uneven diagnostic procedures in the target population) and implementation.

10. Examples of Selection Bias Select volunteers as exposed group and non-volunteers as non - exposed group in a study of screening effectiveness Study health of workers in a workplace exposed to some occupational exposures comparing to health of general population Working individuals are likely to be healthier than genera l population that includes unemployed people (Healthy Worker Effect) Use prevalent cases instead of incidence cases

11. Controlling Selection Bias Define criteria of selection of diseased and non-diseased participants independent of exposures in a case-control study Define criteria of selection of exposed and non-exposed participants independent of disease outcomes in a cohort study

12. Inappropriate definition of eligible population A scertainment bias- Competing risks , Healthcare access bias, Length-bias sampling, Neyman bias , Spectrum bias, Survivor treatment selection bias Selection of control- Berkson’s bias, Exclusion bias , Friend control bias, Inclusion bias , Matching bias, Relative control bias Lack of accuracy of sampling frame Non-random sampling bias T elephone random sampling bias Citation bias D i s semina t ion bias Publication bias Uneven diagnostic procedures in the target population During study i m p leme n ta t ion L os ses/ w ithdrawals to follow up Missing information in multivariable analysis Non-response bias SELECTION BIAS

13. Competing risks When two or more outputs are mutually exclusive, any of them competes with each other in the same subject. For example, early death by AIDS can produce a decrease in liver failure mortality in parenteral drug users. A proper analysis of this question should take into account the competing causes of death; for instance. All studies

14. Healthcare access bias Observational study When the patients admitted to an institution do not represent the cases originated in the community. Popularity bias Centripetal bias Referral filter bias Diagnostic/treatment access bias

15. Neyman bias (incidence-prevalence bias/selective survival bias) Where the very sick or very well (or both) are erroneously excluded from a study. The bias (“error”) in results can be skewed in two directions Excluding patients who have died will make conditions look less severe. Excluding patients who have recovered will make conditions look more severe. Preferable to use incident cases instead of prevalent cases.

16. Both cross sectional and (prevalent) case-control studies. Any risk factors we may identify in a study using prevalent cases may be related more to survival with the disease than to the development of the disease (incidence). Careful selection of study type can help to lessen the effects from this bias

17. Spectrum bias In the assessment of validity of a diagnostic test When researchers included only ‘‘clear’’ or ‘‘definite’’ cases, not representing the whole spectrum of disease presentation, and/or ‘‘clear’’ or healthy controls subjects, not representing the conditions in which a differential diagnosis should be carried out. Sensitivity and specificity of a diagnostic test are increased.

18. Berkson’s bias First described by Berkson in 1946 for case- control studies. Hospital control It is produced when the probability of hospitalization of cases and controls differ, and it is also influenced by the exposure. Hospital patients differ from people in the community

19. Friend control bias It was assumed that the correlation in exposure status between cases and their friend controls lead to biased estimates of the association between exposure and outcome. Problem may be that the controls are too similar to the cases in regard to many variables, including the variables that are being investigated in the study.

20. Matching It is well known that matching, either individual or frequency matching, introduces a selection bias, which is controlled for by appropriate statistical analysis Overmatching is produced when researchers match by a non-confounding variable and can underestimate an association

21. Inappropriate definition of eligible population A scertainment bias- Competing risks , Healthcare access bias, Length-bias sampling, Neyman bias , Spectrum bias, Survivor treatment selection bias Selection of control- Berkson’s bias, Exclusion bias , Friend control bias, Inclusion bias , Matching bias, Relative control bias Lack of accuracy of sampling frame Non-random sampling bias T elephone random sampling bias Citation bias D i s semina t ion bias Publication bias Uneven diagnostic procedures in the target population During study i m p leme n ta t ion L os ses/ w ithdrawals to follow up Missing information in multivariable analysis Non-response bias SELECTION BIAS

22. Lack of accuracy of sampling frame Non-random sampling bias: most common bias in this group Telephone random sampling bias : It excludes some households from the sample, thus producing a coverage bias. In the US it has been shown that the differences between participants and non-participants are generally not large, but the situation can be very different in less developed countries Citation bias Dissemination bias Publication bias

23. Uneven diagnostic procedures in the target population In case-control studies Detection bias Diagnostic suspicion bias : types of detection bias, exposure is taken as another diagnostic criterion Unmasking-detection signal-bias : exposure that, rather than causing a disease, causes a sign or symptom that precipitates a search for the disease Mimicry bias : exposure may become suspicious if, rather than causing disease, it causes a benign disorder which resembles the disease

24. During study implementation Losses/withdrawals to follow up : in both cohort and experimental studies. Missing information : multivariable analysis selects records with complete information on the variables included in the model. If participants with complete information do not represent target population, it can introduce a selection bias.

25. Non-response bias: A bias that occurs due to systematic differences between responders and non-responders This bias is common in descriptive, analytic and experimental research and survey studies Results in mistakes in estimating population characteristics based on the underrepresentation of phenomena due to non-response

26. Information Bias Information bias occurs when information is collected differently between two groups, leading to an error in the conclusion of the association When information is incorrect - there is misclassification Differential misclassification Non-differential misclassification

27. Examples of Information Bias Interviewer knows the status of the subjects before the interview process Interviewer may probe differently about exposures in the past if he or she knows the subjects as cases Subjects may recall past exposure better or in more detail if he or she has the disease (recall bias) Surrogates, such as relatives, provide exposure information for dead cases, but living controls provide exposure information themselves

28. Controlling Information Bias Have a standardized protocol for data collection Make sure sources and methods of data collection are similar for all study groups Make sure interviewers and study personnel are unaware of exposure/disease status Adapt a strategy to assess potential information bias Blinding

29. Differential misclassification bias Detection bias Observer/interviewer bias Recall bias Reporting bias Non-differential misclassification bias Misc l assif i cation b i as INFORMATION BIAS Ecological fallacy Regression to mean Others Hawthorne effect Temporal ambiguity Will Rogers phenomenon Work up bias (verification bias) Lead time bias Protopathic bias

30. Misclassification bias When individuals are assigned to a different category than the one they should be in. Non-differential misclassification occurs when the probability of individuals being misclassified is equal across all groups in the study. Differential misclassification occurs when the probability of being misclassified differs between groups in a study

31. Observer/interviewer bias Knowledge of the hypothesis, the disease status, or the exposure status (including the intervention received) can influence data recording (observer expectation bias) Type of detection bias; affect assessment in observational and interventional studies Blinding/adequate training for observers in how to record findings

32. Recall bias Rumination bias- the presence of disease influences the perception of its causes Exposure suspicion bias- the search for exposure to the putative cause Participant expectation bias- in a trial if the patient knows what they receive may influence their answers More common in case-control studies, although it can occur in cohort studies and trials.

33. Reporting bias Systematic distortion that arises from the selective disclosure or withholding of information by parties Obsequiousness bias Family aggregation bias Underreporting bias

34. Ecological fallacy When analyses realised in an ecological (group level) analysis are used to make inferences at the individual level. For instance, if exposure and disease are measured at the group level (for example, exposure prevalence and disease risk in each country), exposure-disease relations can be biased from those obtained at the individual level (for example, exposure status and disease status in each subject).

35. Hawthorne effect Described in the 1920s in the Hawthorne plant of the Western Electric Company (Chicago, IL). It is an increase in productivity—or other outcome under study—in participants who are aware of being observed. A study of hand-washing among medical staff found that when the staff knew they were being watched, compliance with hand-washing was 55% greater than when they were not being watched ( Eckmanns 2006)

36. Lead time bias A distortion overestimating the apparent time surviving with a disease caused by bringing forward the time of its diagnosis The added time of illness produced by the diagnosis of a condition during its latency period. This bias is relevant in the evaluation of the efficacy of screening, in which the cases detected in the screened group has a longer duration of disease than those diagnosed in the non-screened one

37. Lead time bias where health outcome is the same in someone whose disease is detected by screening compared with someone whose disease is detected from symptoms, but survival time from the time of diagnosis is longer in the screened patient Lead time bias where the screened patient lives longer than the unscreened patient, but overall survival time is still exaggerated by the lead time from earlier diagnosis.

38. Verification bias/ work-up bias Occurs during investigations of diagnostic test accuracy when there is a difference in testing strategy between groups of individuals, leading to differing ways of verifying the disease of interest. In the assessment of validity of a diagnostic test, it is produced when the execution of the gold standard is influenced by the results of the assessed test, typically the reference test is less frequently performed when the test result is negative

39. Confounding From confounder, i.e. to mix together. A distortion that modifies an association between an exposure and an outcome because a factor is independently associated with the exposure and the outcome. Not possible to separate the contribution that any single causal factor has made. Solution: Study design: Matching/randomization, Stratification and statistical adjustment smoking Ca esophagus alcohol

40. Bias and Confounding Systematic error in a study Cannot be fixed May lead to errors in the conclusion When confounding variables are known, the effect may be fixed

41. Specific Biases in a trial Allocation of intervention bias Compliance bias Contamination bias Lack of intention to treat analysis

42. Allocation of intervention bias Systematic difference in how participants are assigned to comparison groups in a clinical trial More common in non- randomised trials Sequentially numbered, opaque, sealed envelopes (SNOSE); sequentially numbered containers; pharmacy controlled allocation; and central allocation

43. Compliance bias David Sackett, 1979 paper on Biases in Analytic Research Participants who are compliant with an intervention may differ from those who are non-compliant, and in ways that might affect the risk of the outcome being measured. Clinical trials should attempt to collect data on compliance and while analyses should include the intention to treat analysis

44. All Studies Reporting bias Recall bias Observer/interviewer bias Observer expectation bias Misclassification bias Competing risk Apprehension bias Observational Ascertainment bias Centripetal bias Diagnosis/treatment access bias Health care access bias Healthy volunteer bias Non-random sampling bias Protopathic bias Referral filter bias Sick quitter bias Underreporting bias

45. Ecological Confounding Ecological fallacy Temporal ambiguity Cross-sectional Temporal ambiguity Detection bias Neyman bias

46. Case-control Confounding Diagnostic suspicion bias Exclusion bias Recall bias Exposure suspicion bias Friend control bias Overmatching Relative control bias Cohort Confounding Detection bias Diagnostic suspicion bias Healthy worker effect Losses/withdrawal to follow up Rumination bias Survival treatment selection bias

47. Trial Allocation of intervention bias Compliance bias Hawthorne effect Lack of intention to treat analysis Participant expectation bias SRMA Citation bias Dissemination bias Language bias Publication bias

48. References Bias, Miguel Delgado- Rodrı´guez , Javier Llorca , J Epidemiol Community Health 2004;58:635–641. doi : 10.1136/jech.2003.008466 Catalogue of Bias, accessible at: https://catalogofbias.org/ Gordis Epidemiology, Elsevier, 6th edition.