Bias and confounding
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Bias can systematically skew study results and lead to incorrect estimates of associations between exposures and health outcomes. There are two main types of bias: selection bias, which occurs when the study groups are selected in a non-random way; and information bias, which arises when exposure or disease data is incorrectly gathered or recorded. Confounding is a type of systematic error caused by a third variable, called a confounder, that distorts the exposure-outcome association.
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Page 1
BIAS IN EPIDE MIOLO GY
THE MEANING AND CONTEXT OF BIAS
We briefly touched upon bias earlier in the course. The word bias probably has an intuitive meaning to you,
and recall the definition from Module 3:
“Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the
collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are
systematically different from the truth.” (J.M. Last, A Dictionary of Epidemiology, 4th ed.)
Bias is a systematic error that results in an incorrect (invalid) estimate of the measure of association. Bias can
therefore create the appearance of an association when there really is none (bias away from the null), or mask
an association when there really is one (bias towards the null). Bias can arise in all study types: experimental,
cohort, and case-control designs. It is primarily introduced by the investigator or study participants in the
design and conduction of a study, and once it occurs, cannot be removed – but it can be evaluated during the
analysis phase of a study. Bias is a direct threat to the validity of any epidemiologic study, and influences
whether we can believe the observed association is a true association.
DIRECTION OF BIAS
We can describe the impact of bias on the measure of association in a few different ways. Remember, the null
value is 1.0; the value that means there is no observed association between exposure and disease. If the true
association is protective (less than 1.0), bias towards the null will make it seem less protective as the measure
of association gets closer to the null value.
• Positive bias: the observed value is higher than the true value
• Negative bias: the observed value is lower than the true value
• Bias towards the null: the observed value is closer to 1.0 than the true value.
• Bias away from the null: the observed value is farther away from 1.0 than the true value.
Bias towards null
Bias away from null
Bias away from null
Bias towards null
Protective effects Harmful effects
1.0
(no association)
Bias towards null
Bias away from null
Bias away from null
Bias towards null
Protective effects Harmful effects
1.0
(no association)
Excelsior College PBH 321
Page 2
TYPES OF BIAS
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This document provides an overview of concepts related to causation in epidemiology. It discusses the difference between association and causation, outlines various types of causal relationships, and describes criteria for evaluating evidence of a causal relationship. Specifically, it explains that determining causation is a two-step process involving first establishing an association and then evaluating it against criteria like strength of association, consistency, and biological plausibility to determine if the relationship is likely causal.
Association & causation.pptx
The document discusses association versus causation in epidemiology. It defines association as a statistical relationship between two variables, such as an exposure and disease rate. Association does not necessarily imply causation. The document outlines different types of associations, including spurious, indirect, and direct associations. It also discusses criteria for determining causation, including those proposed by Hill, such as strength of association, consistency of findings, and biological plausibility. The overall goal is to help distinguish true causal relationships from associative relationships.
Bias and confounding
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of exposure-outcome associations. Selection bias and information bias are two common types of bias. Confounding occurs when another exposure is associated with both the disease and exposure being studied, independently of the exposure-disease relationship. Methods to control for confounding include restriction, matching, randomization, stratification, and multivariate analysis at the design and analysis stages of a study.
Case control & cohort study
This document provides information on epidemiological study designs, including analytical studies, case-control studies, and cohort studies. It defines epidemiology as the study of health-related states in populations. Case-control studies look backward from the outcome to exposures, comparing cases to controls. Cohort studies follow groups over time to examine exposure-outcome relationships. The key difference is that cohort studies measure incidence while case-control studies measure odds ratios. Selection of appropriate study populations and controls is important to minimize biases.
BIAS AND CHANCE AND CONFOUNDING
Selection bias occurs when the comparison groups in a study are assembled in a way that reduces their comparability, impacting the exposure-outcome relationship. Information/misclassification bias arises when there are errors in how exposures or outcomes are measured, which may differentially affect cases and controls. Confounding bias is caused by a third factor that is associated with both the exposure and outcome, distorting the exposure-outcome relationship. Proper study design and analysis techniques like matching, restriction, stratification and adjustment can help control for these biases.
Bias, confounding and causality in p'coepidemiological research
A brief description of three issues (Bias, Confounding and Causality) commonly encountered while performing pharmacoepidemiological research. A big THANK YOU to Mr. Strom and Mr. Kimmel.
Basics of epidemiology
This document discusses epidemiology and how it was used to identify smoking as a cause of lung cancer. It shows that lung cancer rates increased dramatically between 1937-1950 in the US. A case-control study found that smokers were over 20 times more likely to develop lung cancer than non-smokers. A later British study found that lung cancer risk increased with the number of cigarettes smoked per day. Through observational epidemiological studies, researchers were able to establish smoking as a major risk factor and cause of lung cancer.
Bias in health research
Selection bias, information bias, and confounding are the three main types of bias that can occur in epidemiological studies. Selection bias results from the inappropriate selection of study participants and can be reduced through randomization and clearly defining eligibility criteria. Information bias occurs due to errors in measuring or classifying exposure, disease status, or other variables and can be reduced through blinding of outcome assessors. Confounding happens when a variable is associated with both the exposure and the outcome but is not on the causal pathway, distorting the exposure-outcome relationship.
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Bias and confounding
- 1. Bias and confounder Presented by : Erum Ali Akbar
- 2. What is bias? Bias is any systematic error in an epidemiologic study that results in an incorrect estimate of the association between exposure and the health outcome. … The consequence of bias is systematic error in the risk ratio, rate ratio, or odds ratio estimate.
- 3. Types of Error :
- 4. Source of resources in epidemiology :
- 5. Types of bias : There are two types of bias: 1: Selection bias 2: Information bias
- 6. What is selection bias? When a systematic error is made inselecting one or more of the study group that will be compared .
- 7. Example of selection bias: In a Control study of smoking and Chronic lung disease, the association of Exposure with disease will tend to be weaker If controls are selected from a hospital Population (because smoking causes many Diseases resulting in hospitalization) than if Controls are selected from the community.
- 8. Information bias : information gathered regarding exposure and/ or disease outcome is incorrect.
- 9. Example of information bias: Missing data can be a major cause of information bias, where certain groups of people are more likely to have missing data. An example where differential recording may occur is in smoking data within medical records. … The bias was more likely when the exposure is dichotomized.
- 10. What is confounding? Confounding is one type of systematic error that can occur in epidemiologic studies. … Confounding is the distortion of the association between an exposure and health outcome by an extraneous, third variable called a confounder