This document discusses bias, defining it as systematic deviation from the truth. It outlines key types of bias including selection bias, information bias, and confounding. Selection bias can occur through sampling, ascertainment, or participation. Information bias arises from inaccurate or differential measurement of exposures or outcomes. Berksonian bias specifically refers to bias that occurs when using hospital cases and controls due to different admission rates between diseases. The document emphasizes that bias should be avoided in study design to obtain accurate results.
Bias results from systematic errors in study design or data collection. This document discusses selection bias and information bias, including examples. Selection bias occurs when individuals have different probabilities of being included in the study based on characteristics like exposure or outcome. Information bias results from imperfect definition or collection of exposure and outcome data, potentially causing misclassification. Differential misclassification differs between groups and inflates or deflates associations, while non-differential misclassification occurs equally and biases toward the null. Careful study design and data collection can help prevent and control for bias.
This document discusses different types of error and bias that can occur in epidemiological studies. It defines random error as occurring due to chance and resulting in imprecise measures, while systematic error or bias results in invalid measures that are not true. Types of bias discussed include selection bias, information bias, and confounding. Selection bias can arise from how cases and controls are selected, while information bias occurs when exposure or disease status is incorrectly classified. The document emphasizes the importance of reducing both random and systematic errors to obtain valid study results.
This document discusses various types of bias that can occur in epidemiological studies, including selection bias and information bias. Selection bias occurs when the study population is not representative of the target population, such as when there are differential participation rates related to exposure or disease status. Information bias, also called misclassification bias, arises from errors in measuring or recording exposure, disease status, or other variables. There is often no perfect way to measure exposures, and tools to assess past exposures like diet are especially imprecise. The direction and magnitude of bias introduced can impact study results and conclusions.
Excelsior College PBH 321 Page 1 BIAS IN EPIDE.docxgitagrimston
Excelsior College PBH 321
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
Two main types of bias are selection and information (observation) bias. You had an introduction to these
biases in Module 4.
1. Selection Bias
Selection bias is error that occurs if selection of subjects into a study is related to both exposure and disease.
As a result, the measure of association differs from what would have been obtained if we could examine the
entire population targeted for study. Selection bias is most likely to occur in case-control or retrospective
cohort studies because exposure and outcome have occurred at time of study selection, and may influence
the willing ...
Here is an alphabetical list of biases mentioned in the document along with their type and which study design they can occur in:
- Attrition bias (selection bias) - Can occur in cohort studies
- Berksonian bias (selection bias) - Can occur in observational studies
- Information bias/Measurement bias - Can occur in all study designs
- Interview bias (information bias) - Can occur in case-control and cohort studies
- Lead time bias (selection bias) - Can occur in screening studies
- Length time bias (selection bias) - Can occur in screening studies
- Non-differential misclassification bias (information bias) - Can occur in all study designs
- Overdiagnosis bias (selection bias
This document discusses various types of bias that can occur in research studies. It defines bias as an unknown or unacknowledged error created during the research process. Some key biases discussed include selection bias, measurement bias, confounding, and publication bias. The document emphasizes the importance of research design features like randomization and blinding to help reduce bias.
COMMON BIASES IN PHARMACOEPIDEMIOLOGICAL RESEARCH.pdfsamthamby79
Major biases in pharmacoepidemiological research include selection bias, information bias, and confounding. Selection bias can occur through referral bias, self-selection bias, prevalence study bias, and protopathic bias. Information bias includes non-differential and differential misclassification. Differential misclassification involves recall bias and detection bias. Confounding involves covariates related to disease development that may incorrectly attribute an effect to drug exposure. Accurately defining exposure timing and avoiding these biases is important for valid pharmacoepidemiological research.
This document discusses various types of bias, confounding, and interaction that can occur in epidemiological studies. It begins by defining bias as a systematic error in a study's design, conduct, or analysis. The document then covers selection bias, which arises when the study population does not represent the target population. It also discusses information bias, which occurs during data collection through flawed measurement of exposures or outcomes. Examples of specific biases are provided such as recall bias, surveillance bias, regression to the mean, and others. The document outlines how biases can influence effect estimates and introduces the concept of confounding.
Bias results from systematic errors in study design or data collection. This document discusses selection bias and information bias, including examples. Selection bias occurs when individuals have different probabilities of being included in the study based on characteristics like exposure or outcome. Information bias results from imperfect definition or collection of exposure and outcome data, potentially causing misclassification. Differential misclassification differs between groups and inflates or deflates associations, while non-differential misclassification occurs equally and biases toward the null. Careful study design and data collection can help prevent and control for bias.
This document discusses different types of error and bias that can occur in epidemiological studies. It defines random error as occurring due to chance and resulting in imprecise measures, while systematic error or bias results in invalid measures that are not true. Types of bias discussed include selection bias, information bias, and confounding. Selection bias can arise from how cases and controls are selected, while information bias occurs when exposure or disease status is incorrectly classified. The document emphasizes the importance of reducing both random and systematic errors to obtain valid study results.
This document discusses various types of bias that can occur in epidemiological studies, including selection bias and information bias. Selection bias occurs when the study population is not representative of the target population, such as when there are differential participation rates related to exposure or disease status. Information bias, also called misclassification bias, arises from errors in measuring or recording exposure, disease status, or other variables. There is often no perfect way to measure exposures, and tools to assess past exposures like diet are especially imprecise. The direction and magnitude of bias introduced can impact study results and conclusions.
Excelsior College PBH 321 Page 1 BIAS IN EPIDE.docxgitagrimston
Excelsior College PBH 321
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
Two main types of bias are selection and information (observation) bias. You had an introduction to these
biases in Module 4.
1. Selection Bias
Selection bias is error that occurs if selection of subjects into a study is related to both exposure and disease.
As a result, the measure of association differs from what would have been obtained if we could examine the
entire population targeted for study. Selection bias is most likely to occur in case-control or retrospective
cohort studies because exposure and outcome have occurred at time of study selection, and may influence
the willing ...
Here is an alphabetical list of biases mentioned in the document along with their type and which study design they can occur in:
- Attrition bias (selection bias) - Can occur in cohort studies
- Berksonian bias (selection bias) - Can occur in observational studies
- Information bias/Measurement bias - Can occur in all study designs
- Interview bias (information bias) - Can occur in case-control and cohort studies
- Lead time bias (selection bias) - Can occur in screening studies
- Length time bias (selection bias) - Can occur in screening studies
- Non-differential misclassification bias (information bias) - Can occur in all study designs
- Overdiagnosis bias (selection bias
This document discusses various types of bias that can occur in research studies. It defines bias as an unknown or unacknowledged error created during the research process. Some key biases discussed include selection bias, measurement bias, confounding, and publication bias. The document emphasizes the importance of research design features like randomization and blinding to help reduce bias.
COMMON BIASES IN PHARMACOEPIDEMIOLOGICAL RESEARCH.pdfsamthamby79
Major biases in pharmacoepidemiological research include selection bias, information bias, and confounding. Selection bias can occur through referral bias, self-selection bias, prevalence study bias, and protopathic bias. Information bias includes non-differential and differential misclassification. Differential misclassification involves recall bias and detection bias. Confounding involves covariates related to disease development that may incorrectly attribute an effect to drug exposure. Accurately defining exposure timing and avoiding these biases is important for valid pharmacoepidemiological research.
This document discusses various types of bias, confounding, and interaction that can occur in epidemiological studies. It begins by defining bias as a systematic error in a study's design, conduct, or analysis. The document then covers selection bias, which arises when the study population does not represent the target population. It also discusses information bias, which occurs during data collection through flawed measurement of exposures or outcomes. Examples of specific biases are provided such as recall bias, surveillance bias, regression to the mean, and others. The document outlines how biases can influence effect estimates and introduces the concept of confounding.
This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.
Validity and bias in epidemiological studyAbhijit Das
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.
This document discusses various types of biases that can occur in epidemiological studies. It defines bias as systematic error that results in an incorrect estimate of the association between exposure and disease. Several types of biases are described, including selection bias, information bias, and confounding. Selection bias occurs when the study population is not representative of the target population and can arise from inappropriate definitions of eligibility, sampling frames, or follow-up. Information bias results from inaccurate or missing data.
Bias, confounding and causality in p'coepidemiological researchsamthamby79
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.
This document discusses various types of errors and biases that can occur in epidemiological studies. It defines error as a phenomenon where a study's results do not reflect the true facts. There are two basic types of error: random error, which occurs by chance and makes observed values differ from true values; and systematic error or bias, which is due to factors in the study design that cause results to depart from the truth. Types of bias discussed include selection bias, information bias, and confounding. Strategies for controlling biases such as randomization, restriction, matching, and statistical modeling are also outlined.
This document discusses sources of bias and error in epidemiological studies. It defines random and systematic errors, and describes the main types of each. Random errors are due to chance and include sampling variability. Systematic or bias errors are due to flaws in study design, implementation or analysis. The key types of bias discussed are selection bias, information bias, and confounding. Selection bias results from non-representative samples. Information bias stems from errors in measuring or recording exposures and outcomes. Confounding occurs when a third variable influences the exposure-outcome relationship. The document also provides examples and ways to reduce biases, such as increasing sample size and using statistical controls.
Etiologic research aims to establish causal relationships between determinants and disease outcomes. There are two main observational study designs for etiologic research: cohort studies and case-control studies. Cohort studies follow groups of individuals over time based on exposure to a determinant and compare disease outcome rates. Case-control studies identify cases of a disease and controls without the disease and compare past exposure to determinants. Both study designs are prone to biases like selection bias, information bias, and confounding, which can distort the true determinant-outcome relationship if not adequately addressed.
1. Types of biases in case control study.pptxSherpaFurtenzi
This document discusses biases that can occur in case-control studies. It describes three main types of biases: selection bias, information bias, and confounding. Selection bias occurs when there are systematic differences between cases and controls, such as differences in characteristics related to exposure or outcome. Information bias results from differences in how exposure or outcome data is collected between study groups. The document also provides examples of specific biases like Berksonian bias, non-response bias, and sampling bias that fall under selection bias. It concludes with recommendations for preventing biases, such as using population-based samples, blinded observers, standardized questionnaires, and collecting exposure data from medical records.
XNN001 Introductory epidemiological concepts - sampling, bias and errorramseyr
1. The document discusses key concepts in epidemiological sampling including different sampling methods such as probability and non-probability sampling.
2. It describes specific sampling techniques like simple random sampling, stratified sampling, cluster sampling, and their advantages and limitations.
3. The document also discusses potential sources of bias and error in epidemiological studies from sampling, data collection and analysis that can influence the validity and reliability of findings.
Error/Bais in Rsearch Methodology and pharmaceutical statisticsakashpharma19
Error/Bais in Rsearch Methodology and Pharmaceutical Statistics .
A biased estimate is
one which, on the average, does not equal the population parameter.
1. Bias and confounding can systematically skew epidemiological study results if not properly addressed. Bias results from errors in study design, data collection, analysis, or reporting. Confounding occurs when another variable is associated with both the exposure and outcome under study.
2. Common types of bias include selection bias, information bias, and surveillance bias. Selection bias results when study groups systematically differ. Information bias stems from misclassification of exposures or outcomes.
3. Confounding can be controlled for through restriction, matching, randomization, and statistical techniques like stratification and multivariate analysis. These methods help equalize the effects of extraneous variables to isolate the exposure-outcome association being studied.
The document discusses different types of epidemiological studies, including descriptive studies like case reports and case series that focus on person, place and time to create hypotheses. Analytical studies like case-control and cohort studies are used to test hypotheses by being either observational or interventional. Randomized controlled trials are the gold standard for comparing new interventions. Observational analytical studies include cross-sectional, cohort and case-control designs, while interventional analytical studies are clinical trials. The appropriate study design depends on the research goals and objectives.
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
This document discusses biases that can occur in epidemiological studies. It defines bias as systematic error that can lead to incorrect effect estimates. Two main types of bias are discussed: selection bias and information bias. Selection bias occurs when there are systematic differences between study groups, such as in how cases and controls are selected. Information bias results from incorrect or inaccurate measurement of exposures or diseases. Specific examples of biases in study designs like case-control and cohort studies are provided. The document emphasizes that biases cannot be adjusted for and should be prevented or accounted for in interpreting study results.
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of the association between exposure and outcome. Selection bias and information bias are two common types of bias. Selection bias occurs when groups being compared differ systematically, while information bias results from misclassification of exposure or disease status. Confounding occurs when another exposure is associated with both the disease and exposure being studied. Methods for handling confounding include restriction, matching, randomization, stratification, and multivariate analysis.
This document discusses various types of bias and confounding that can occur in epidemiological studies, including:
1) Selection bias, information bias, and confounding can all introduce systematic error and invalidate study results. Selection bias occurs when individuals have different probabilities of being included based on exposure or outcome. Information bias is misclassification of exposure or outcome.
2) Specific types of biases discussed include recall bias, interviewer bias, observer bias, non-response bias, inappropriate control selection, incidence-prevalence bias, loss to follow up, migration bias, Berksonian bias, healthy worker effect, exposure-related bias, non-differential misclassification, and differential misclassification.
3) Bias can
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.
This document defines and describes different types of bias that can affect studies, including confounding, selection bias, and information bias. Bias can result in incorrect estimates of associations between exposures and outcomes. Selection bias occurs when subjects have different probabilities of being included based on exposure or outcome. Information bias results from poor measurement of variables. Confounding bias is due to the influence of a third variable, called a confounder, that is associated with both the exposure and outcome. The document provides examples and discusses approaches to control for different types of bias, such as matching, stratification, and adjustment in data analysis.
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
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.
Mercurius is named after the roman god mercurius, the god of trade and science. The planet mercurius is named after the same god. Mercurius is sometimes called hydrargyrum, means ‘watery silver’. Its shine and colour are very similar to silver, but mercury is a fluid at room temperatures. The name quick silver is a translation of hydrargyrum, where the word quick describes its tendency to scatter away in all directions.
The droplets have a tendency to conglomerate to one big mass, but on being shaken they fall apart into countless little droplets again. It is used to ignite explosives, like mercury fulminate, the explosive character is one of its general themes.
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.
Validity and bias in epidemiological studyAbhijit Das
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.
This document discusses various types of biases that can occur in epidemiological studies. It defines bias as systematic error that results in an incorrect estimate of the association between exposure and disease. Several types of biases are described, including selection bias, information bias, and confounding. Selection bias occurs when the study population is not representative of the target population and can arise from inappropriate definitions of eligibility, sampling frames, or follow-up. Information bias results from inaccurate or missing data.
Bias, confounding and causality in p'coepidemiological researchsamthamby79
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.
This document discusses various types of errors and biases that can occur in epidemiological studies. It defines error as a phenomenon where a study's results do not reflect the true facts. There are two basic types of error: random error, which occurs by chance and makes observed values differ from true values; and systematic error or bias, which is due to factors in the study design that cause results to depart from the truth. Types of bias discussed include selection bias, information bias, and confounding. Strategies for controlling biases such as randomization, restriction, matching, and statistical modeling are also outlined.
This document discusses sources of bias and error in epidemiological studies. It defines random and systematic errors, and describes the main types of each. Random errors are due to chance and include sampling variability. Systematic or bias errors are due to flaws in study design, implementation or analysis. The key types of bias discussed are selection bias, information bias, and confounding. Selection bias results from non-representative samples. Information bias stems from errors in measuring or recording exposures and outcomes. Confounding occurs when a third variable influences the exposure-outcome relationship. The document also provides examples and ways to reduce biases, such as increasing sample size and using statistical controls.
Etiologic research aims to establish causal relationships between determinants and disease outcomes. There are two main observational study designs for etiologic research: cohort studies and case-control studies. Cohort studies follow groups of individuals over time based on exposure to a determinant and compare disease outcome rates. Case-control studies identify cases of a disease and controls without the disease and compare past exposure to determinants. Both study designs are prone to biases like selection bias, information bias, and confounding, which can distort the true determinant-outcome relationship if not adequately addressed.
1. Types of biases in case control study.pptxSherpaFurtenzi
This document discusses biases that can occur in case-control studies. It describes three main types of biases: selection bias, information bias, and confounding. Selection bias occurs when there are systematic differences between cases and controls, such as differences in characteristics related to exposure or outcome. Information bias results from differences in how exposure or outcome data is collected between study groups. The document also provides examples of specific biases like Berksonian bias, non-response bias, and sampling bias that fall under selection bias. It concludes with recommendations for preventing biases, such as using population-based samples, blinded observers, standardized questionnaires, and collecting exposure data from medical records.
XNN001 Introductory epidemiological concepts - sampling, bias and errorramseyr
1. The document discusses key concepts in epidemiological sampling including different sampling methods such as probability and non-probability sampling.
2. It describes specific sampling techniques like simple random sampling, stratified sampling, cluster sampling, and their advantages and limitations.
3. The document also discusses potential sources of bias and error in epidemiological studies from sampling, data collection and analysis that can influence the validity and reliability of findings.
Error/Bais in Rsearch Methodology and pharmaceutical statisticsakashpharma19
Error/Bais in Rsearch Methodology and Pharmaceutical Statistics .
A biased estimate is
one which, on the average, does not equal the population parameter.
1. Bias and confounding can systematically skew epidemiological study results if not properly addressed. Bias results from errors in study design, data collection, analysis, or reporting. Confounding occurs when another variable is associated with both the exposure and outcome under study.
2. Common types of bias include selection bias, information bias, and surveillance bias. Selection bias results when study groups systematically differ. Information bias stems from misclassification of exposures or outcomes.
3. Confounding can be controlled for through restriction, matching, randomization, and statistical techniques like stratification and multivariate analysis. These methods help equalize the effects of extraneous variables to isolate the exposure-outcome association being studied.
The document discusses different types of epidemiological studies, including descriptive studies like case reports and case series that focus on person, place and time to create hypotheses. Analytical studies like case-control and cohort studies are used to test hypotheses by being either observational or interventional. Randomized controlled trials are the gold standard for comparing new interventions. Observational analytical studies include cross-sectional, cohort and case-control designs, while interventional analytical studies are clinical trials. The appropriate study design depends on the research goals and objectives.
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
This document discusses biases that can occur in epidemiological studies. It defines bias as systematic error that can lead to incorrect effect estimates. Two main types of bias are discussed: selection bias and information bias. Selection bias occurs when there are systematic differences between study groups, such as in how cases and controls are selected. Information bias results from incorrect or inaccurate measurement of exposures or diseases. Specific examples of biases in study designs like case-control and cohort studies are provided. The document emphasizes that biases cannot be adjusted for and should be prevented or accounted for in interpreting study results.
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of the association between exposure and outcome. Selection bias and information bias are two common types of bias. Selection bias occurs when groups being compared differ systematically, while information bias results from misclassification of exposure or disease status. Confounding occurs when another exposure is associated with both the disease and exposure being studied. Methods for handling confounding include restriction, matching, randomization, stratification, and multivariate analysis.
This document discusses various types of bias and confounding that can occur in epidemiological studies, including:
1) Selection bias, information bias, and confounding can all introduce systematic error and invalidate study results. Selection bias occurs when individuals have different probabilities of being included based on exposure or outcome. Information bias is misclassification of exposure or outcome.
2) Specific types of biases discussed include recall bias, interviewer bias, observer bias, non-response bias, inappropriate control selection, incidence-prevalence bias, loss to follow up, migration bias, Berksonian bias, healthy worker effect, exposure-related bias, non-differential misclassification, and differential misclassification.
3) Bias can
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.
This document defines and describes different types of bias that can affect studies, including confounding, selection bias, and information bias. Bias can result in incorrect estimates of associations between exposures and outcomes. Selection bias occurs when subjects have different probabilities of being included based on exposure or outcome. Information bias results from poor measurement of variables. Confounding bias is due to the influence of a third variable, called a confounder, that is associated with both the exposure and outcome. The document provides examples and discusses approaches to control for different types of bias, such as matching, stratification, and adjustment in data analysis.
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
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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2. Lecture Learning Outcomes
By the end of this lecture, you should be able to:
Definition of bias
Types of bias
Understanding & Handling bias
Practice & examples of bias
4. Bias
(Definition)
Systematic deviation of results or inferences from truth.
Systematic error (as opposed to a random error) that skews
the observation to one side of the truth.
Processes leading to such deviation.
An error in the conception and design of a study—or in the
collection, analysis, interpretation, reporting, publication, or
review of data—leading to results or conclusions that are
systematically (as opposed to randomly) different from truth.
5. Concept of Bias
Thus, if we use a scale that is not calibrated to zero, the
weights we obtain using this scale will be biased.
Similarly, if a sample is biased (for example, more males in
the sample than the proportion of males in the population, or
selecting cases from a hospital and controls from the general
community in a case-control study), the results tend to be
biased.
Bias are often inevitable
Since it is often difficult to correct for biases once the data
have been collected, it is always advisable to avoid bias when
designing a study.
6. Bias As Deviation From the Truth
Ways in which deviation from the truth can occur include:
Systematic variation of measurements or estimates from the true values.
Variation of statistical estimates (means, rates, measures of
association, etc.) from their true values as a result of statistical artifacts or
flaws in study design, conduct, or analysis.
Deviation of inferences from truth as a result of conceptual or
methodological flaws in study conception or design, data collection, or the
analysis or interpretation of results.
A tendency of procedures to yield results or conclusions that depart from
the truth (in study design, data collection, analysis, interpretation, review,
or publication)
Prejudice leading to the conscious or unconscious selection of research
hypotheses or procedures that depart from the truth in a particular direction
or to one-sidedness in the interpretation of results.
8. Allocation Bias
Allocation bias:
An error in the estimate of an effect caused by failure to
implement valid procedures for random allocation of subjects
to intervention and control groups in a clinical trial or in
another type of randomized study (randomized field trials,
randomized community trials).
9. Ascertainment Bias
Ascertainment bias:
Systematic failure to represent equally all classes of cases or
persons supposed to be represented in sample
This bias may arise because of the nature of the sources from
which persons come (e.g., a specialized clinic);
From a diagnostic process influenced by culture, or
idiosyncrasy; or,
In genetic studies, from the statistical chance of selecting
from large or small families.
10. Attrition Bias
Attrition bias:
A type of selection bias due to systematic differences between
the study groups in the quantitative and qualitative
characteristics of the processes of loss of their members
during study conduct; i.e., due to attrition among subjects in
the study.
Different rates of losses to follow-up in the exposure groups
may change the characteristics of these groups irrespective of
the studied exposure or intervention, or losses may be
influenced by the positive or adverse effects of the exposures.
11. Auxiliary Hypothesis Bias
Auxiliary hypothesis bias:
A form of rescue bias and thus of interpretive bias, which
occurs in introducing ad hoc modifications to imply that an
unanticipated finding would have occurred otherwise had the
experimental conditions been different.
Because experimental conditions can easily be altered in
many ways, adjusting a hypothesis is a versatile tool for
saving a cherished theory.
12. Berksonian & Berkson’s Bias
Berksonian bias:
A general term to indicate all types of bias that have the
structure of selection bias, based on the assumption that
Berkson originally described a bias with that structure.
Berkson’s bias: (Syn: Berkson’s fallacy):
A form of selection bias that arises when the variables whose
association is under study affect selection of subjects into the
study.
It is a particular concern in hospital-based studies, especially
when prevalent or previously diagnosed cases are not
excluded.
13. Berkson’s Bias
Berkson’s bias: (Syn: Berkson’s fallacy):
Joseph Berkson (1899-1982) described an imaginary hospital-
based case-control study wherein the controls are patients
with other diseases, and the “exposure” is also a disease; he
noted that in such a study the association between the
disease prevalences is expected to differ from the
corresponding association in the general population.
This difference in the association has historically been
referred to as Berkson’s bias.
14. Bias Due to Withdrawals
Bias due to withdrawals:
A difference between the true effect and the association
observed in a study due to characteristics of subjects who
choose to withdraw.
15. Bias Due to Instrument Error
Bias due to instrument error:
Systematic error due to faulty calibration, inaccurate
measuring instruments, contaminated reagents, incorrect
dilution or mixing of reagents, etc. See also contamination,
data; information bias; measurement bias.
17. Bias in Epidemiologic Studies
(Types)
Several types of bias exist in research.
Sackett et al. have listed 19 types of bias commonly
encountered in epidemiological studies.
Choi has expanded this list further to 65.
18. Bias in Epidemiologic Studies
(Types)
Selection bias
Information bias
Confounding
19. Bias in Epidemiologic Studies
(Selection Bias)
It is a systematic error resulting from participant
selection procedures or factors influencing
participation.
Occurs in all types of study (observational &
experimental).
Cannot be corrected analytically (must be prevented)
20. Bias in Epidemiologic Studies
(Selection Bias)
Prevalence-incidence bias:
The high case-fatality rate in the early stages of clinically
manifested coronary artery disease may invalidate the study
of possible etiological factors, since the persons available for
study as cases are the survivors (severe cases are absent).
Likewise, myocardial infarction may be silent. Clinical features
may be absent, and the biochemical and electrocardiographic
changes in myocardial infarction may return to normal after an
infarct (these mild cases will not appear among cases for
study). The type of bias introduced into the study may be clear
by contrasting a cohort study (where the disease is identified
in all its forms).
22. Bias in Epidemiologic Studies
(Selection Bias)
Minimising selection bias:
Clear definition of study population
Explicit case and control definitions
Cases and controls from same population
23. Bias in Epidemiologic Studies
(Information Bias)
Systematic error in the measurement of information on
exposure or outcome.
Differences in accuracy:
of exposure data between cases and controls.
of outcome data between different exposure groups.
Study subjects are classified in the wrong category.
24. Types of Information Bias
Interviewer bias
Recall bias
Reporting bias
Publication bias
Follow up bias
25. Types of Information Bias
Interviewer bias:
Investigator asks cases and controls differently about
exposure
Cases of
listeriosis
Controls
b
Eats soft cheese a
Does not eat
soft cheese
c d
Investigator may probe listeriosis cases about consumption of
soft cheese
Overestimation of “a” → Overestimation of OR
26. Types of Information Bias
Recall bias:
Cases remember exposure differently than controls
Mothers of
Children with
malformation
Controls
b
Took tobacco,
alcohol, drugs
a
Did not take c d
Mothers of children with malformations will remember past exposures better
than mothers with healthy children.
Overestimation of “a” → Overestimation of OR
27. Types of Information Bias
Follow up bias:
Unexposed are less likely diagnosed for disease than
exposed
Example:
• Cohort study to investigate risk factors for
mesothelioma.
• Difficult histological diagnosis.
• Histologist more likely to diagnose specimen as
mesothelioma if asbestos exposure known.
28. Berkisonian Bias
A special example of bias after Dr. Joseph Berkson
who recognized this problem.
The bias arises because of the different rates of
admission to hospitals for people with different
diseases (i.e., hospital cases and controls)
29. Berkisonian Bias
Example:
Household interviews were performed on random samples of
the general population asking about musculoskeletal and
respiratory diseases and recent hospitalizations. In the
general population, there appeared to be no association
between these two disorders (OR = 1.06), but in the subset of
the population who had been in hospital during the previous
six months, individuals with musculoskeletal disorders were
more likely to have respiratory disease than not (OR = 4.06).
This occurred because individuals with both disorders were
more likely to be hospitalized than those with only one of the
disorders.
30. Available at: http://www.sph.emory.edu/activepi/Instructors/Kevin_MSword/Lesson_9boh.htm. Accessed
on Oct 18, 2011.
Why misclassification of disease status?
•Incorrect Diagnosis
•Limited knowledge
•Diagnostic process complex
•Inadequate access to technology
•Laboratory error
•Disease subclinical
•Detection bias (e.g. more thorough exam in exposed)
•Subject Self report
•Incorrect recall
•Reluctant to be truthful
•Records incorrectly coded in data-base
32. Differential misclassification:
◦ When the misclassification results in exposure is
incorrectly more in cases than in controls. Or vice
versa; like one group has a lot more incorrect
information than the other group
Non-differential misclassification:
◦ When the misclassification is not related to
exposure status or disease status. And is occurring
at the same proportion in both groups; e.g. if 20%
of cases are classified incorrectly on exposure in
cases and about 20% in controls too
33. An obstetrician wanted to study the
association between congenital
malformations and history of infections
during pregnancy.
He interviewed women (a group who
delivered children with malformations, and a
group of women with normal children). He
asked about history of all types of infections
during pregnancy.
34. After finishing the interviews, he also wanted to
go through the women's’ medical records, in
order to minimize recall bias.
He discovered that women who had a baby with
malformation tended to remember all infections
during pregnancy more than the mothers with
normal babies.
What kind of misclassification is this?
35.
36. Bias in Prospective Cohort Studies
Loss to follow up:
The major source of bias in cohort studies
Assume that all do / do not develop outcome?
Ascertainment and interviewer bias:
Some concern: Knowing exposure may influence how
outcome determined.
Non-response, refusals:
Little concern: Bias arises only if related to both exposure
and outcome.
Recall bias:
It is not a problem: Exposure determined at time of
enrolment.
37. Bias in Retrospective Cohort & Case-control
Studies
Ascertainment bias, participation bias, interviewer bias:
Exposure and disease have already occurred →
differential selection / interviewing of compared groups
possible.
Recall bias:
Cases (or ill) may remember exposures differently than
controls (or healthy)
38. Controlling for Information Bias
- Blinding
prevents investigators and interviewers from
knowing case/control or exposed/non-exposed
status of a given participant
- Form of survey
mail may impose less “white coat tension” than a
phone or face-to-face interview
- Questionnaire
use multiple questions that ask same information
acts as a built in double-check
- Accuracy
multiple checks in medical records
gathering diagnosis data from multiple sources
39. Bias Due to Confounding
Bias due to confounding factors.
Confounding factors: associated with both exposure and out
come
Differs from selection and information bias because it can be
evaluated and controlled to some extent in the analysis phase
of the study.
It can be removed by matching cases and controls.
40. References
Main Textbook:
1.K. Park's (2015): Textbook of Preventive and Social Medicine. Banarsidas Bhanot-Jabalpur. 23rd edition.
Other references:
1.Text Book of Public Health and Community Medicine. RajVir Bhalwar, Department of Community
Medicine, Armed Forces Medical College, Pune, in collaboration with WHO, India Office, New Delhi
(2009).
2.Lucas, A. and Gilles, H. (2003): Short Textbook of Public Health Medicine for the tropic, 4th edition,
Oxford University Press Inc., New York, USA.
3.Portney, L. G. and Watkins, M. P
. (2008): Foundation of Clinical Research. Applications to Practice. 3rd
edition.
4.Kumar, R. (1996): Research methodology. A step by step guide for beginners. 3rd edition.
5.Miller, D. C. (1991): Handbook of Research Design and Social Measurement. 5th edition.
6.Altman, D. G. (1991):Practical statistics for medical research. Boca Ratón, Chapman & Hall/ CRC;
Websites:
1. World Health Organization (WHO): http://www.who.ch
2. Centers for Disease Control and Prevention (CDC), USA: http://www.cdc.gov
3. The Johns Hopkins Bloomberg School of Public Health, OPENCOURSEWARE (OCW) project:
http://ocw.jhsph.edu
4. The WWW Virtual Library (Medicine and Health): Epidemiology
(http://www.epibiostat.ucsf.edu/epidem/epidem.html).