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  1. Biases Presenter: Dr Himani Moderator: Dr P.R.Deshmukh

  2. Framework • Introduction • Terms to understand • Types of bias • Selection bias and types of selection bias • Information bias and types of information bias • How to control bias • Biases specific for case control study • Biases specific for cohort study • Biases specific for Clinical trial • Biases specific for screening programmes • Confounding

  3. Introduction • Quality of clinical study depends on internal & external factors. • Studies have internal validity when reported differences between exposed & unexposed individuals can only be attributed to exposure under investigation.

  4. tr infer infer Truth in universe Truth in the study Findings in study Error Research question Errors Study plan Actual study Error Errors Design implement Intended Sample (200 pts in SFGH clinic in July 1988) Target population All iv drug absuers in san Francisco Actual subjects 100 pts who get studied Phenomenon of interest (proportion infected by AIDS virus) Actual measurements Proportion with +ve response to ELISA test Intended variables (proportion with Abs to AIDS virus) External validity Internal validity

  5. Sources of Error Random Error Systematic Error

  6. Random Error • Random error is when a value of the sample measurement diverges- due to chance alone- from that of true population value Sources of Random error • Individual biological variation • Sampling Error • Measurement Error

  7. Systematic Error/Bias • Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth. Sources of Error • Basic measurement technique is wrong • Variations between observers or subjects • Systematically differentiating between 2 groups: • being compared at the point of selection or • making measurements

  8. Types of bias • Selection bias • Information bias/Measurement bias • Bias due to confounding

  9. Examples of Random Error, Bias, and confounding in the same study Study: In a cohort study the babies of women who bottle feed and women who breastfeed are compared: Observation: • Incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast feed

  10. Example of Random Error • By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample, producing a type 1 error. (When in truth breast feeding is not protective against gastroenteritis). • Or, also by chance, no difference in risk was found, producing a type 2 error (When in truth breast feeding is protective against gastroenteritis).

  11. Example of Bias • The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only.

  12. Example of confounding • The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowdingand perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding.

  13. Selection bias • Selection bias is a systematic error resulting from the way the subjects are either selected in a study or else are selectively lost to follow up. • Selection bias can cause an overestimate or underestimate of association.

  14. Study of asbestos exposure and lung cancer, the exposure is distributed among cases and controls in target population: True OR in target population = (100*400)/ (100*200) =2

  15. If the selection probabilities for all the cells in the table are equal at 90%, the 2*2 table of selection probabilities would be: OR= (90*360)/ (90*180) =2

  16. If selection probabilities are unequal, and non proportional, then selection bias will occur OR= (90*280)/ (90*180) = 1.6

  17. Self-Selection Bias • Common source of selection bias • Volunteers induced bias • Individuals who volunteer for study possess different characteristics than average general population • Example: A case control study explored an association of family history of heart disease and presence of heart disease in subjects. Volunteers were recruited. Subjects with heart disease may be more likely to participate if they have family history

  18. Self selection bias True OR= 2.25 OR= 3.0

  19. Berksons’ Bias • Hospital selective bias • Patients with two concurrent diseases or health problems are more likely to be admitted to hospital than those with single condition • Example: people who have peptic ulcer and also who smoke are more likely to be admitted to hospital than people who have either of them. A Case-Control study trying to evaluate relationship between smoking and peptic ulcer may find a stronger association between 2 .

  20. Incidence – prevalence bias • Survivorship bias, Neyman’s Bias • Estimate the risk of disease on basis of data collected at a given point in a series of survivors rather than on data gathered during a certain time period in a group of incident cases • Case-control and crossectional study • Example: case control study to assess the protective effect of physical exercise on MI

  21. Healthy Worker Effect • Form of selection bias • General population is often used in occupational studies of mortality, since data is readily available, and they are mostly unexposed • Example: A comparison between health status of military and civilian population may show a better health status of soldiers because during initial medical examination during which unfit persons are excluded

  22. Bias due to loss to follow up Differential loss to follow up in a prospective cohort study on oral contraceptives and thromboembolism RR= 2 (truth) After 40% loss to follow up RR= 1 (biased)

  23. Information bias/ measurement bias • inadequate means for obtaining information about subjects in the study are inadequate. • TYPES: • Non differential missclassification bias • Differential missclassification bias

  24. Nondifferentialmisscclassification bias • When errors in exposure or outcome status occur with approximately equal frequency in groups being compared • Equally inaccurate memory of exposures in both groups. Example:Case-control study of heart disease and past activity • Recording and coding errors in records and databases. Example:ICD9 code used in discharge summaries • Using surrogate measures of exposure. • Non-specific or broad definitions of exposure or outcome. Example: “do you smoke?” vs (how much, how often, how long) to define exposure to tobacco

  25. Example : A Case- Control study comparing CAD cases & controls for history of diabetes. True relationship OR= (40*90)/(10*60) = 6 With non-differential Misclassification (only half of the diabetics are correctly recorded as such in case and controls) OR= (20*95)/ (5*80)= 4.75

  26. Effect: with a dichotomous exposure( eg smoking vs non-smoking), non-differential misclassification minimizes differences & causes an underestimate of effect, i.e. “bias toward null”

  27. Differential misclassification • When errors in classification of exposure or outcome are more frequent in one group • Differences in accurately remembering exposures (unequal). Example: mothers of children with birth defects will remember drugs taken during pregnancy • Interviewer or recorder bias. Example:interviewerhas subconscious better about hypothesis • More accurate information in one of the groups. Example:case-control study with cases from one facility and controls from another with differences in record keeping

  28. Recall Bias • People with disease may remember exposures differently (more or less accurately) than those without disease To minimize: • Use a control group that has a different disease • Use questionnaires that are constructed to maximize accuracy and completeness • For socially sensitive questions, such as alcohol and drug use, use self-administered questionnaire instead of an interviewer • If possible, assess past exposures from pre-existing records

  29. Interviewer bias • Systematic differences in soliciting, recording, or interpreting information Minimized by • Blinding the interviewers if possible • Using standarized questionnaires consisting of closed ended, eay to understand questions • Training all interviewer to adhere to the question and answer format strictly • Obtaining data or verifying data by examining pre existing records (eg medical records or employment records)

  30. Biases in Case- Control study • Selection bias • Information bias • Bias due to confounding

  31. Biases in cohort study • Selection bias • Follow up bias • Information bias • Bias due to confounding • Post hoc bias

  32. Biases in clinical Trial • Selection Bias • Ascertainment bias • Consent bias • Dilution bias • Attrition bias • Analytical bias • Publication bias • Choice of question bias • Choice of population bias • Technical bias • Chance bias

  33. Ascertainment Bias • This occurs when the person reporting the outcome can be biased. • Example, of homeopathy study of histamine, showed an effect when researchers were not blind to the allocation but no effect when they were. • Multiple sclerosis treatment appeared to be effective when clinicians unblinded but ineffective when blinded

  34. Consent bias • This occurs when consent to take part in the trial occurs AFTER randomisation. • Most frequent danger in Cluster trials

  35. Dilution bias • This occurs when the intervention or control group get the opposite treatment. This affects all trials where there is non-adherence to the intervention. • For example, in a trial of calcium and vitamin D about 4% of the controls are getting the treatment and 35% of the intervention group stop taking their treatment. This will ‘dilute’ any apparent treatment effect.

  36. Attrition Bias • Usually most trials lose participants after randomisation. This can cause bias, particularly if attrition differs between groups. • If a treatment has side-effects this may make drop outs higher among the less well participants, which can make a treatment appear to be effective when it is not • We can avoid some of the problems with attrition bias by using Intention to Treat Analysis, where we keep as many of the patients in the study as possible even if they are no long ‘on treatment

  37. Biases in screening programmes • Volunteer bias • Lead time bias • Length time bias • Overdiagnosis bias

  38. Lead time bias Natural history of disease in hypothetical patient with colon cancer

  39. Length time bias • Form of selection bias • Length time bias can occur when lengths of intervals are analysed by selecting intervals that occupy randomly chosen points in time or space • Example: fast growing tumor has shorter incubation period than slow growing tumor

  40. Overdiagnosis Bias • Persons who initiate screening program have almost unlimited enthusiasm for the program. Even cytologists reading pap smears may become so enthusiastic that they may tend to overread the smears (false positive readings). Consequently the abnormal group will be diluted with women who are free of disease. If normal individuals in the screened group are more likely to be diagnosed as positive than are normal individuals in the unscreened group (eg identified as having cancer when in reality they do not), one could get a false impression of increased rates of detection and diagnosis of early-stage disease as a result of screening.

  41. How to control bias Selection bias • Sampling the cases and controls in the same way • Matching • Randomization • Using a population based sample

  42. Control of measurement bias • Development of explicit, objective criteria for measuring environmental characteristics and health outcomes • Careful consistent data collection- for example, through use of standardized instruments; objectives, closed ended questionnaires; valid instruments • Careful consistent use of data instruments- for example, through use of standardized training and instruction manuals, blinding to the extent possible • Development and application of quality control/ quality assurance procedures • Use of multiple sources of data • Data cleaning and coding • Analysis and adjustment, if necessary, to take account of measurement bias

  43. Confounding • Mixing or blurring of effects • Researcher attempts to relate exposure to outcome but actually measures effect of 3rd factor, termed as confounding variable. • A confounding variable is associated with exposure, affects outcome, but not an intermediate link in chain of causation between exposure and outcome

  44. For a variable to be a confounder • It should be a known risk factor for the disease or the outcome • It should be associated with the exposure • It should not be in direct chain or linked between the exposure and outcome • It should be differentially distributed in the two group

  45. Hypothetical case control study to evaluate association between HTN and CHD OR= 1.96 Table ; distribution of cases and control by age 80% of control are younger than 40 yrs of age . 50% of cases are more than 40years.

  46. Relationship of exposure to age <40 yrs, 10%exposed >40yrs, 50%exposed Calculation of OR after stratifying by age

  47. Control of cofounding • During design of epidemiological study: • randomization • Restriction • matching • During analysis of study: • Stratification • Statistical modeling or multivariate analysis

  48. References • David L.Sackett. Biases in analytical research. • Gordis L, Epidemiology, Third Edition(2004), Elsevier Saunders. • Beaglehole R, Bonita R, Kjellstrom T, Basic Epidemiology, 2nd Edition(2006)WHO. • Bhalwar R, Vaidya R, Tilak R, Gupta R, Kunte R, Textbook of Public Health and Community Medicine. 1st Edition (2009). • Hulley SB, Cummings SR. Designing Clinical Research. 1988. Williams & Wilkins. • Web P, Bain C, Pirozzo S. Essential epidemiology- An introduction for students and health professionals. Cambridge University Press2005. • Grimes DA, Schulz KF. Bias and causal association in observational research. Lancet2002; 359:248-52 • Linienfeld, Stolley. Foundations of epidemiology. Third edition.