Systematic error bias
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The document discusses bias in epidemiological studies, highlighting systematic errors that can lead to incorrect associations between exposure and disease outcomes. It covers different types of bias including selection bias and measurement bias, outlining their impacts on study results and strategies to minimize them during research design and analysis. The importance of awareness and reduction of bias throughout various stages of research is emphasized to ensure valid conclusions.
Systematic error bias
- 1. 21 Bias –distortion of thetruth Systematic error- Bias
- 2. 22 Bias in epidemiological studies •Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease. • Can occur in all stages of a study • Not affected by study sample size •Difficult to adjust for afterwards, but can be reduced by adequate study design. •Can never be totally avoided, but we must be aware of it and interpret our results accordingly •To what extent does our outcome measure correspond to the “true value”?
- 3. 24 Consequences of bias We make the wrong conclusion about the relation between exposure and outcome in our study •Conclude there is no association while in reality there is one •Conclude there is an association while in reality there is none •Overestimate the strength of the association •Underestimate the strength of the association
- 4. 25 Bias in all stages of research 1. Research topic 2. Choice of population 3. Participation in a study 4. Inequality in study populations – cases- controls / exposed and non-exposed 5. Assessment of exposure factor/disease 6. Measurement of follow-up 7. Analysis and interpretation of the data 8. Publication 9. Interpretation, judgment and action by readers and listeners
- 5. 26 Two main classifications of bias Selection bias : occurs when comparisons are made between groups of patients that are not comparable in determinants for the outcome other than the one(s) under study. Unequal Groups Measurement / information bias: occurs when the validity of measurement of Exposure and/or Disease is dissimilar among groups of patients not comparing the same exposure / outcome parameters
- 6. 27 Selection bias Systematic error that results from: - the way subjects are selected into a study Consequence: there is a difference between subjects who are selected or participant, and those who are not. Problem: we may make an incorrect conclusion about the association between an exposure and an outcome.
- 7. Selection Bias 1. Are the characteristics of the study population selected for the study different than those who were not selected? 2. If yes, in what way / in which factor do they differ? 3. Is this factor likely to affect the outcome being studied?
- 8. 29 Measurement/ Information bias xposed Systematic error that results from: •the way information is obtained on one or more variables (exposure, disease) that are measured in the study. •Differences in validity of exposure data between cases and controls of outcome data between exposed and une Can occur in all types of studies
- 9. Information bias 1. Is there any possibility that the collection of information has been influenced by : 1. Observer ? 2. Respondent? 3. Instrument? 2. Is there any possibility that the assessment in the different groups has not been done equally similar, appropriate, accurate and complete?
- 10. Why are we prone to bias? Possible pitfalls in observational research • Selectivity - selective nature of perception including of selectivity of choice of topics • Expectations -we only see what we expect to see • Expert seeing - We only can see if we have the skills to see it (e.g.microscope) • The observer effect - Observing is already an intervention in itself – the act of observation can sometimes affect what we observe
- 11. We only see what we expect to see Our ability to learn and to see is profoundly affected by: 1. Information to which we have previously been exposed 2. References and pathways we have built in our brain system 3. Emotional links_ emotionally charged events are remembered better 4. Pleasant emotions are usually remembered better than unpleasant ones
- 12. Selectivity Human mind has to filter information from outside Perception is selective – we take what we feel is important Risk of neglecting /overlooking important factors The moment we label a person or a situation, we put on blinders to all contradictory evidence
- 13. Direction of bias The precise magnitude of bias can never really be quantified, however, the direction of bias can often be determined. •The target parameter can be overestimated (association measure (OR/RR) further away from 1) •The target parameter can be underestimated (association measure towards 1).
- 14. Examples of selection bias (1) Healthy Worker Effect (HWE): The overall mortality experience of an employed population is typically better than that of the general population (in Western countries at least). Use of blood donors as controls is a kind of HWE. Blood donors are self-selected on the basis of better life styles. Non-respondent bias: Non-respondents to a survey often differ from respondents. This difference may be related to the subject of interest Volunteer bias: Those who do participate in a study might be different than those who do not participate. This difference is related to the subject of interest Attrition bias: Number of individuals lost to follow up may be different between exposed/unexposed / intervention/ control
- 15. Non response bias in case control study • Case control study to study the effect of passive smoking on the risk of heart attack • Controls who were current smokers were less likely to participated in the study • Smoking exposure in control group is likely to be an underestimate of the true proportion of smokers in the population • Odds of smoking in control group is lower than ‘true’ odds • Overestimate of the strength of the association
- 16. Selection bias in case-control studies The major problem in case-control studies is choice of controls. How representative are hospitalised trauma patients of the population which gave rise to the cases? Is OR under- or overestimated? Cases liver cirrhosis Controls A trauma ward 80 40 20 60 Heavy alcohol use OR=6 Light/no alcoholuse
- 17. Avoid selection bias – controls should arise from same population as cases Cases liver cirrhosis Controls A trauma ward Controls B non-trauma Heavy alcohol use Light/no alcohol use 80 40 10 20 60 90 OR=6 OR=36 Hospital based case control studies more prone to selection bias than population based case-control studies
- 18. 42 Attrition bias in cohort studies Systematic error that results from: - the way we lose subjects during a study (i.e., this is called loss to follow-up or LTF) Consequence: there is a difference between subjects who are selected or participant, and those who are not at the end of the study for analysis Problem: we may make an incorrect conclusion about the association between an exposure and an outcome may lead to a over/ underestimation of effect
- 19. 43 Attrition bias -during follow up- Problem: Crossover or loss-to-follow up cause bias when related to exposure The exposure & non-exposure groups that were comparable at the start of the study are no longer comparable
- 20. Measurement/ information bias Can be caused by: • The observer (interviewer bias) • The measurement instrument (diagnostic bias) •The way the participant remembers and reports events in that happened the past (recall bias) 45
- 21. Observer bias -Interviewer bias Investigator gathers or interprets information in a different way for cases and controls or for exposed and uneposed Cases of listeriosis Controls Eats soft cheese a b Does not eat soft cheese c d Investigator may probe listeriosis cases about consumption of soft cheese • Overestimation of “a” overestimation of OR
- 22. 47 Diagnostic bias When the exposure under study leads to intensified diagnostic procedures and increased chance of identifying the disease Exposure and non-exposure groups not comparable anymore Example: effect of benign breast disease on breast cancer. Women with benign disease undergo more extensive diagnostic procedure and are more likely to have cancer detected. Bias : overestimation of effect
- 23. Can we trust what we think we remember? Recall bias •The way the participant remembers events in that happened the past
- 24. Recall bias: what and when do we remember? Children with malformation Controls Took tobacco, alcohol, drugs a b Did not take c d Occurs when: Cases remember exposure differently than controls Mothers of Mothers of children with malformations will remember past exposures better than mothers with healthy children. But maybe, they are ashamed and will underreport their tobacco, alcohol and drug use Overestimation or underestimation of “a” overestimation/underestimation of OR
- 25. Recall bias can be minimized by: • Interviewing cases and controls in a standard way • Making use of independent sources for measuring exposure (or at least validate a sample) Minimizing recall bias
- 26. 54 Measurement/ information bias Can lead to misclassification bias Study subjects are classified in the wrong category (disease /not diseased; exposed / non-exposed) Non-differential misclassification • Misclassification is equally divided among comparison groups • Generally dilutes the exposure effect (toward to null effect) Differential misclassification • It is worse when the proportions of subjects misclassified differ between the study groups • Such a differential between groups may mask an association or cause one when there is none. • Effect is unpredictable
- 27. Exercise: alcohol consumption and colon rectal cancer You have conducted a case control study to examine the association between high alcohol consumption and colon rectal cancer. In pairs: List and discuss types of biases that may occur when conducting a case control study to study this particular question.
- 28. Exercise: Potential bias Prone to selection bias: Selection of controls can introduce bias explain • If controls do not represent the population from which the cases come from Prone to information bias: Recall bias explain • non-differential recall bias – difficult to remember for both cases and controls how much alcohol one has consumed in past • differential recall bias – cases may remember or report their alcohol consumption more or less accurate than controls
- 29. Exercise: diabetes and stroke You have conducted a cohort study looking at the association between adult onset diabetes and risk of death from stroke, during 10 years of follow-up. In pairs: List and discuss types of biases that may occur when conducting a cohort study to study this particular question. Titel
- 30. Exercise: potential biases Prone to: Information bias: outcome assessment Explain • If the person determining stroke/no stroke had prior knowledge of diabetic history they may be more or less likely to classify the outcome as stroke Prone to: Selection bias: attrition bias Explain • If in a prospective cohort study loss to follow up was different between those with and without diabetes the incidence rates of death from stroke is difficult to interpret Titel
- 31. Study type and Bias A study that suffers from bias lacks internal validity. In case-control studies, recall bias (knowledge of disease status influences the determination of exposure status) and selection bias (knowledge of exposure status influences the identification of diseased and non-diseased study subjects) are most important. In cohort studies, bias due to loss to follow-up (attrition) would be the greatest danger. The potential for misclassification is present in all types of epidemiologic studies and in all stages of the study
- 32. Minimising selection bias In case control studies: • Clear definition of study population • Explicit case and control definitions • Cases and controls from same population In cohort studies: • Selection of exposed and non-exposed without knowing disease status (retrospective cohort) • Tracking procedures for loss to follow-up
- 33. Minimising information bias Interviewer/observer bias • Blinding of observer/interview for exposure/disease • Development of a protocol for collection, measurement and interpretation of information • Standardize questionnaires, calibrated diagnostic tools • Using trained and experienced interviewer Attrition bias • Minimise lost to follow up Recall bias • Blind participants to study hypothesis • Objectively collect exposure data (e.g. work / medical records) Titel
- 34. Bias in randomised controlled trials Gold-standard: randomised, placebo-controlled, double- blinded study Least biased when: • Exposure randomly allocated to subjects - minimises selection bias • Masking of exposure status in subjects and study staff - minimises information bias Selectionbias Performancebias Attritionbias Detectionbias
- 35. Reference population Informed consent sought Assignment by randomization Participants Non-participants Follow for outcome Outcome known Outcome unknown Intervention YES Intervention NO Follow for outcome Outcome known Outcome unknown Study population
- 36. Remember the consequences of bias We make the wrong conclusion about the relation between exposure and outcome in our study • Conclude there is no association while in reality there is one • Conclude there is an association while in reality there is none • Overestimate the strength of the association • Underestimate the strength of the association
- 37. How to deal with bias – think ahead 1. Design stage - minimize or avoid bias. • Avoid selection bias by including/excluding eligible subjects, by • Choice of source population • Choice of the comparison group 2. Analysis stage - determine presence or direction of possible bias and also account for confounding in analysis. 3. Publication stage - Potential biases typically described in "Discussion" section. Provide judgment and possible consequences of bias on results