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Bias in health research

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Classification and common biases encountered in health research

Published in: Health & Medicine
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Bias in health research

  1. 1. Dr. R. VENKITACHALAM
  2. 2. Contents  Introduction  Classification of bias  Bias in clinical trials  Bias in qualitative research  Case scenarios  Summary and conclusion  References
  3. 3. Introduction  Bias is a fundamental concept in epidemiology  It is defined as ‘deviation of results or inferences from the truth , or processes leading to such deviation’ - Grimes and Schulz, 2002  It is the result of errors  not random but systematic  results invalid  Results mainly from faulty design
  4. 4.  Scientifically speaking, bias can be explained as: lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population in which the statistic estimated has an expectation that does not equal the true value.  Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004 Aug 1;58(8):635–41.
  5. 5. Etymology  mid 16th century (in the sense ‘oblique line’; also as an adjective meaning ‘oblique’): from French biais, from Provençal, perhaps based on Greek epikarsios ‘oblique’.
  6. 6. Classification of bias  Several classifications of bias exist in literature. Prominent among them are: 1. Sackett (19 types) and Choi (65 types) – based on stages of research 2. Maclure and Schneeweiss – causal diagram theory 3. Kleinbaum et al – three main groups (selection, information and confounding) 4. Steineck and Ahlbom – misclassification, misrepresentation and analysis deviation
  7. 7. Maclure and Schneeweiss episcope of bias
  8. 8. WHO classification  Selection bias  Occurs from the manner in which study population is selected  Most common type of bias in health research  Seen in observational and analytical studies  Ascertainment or information bias  Occurs due to measurement error or misclassification of subjects according to one or more variables
  9. 9. WHO classification Health research methodology - A Guide for training in Research methods. Second edition. WHO Bias Selection bias Prevalence – incidence bias Admission rate bias Non-response bias Information bias Diagnostic bias Recall bias
  10. 10. How to reduce selection bias  The study population should be clearly identified i.e. clear definition of study population.  The choice of the right comparison/ reference group (unexposed or controls) is crucial  In a cohort study:  exposed and unexposed groups should be identical but for the exposure  in a retrospective cohort study, the selection of exposed and unexposed groups should be done without knowing the outcome (disease status).
  11. 11.  In a case-control study:  the control group should reflect the exposure of the population which gave rise to the cases  controls should be selected independently of the exposure status  precise case definition and exposure definition should be used by all investigators.  In a clinical trial:  Randomization and allocation concealment from the investigator
  12. 12. How to reduce information bias  Blinding  Placebo arm in case of RCT’s
  13. 13. 1. SELECTION BIAS  Error introduced when the study population does not include the target population  Causes:  Due to design  Bad definition of eligible population  Lack of accuracy of sampling frame  Uneven diagnostic procedures in target population  Due to implementation
  14. 14. 1.1 Inappropriate definition of eligible population  Occurs when kind of patients gathered does not represent the cases originated in the population  It is of the following types:  1.1.a – Competing risks bias  When two or more outputs are mutually exclusive, any of them competes with each other in the same subject. Eg: death
  15. 15.  1.1.b – Healthcare access bias  When patients admitted to an institution does not represented the cases originated in the community  Popularity bias  Centripetal bias  Referral filter bias  Diagnostic/treatment access bias  1.1.c – Length bias  Cases with disease with longer duration are more easily included in surveys
  16. 16.  1.1.d - Neyman bias  When a series of survivors is selected, if the exposure is related to prognostic factors, or the exposure itself is a prognostic determinant , the sample of cases offers a distorted frequency of exposure.  1.1.e – Spectrum bias  In the assessment of validity of a diagnostic test, this bias is produced when researchers include only clear or definite cases, not representing the whole spectrum of disease. Also applicable for controls
  17. 17.  1.1.f – Survivor treatment selection bias  In observational studies, patients who live longer have more probability to receive a certain treatment  1.1.g – Berkson bias  When probability of hospitalization of cases and controls differ, and it is also influenced by exposure  1.1.h – Healthy worker effect  Lower mortality observed in employed population compared to general population
  18. 18.  1.1.h – Inclusion bias  When one or more conditions of controls are related with exposure. Frequency of exposure higher in control group.  Seen commonly in hospital based studies  1.1.i – Exclusion bias  When controls with conditions related to exposure are excluded where as cases with co-morbidities are included
  19. 19. 1.2 lack of accuracy of sampling frame  1.2.a – Non random sampling bias  Results in non-representative sample  1.2.b – telephone random sampling bias  Excludes some households from the sample . . Coverage issues
  20. 20. In systematic reviews and meta analysis, selection of samples (relevant studies) is most important  1.2.c – Citation bias  More frequently cited, more easily found  1.2.d – Dissemination bias  Biases in retrieval of information (language, reporting of results)  1.2.e – Post-hoc analysis  Due to subgroup analysis which give misleading results  1.2.f – Publication bias  When published reports do not represent the studies carried out on that association
  21. 21. 1.3 uneven diagnostic procedures  In case control studies, if exposure influences diagnosis of disease, detection bias occurs  1.3.a – diagnostic suspicion bias  Exposure is taken as a diagnostic criterion  1.3.b – Mimicry bias  When benign conditions mimic clinically to the disease
  22. 22. 1.4 during study implementation  1.4.a – Loss to follow up  Attrition/withdrawal is uneven in exposure and outcome categories  study results affected  1.4.b - Missing information bias  Seen mostly in multivariate analysis  Missing data affects study outcome  1.4.c – Non-response bias  This type of bias is due to refusals to participate in a study.  The individuals concerned are likely to be different from individuals who do participate.  Non-respondents must be compared with respondents with regard to key exposure and outcome variables in order to ascertain the relative degree of non-response bias.
  23. 23. 2. INFORMATION BIAS  It occurs during data collection.  Three types of information bias are:  Misclassification bias  Ecological fallacy bias  Regression to the mean bias
  24. 24. 2.1 Misclassification bias  When sensitivity and/or specificity of the procedure to detect exposure and/or effect is not perfect, ie exposed/diseased subjects can be classified as non-exposed/non-diseased subjects and vice-versa  Two types:  Differential misclassification bias  Non-differential misclassification bias
  25. 25.  2.1.a – Detection bias  Seen in studies with follow-up (cohort, clinical trials)  2.1.b – Observer/Interviewer bias  Knowledge of hypothesis, disease status, exposure status or intervention received can influence data recording  Interviewers can influence errors into a questionnaire or guide the respondents to a particular answer.
  26. 26.  2.1.c – Recall bias  If presence of disease influences the perception of its causes (rumination bias) or  In a trial, if patients knows what they receive, it may influence their answers (participant expectation bias)  2.1.d – Reporting bias  Participants can collaborate with researchers and give answers in the directions they perceive are of interest (obsequiesness bias)  Existence of a case triggers family information (family aggregation bias)  Measures or sensitive questions that embarrass or hurt can be refused  Reporting of socially undesirable behaviours (underreporting bias)
  27. 27. 2.2 Ecological fallacy  It is produced when analysis realized in an ecological (group level) analysis are used to make inferences at the individual level.  Eg: if exposure and disease are measured at group level, exposure disease relations can be biased from those obtained at the individual level
  28. 28. 2.3 Regression to the mean  It is a phenomenon that a variable that shows an extreme value on its first assessment will tend to be closer to the centre of its distribution on a later measurement.  Eg: high cholesterol level measurement
  29. 29. 2.4 Other information biases  2.4.a – Hawthorne effect  Increase in outcome under study in participants who are aware of being observed.  2.4.b – Lead time bias  The added time of illness produced by the diagnosis of a condition during its latency period.
  30. 30.  2.4.c – protopathic bias  When an exposure is influenced by early (subclinical) stages of disease  Sick quitter bias: People with risky behaviours (eg: alcohol consumption) quit their habit as a consequence of disease . . Which will mention them as non-exposed  2.4.d – temporal ambiguity  When it cannot be established that exposure precedes effect.  Seen in cross-sectional and ecological studies
  31. 31.  2.4.e – Will Rogers phenomenon  Improvement in diagnostic tests refines disease staging in diseases such as cancer  It is seen when survival rates are measured across time and even among centres with different diagnostic capabilities  2.4.f – Work-up bias (verification bias)  In the assessment of validity of a diagnostic test, it is produced when the execution of gold standard is influenced by the results of the assessed test, ie reference test is less frequently performed when the test result is negative
  32. 32. 3. Confounding  It occurs when a variable is a risk factor for an effect among non-exposed persons and is associated with the exposure of interest in the population from which the effect derives, without being affected by the exposure or the disease  Confounding can occur in every epidemiological study  Susceptibility bias is a synonym
  33. 33.  Confounding can be neutralised at the design stage of a research (for example, by matching or randomisation) and/or at the analysis, given that the confounders have been measured properly  3.1 - Confounding by group:  It is produced in an ecological study, when the exposure prevalence of each community (group) is correlated with the disease risk in non-exposed of the same community
  34. 34.  3.2 - Confounding by indication  This is produced when an intervention (treatment) is indicated by a perceived high risk, poor prognosis, or simply some symptoms. Here the confounder is the indication, as it is related to the intervention and is a risk indicator for the disease
  35. 35. 4. Specific biases in trials  4.1 – allocation of intervention bias  Seen in non-randomised trials when sequence of allocation is known in advance or concealment is unclear/inadequate  4.2 – compliance bias  In trials requiring adherence to intervention, the degree of adherence (compliance) influences efficacy assessment of the intervention
  36. 36.  4.3 – Contamination bias  When intervention-like activities find their way into control group  Seen in community based trials due to relationships among members and interference by mass media etc  4.4 – Lack of intention to treat analysis  In RCT’s, analysis should be done keeping participants in the group originally assigned to.  Otherwise, bias results . . .
  37. 37. Publication bias  Scientific journals are most likely to accept studies that have ‘positive findings’ than those with ‘negative findings’.  Creates false impression in the literature and may cause long-term consequences to the scientific community  To overcome this bias, several journals have been launched which publish only negative findings.  Eg: Journal of Pharmaceutical Negative Results, Journal of Negative Results in Biomedicine, Journal of Interesting Negative Results
  38. 38. BIAS in clinical trials
  39. 39. Selection bias Ascertainment bias
  40. 40. Other biases in RCT’s Planning phase During conduct During Reporting During dissemination of results During uptake of results
  41. 41. During planning phase Choice of question bias Regulation bias Wrong design bias
  42. 42. During planning phase Choice of question bias Hidden agenda bias Self-fulfilling prophecy bias Cost and convenience bias Funding availability biasRegulation bias Wrong design bias
  43. 43. During planning phase Choice of question bias Regulation bias IRB/Bureaucracy bias Complicated informed consent Wrong design bias
  44. 44. During planning phase Choice of question bias Hidden agenda bias Self-fulfilling prophecy bias Cost and convenience bias Funding availability bias Regulation bias IRB/Bureaucracy bias Complicated informed consent Wrong design bias
  45. 45. During conduct of trial Population choice bias Intervention choice bias Outcome choice bias
  46. 46. During conduct of trial Population choice bias Gender bias Age bias Pregnancy bias Special circumstances bias Informed consent / literary bias Intervention choice bias Outcome choice bias
  47. 47. During conduct of trial Population choice bias Intervention choice bias Too early bias Too late bias Learning curve bias Complexity bias Outcome choice trial
  48. 48. During conduct of trial Population choice bias Intervention choice bias Outcome choice trial Measurement bias Time term bias
  49. 49. During conduct of trial Population choice bias Gender bias Age bias Pregnancy bias Special intervention bias Informed consent / literary bias Intervention choice bias Too early bias Too late bias Learning curve bias Complexity bias Outcome choice trial Measurement bias Time term bias
  50. 50. During reporting of trial Withdrawal bias Selective reporting bias Fraud bias
  51. 51. During reporting of RCT Withdrawal bias Selective reporting bias Social desirability bias Data dredging bias Interesting data bias Fraud bias
  52. 52. During dissemination of results Publication bias Language bias Country of publication bias Time lag bias
  53. 53. During uptake of RCT Relation to author bias Rivalry bias I owe him bias Personal habit bias Morals and values bias Clinical practice bias Institution bias Territory bias Tradition biasDo something bias Printed word bias Prestigious journal bias Peer review bias
  54. 54. During uptake of RCT Prominent author bias Trial design bias Complimentary medicine bias Flashy title bias Careless reading bias
  55. 55. During uptake of RCT Prominent author bias Esteemed author Esteemed professor Friendship Trial design bias Complimentary medicine bias Flashy title bias Careless reading bias
  56. 56. During uptake of RCT Prominent author bias Trial design bias Favoured design bias Large trial bias Small trial bias Multicentre trial bias Complimentary medicine bias Flashy title bias Careless reading bias
  57. 57. During uptake of RCT Prominent author bias Esteemed author Esteemed professor Friendship Trial design bias Favoured design bias Large trial bias Small trial bias Multicentre trial bias Complimentary medicine bias Flashy title bias I am an epidemiologist bias Careless reading bias
  58. 58. BIAS in qualitative research
  59. 59. BIAS in qualitative research  In qualitative research, bias affects validity and reliability of findings and thus the results  Categories of biases seen in qualitative research are:  Moderator bias  Biased questions  Biased answers  Biased sampling  Biased reporting
  60. 60. Moderator bias  The moderator’s facial expressions, body language, tone, manner of dress, and style of language may introduce bias.  Similarly, the moderator’s age, social status, race, and gender can produce bias.  Some conditions unavoidable, still some can be controlled
  61. 61. Biased questions  Leading question bias  Misunderstood question bias  Unanswerable question bias  Question order bias. Always ask:  general questions before specific questions  unaided before aided questions  positive questions before negative questions  behavior questions before attitude questions
  62. 62. Biased answers  Consistency bias  Dominant respondent bias  Error bias  Hostility bias  Moderator acceptance bias  Mood bias  Overstatement bias  Reference bias  Sensitivity bias  Social acceptance bias  Sponsor bias
  63. 63. Biased sampling  Due to errors in sampling.  Professional respondents should be avoided Biased reporting  Experiences, beliefs, feelings, wishes, attitudes, culture, views, state of mind, reference, error, and personality can bias analysis and reporting.
  64. 64. Case scenarios
  65. 65. Scenario 1  Investigators recruited both cases and controls from a defined catchment area in the general population. This is often difficult to do in the absence of a comprehensive registry. Suppose investigators had recruited all cases of oral cancer from a comprehensive national registry, between August 1, 2013 to July 31, 2014. What bias might be intoduced if controls were obtained from the catchment area?  Selection Bias
  66. 66. Scenario 2  Suppose you are designing a case-control study on association between smoking and oral cancer. Which of the following methods of acquiring cases and controls is most practical to reduce bias?  Scenario 1: Cases from AIMS (tertiary hospital) and controls from xyz dental clinic  Scenario 2: Cases from AIMS and controls from admission office of AIMS with diagnosis of any other cancer other than oral cancer  Scenario 3: Cases from AIMS and controls from Panangad satellite clinic
  67. 67. Scenario 3  What potential bias could have been introduced if you found out that those who interviewed cases took 30 minutes longer on average than those who interviewed controls?  Selection bias  Information bias  Volunteer bias  Loss to follow up bias
  68. 68. Scenario 4  A study reported a significant association between long-term use of smoking and oral cancer compared to no smoking and oral cancer. The duration of exposure varied among both cases and controls. What bias could arise when trying to measure exposures that happened over different time periods?  Misclassification bias  Measurement bias  Recall bias
  69. 69. Scenario 5  What effect would you observe if the interviewers were aware of the disease status of the study subjects?  It would benefit the validity of results since interviewer would understand more precisely of the disease and collect better data for cases  The results would likely not change  It could damage the results by introducing interviewer bias
  70. 70. SUMMARY  Bias is any trend or deviation in truth or data collection, analysis, interpretation and publication which can cause false conclusions  Occurs either intentionally or unintentionally  Due to consequences of bias, it is unethical to conduct and publish a biased research even unintentionally
  71. 71.  Confounding effect cannot be completely avoided.  Every researcher should therefore be aware of all potential sources of bias and undertake all possible actions to reduce and minimize the deviation from the truth.  If deviation is still present, authors should confess it in their articles by declaring the known limitations of their work.
  72. 72. References  1. 8.4 Introduction to sources of bias in clinical trials [Internet]. [cited 2014 Dec 22]. Available from: http://handbook.cochrane.org/chapter_8/8_4_introduction_to_sources_of_bias_i n_clinical_trials.htm  2. 9781405132664_4_003.indd - 9781405132664_4_003.pdf [Internet]. [cited 2014 Dec 22]. Available from: http://www.blackwellpublishing.com/content/BPL_Images/Content_store/Sample_ chapter/9781405132664/9781405132664_4_003.pdf  3. Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004 Aug 1;58(8):635–41.  4. bias.aspx [Internet]. [cited 2014 Dec 22]. Available from: http://www.ashpfoundation.org/mainmenucategories/researchresourcecenter/fost eringyounginvestigators/ajhpresearchfundamentalsseries/bias.aspx  5. Sackett DL. Bias in analytic research. J Chronic Dis. 1979;32(1-2):51–63.
  73. 73.  6. Kopec JA, Esdaile JM. Bias in case-control studies. A review. J Epidemiol Community Health. 1990 Sep;44(3):179–86.  7. FEM - Preventing bias [Internet]. [cited 2014 Dec 22]. Available from: https://wiki.ecdc.europa.eu/fem/w/fem/preventing-bias.aspx  8. Pannucci CJ, Wilkins EG. Identifying and Avoiding Bias in Research. Plast Reconstr Surg. 2010 Aug;126(2):619–25.  9. Qualitative Research Bias - How to Recognize It [Internet]. [cited 2014 Dec 22]. Available from: http://www.focusgrouptips.com/qualitative-research.html  10. Types of bias.pdf [Internet]. [cited 2014 Dec 22]. Available from: http://www.medicalbiostatistics.com/Types%20of%20bias.pdf

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