Questions and Answers
What are the levels of the dependent variable for the study involving the anti-nausea drug?
What measure of comparison is most appropriate for the study design concerning mothers' recall of anti-nausea medication use?
What is the formula for the odds ratio?
OR = a × d / (b × c)
Which statements regarding bias and odds ratio are true? (Select two)
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Which type of bias are the investigators likely investigating?
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Selection bias is most likely to occur in which types of studies?
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Identify three ways confounding can be controlled in the design phase of an epidemiologic study.
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Identify three ways confounding can be controlled in the analysis phase of an epidemiologic study.
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Is it better to control for confounding at the design phase or the analytic phase of a study?
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What are the limitations of controlling for confounding in the design phase of an epidemiologic study?
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What are the limitations of controlling for confounding in the analysis phase of an epidemiologic study?
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What is residual confounding?
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Do you suspect socioeconomic status (SES) is a confounder based on examining the crude and the stratified data?
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Is SES a confounder in this analysis?
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What is the magnitude of the confounding?
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Identify the independent variable, dependent variable, and potential confounding factor in the study about exercise habits and heart attacks.
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What is happening with the observed measure of association when positive confounding is present?
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What is happening with the observed measure of association when negative confounding is present?
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Which statement about smoking in relation to heart disease is false?
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What is the difference between a mediator and a confounder?
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Mediators cannot be in the causal pathway between exposure and disease.
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Confounding is the same as random error.
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It may be possible to control for confounding in both the design phase and the analytic phase of research studies.
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Define random error.
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In which types of studies can random error occur?
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What effect(s) does having random error have in epidemiologic studies?
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What are the top two causes of random error in epidemiologic studies?
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Interpret the RR measure from the researcher studying sedentary computer programmers' risk of cardiovascular disease.
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What does the term 'validity' mean?
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What are the three alternative explanations to consider when assessing the validity of a study? Define each.
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Define generalizability.
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Is it possible to have both random error and systematic error in the same study?
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Explain what happens when there is bias in a study that biases results towards the null.
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Explain what happens when there is bias in a study that biases results away from the null.
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If bias occurs in a study, it is possible for us to fix or remove it when we do the analysis.
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Briefly describe when/how selection bias occurs in an epidemiologic study.
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What types of studies is selection bias more likely to occur?
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List specific types of selection bias.
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What can be done to fix selection bias?
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How does selection bias impact measures of association?
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Name methods to avoid selection bias.
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Define information bias.
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What are three types of information bias?
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What is recall bias?
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What is interviewer bias?
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What is measurement/misclassification error?
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List some ways to prevent/avoid interviewer bias.
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What is differential misclassification?
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What is non-differential misclassification?
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What can non-differential misclassification do to the true value for a measure of association?
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What can differential misclassification do to a measure of association?
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When does information bias occur?
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List the three alternative explanations to consider for internal validity.
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Methods to avoid information bias?
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What does selection bias have to do with?
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When does selection bias occur?
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What can selection bias lead to?
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Bias can occur if controls are more (or less) likely to be selected based on their exposure status.
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Differential participation is a type of selection bias.
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Bias occurs when an investigator chooses to analyze only a subset of participants in a study.
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Only once a study is deemed as having internal validity is it appropriate to consider generalizability.
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Having participants refuse to participate in a study can lead to self-selection bias.
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Bias can be introduced at any stage of a study.
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Loss to follow up results in selection bias in a cohort study when the loss is associated with both the exposure and outcome.
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If bias occurs in a study, it is possible to fix/correct with special analytic methods.
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Define confounding.
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What are the three criteria for a variable to be considered a confounder?
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What might be considered a confounder in epidemiological research?
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What is positive confounding doing regarding the true value for a measure of association?
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What is negative confounding doing regarding the true value for a measure of association?
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What could be the relationship between modest alcohol consumption and coronary heart disease according to a directed acyclic graph?
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According to the directed acyclic graph, what is the exposure of interest, health outcome, and potential confounder?
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What is a mediator variable?
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Confounding is one source of systematic error in epidemiological research.
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Confounding can be thought of as a failure of the comparison group to reflect the counterfactual experience of the control group.
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Confounding always biases the resulting measures of association away from the null.
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Mediator variables are confounding variables.
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Confounding variables are considered independent variables.
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Confounding variables result from random error.
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Is it possible to control for confounding in the design phase of research studies?
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Is it possible to control for confounding in the analysis phase of research studies?
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The crude measure of association is the measure obtained from study data.
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The true RR=2.5, but the confounded RR=3.8. This is an example of negative confounding.
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In a case-control study, what are the levels of the independent variable?
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Study Notes
Validity and Bias
- Validity denotes the accuracy of a study, characterized by the absence of random error, bias, and confounding.
- Three critical alternative explanations to assess study validity:
- Bias: Systematic error that skews the perceived relationship between exposure and disease.
- Confounding: Influence of a third variable that distorts the true association between exposure and disease.
- Random Error: Variability in study results that occurs by chance, lacking a specific cause.
Generalizability and Association
- Generalizability refers to extending study conclusions beyond the specific study population and setting.
- Bias can lead to both random and systematic errors simultaneously.
- When bias drives results toward the null hypothesis, the true association is underestimated; when it moves away, the true association is overestimated relative to the null.
Selection Bias
- Selection bias arises from differences in how study groups are chosen.
- More common in case-control and retrospective cohort studies; also a concern in prospective cohort studies due to differential follow-up.
- Strategies to mitigate selection bias: consistent case/control selection criteria, high participation rates, and effective tracking methods.
- The impact of selection bias can distort association metrics, shifting them either towards or away from the null.
Information Bias
- Information bias occurs from systematic discrepancies in how data on exposure and disease is gathered from participants.
- Types of information bias include:
- Recall Bias: Differences in accuracy of reported information between groups.
- Interviewer Bias: Systematic differences in data collection by interviewers.
- Measurement Error: Nonsystematic errors that affect both exposure and outcome assessment.
- Preventive methods for information bias involve careful study design and data collection methods.
Confounding Variables
- Confounding is the interplay of an exposure, outcome, and a third variable that alters the perceived relationship.
- A valid confounder meets three criteria:
- Associated with the exposure in the population being studied.
- Acts independently as a predictor of the disease.
- Is not part of the causal pathway between exposure and disease.
Misclassification
- Differential Misclassification: Errors are related to a specific axis (exposure or disease) and can bias outcomes in either direction.
- Non-Differential Misclassification: Errors on one axis unrelated to the other, generally biasing results towards the null hypothesis.
- Preventing misclassification focuses on enhancing data collection accuracy through various measurement techniques.
Case-Control Study Framework
- In case-control studies, determining how participants recall past exposure can affect odds ratio calculations.
- Bias introduced through inaccurate reporting can shift odds ratios toward the null value due to non-differential misclassification.
- Understanding how exposure and outcome relate in these studies is critical for evaluating true associations.
Statistical Concepts and Directions
- Adjusting for confounding can be achieved during both study design and analysis phases.
- The "crude measure of association" represents initial estimates without adjustment for confounding factors.
- Direction of bias can vary significantly depending on whether confounding leads to overestimation or underestimation of the true association.
Key Takeaways
- Recognizing and addressing different biases is essential to maintain the integrity of epidemiological research.
- Effective study design, critical evaluation of variables, and comprehensive data collection methodologies are key to minimizing errors.
- Continuous vigilance for biases at all study stages can enhance the reliability and applicability of research findings.### Bias in Epidemiological Studies
- The magnitude of the Odds Ratio (OR) is often less than the true value due to bias.
- Investigators commonly investigate types of bias such as recall bias and interviewer bias, both of which are forms of information bias.
- Selection bias is prevalent in case-control studies and retrospective cohort studies, leading to incorrect associations.
Confounding Control
- Confounding can be managed during the design phase using:
- Randomization
- Restriction
- Matching
- In the analysis phase, confounding can be controlled through:
- Standardization
- Stratified analysis
- Multivariate analysis
- Controlling confounding during the design phase is generally more effective than during the analysis phase.
Limitations in Confounding Control
- Matching is challenging due to its dependence on known confounders; may incur high costs and time.
- Restriction limits generalizability and sample size and may not fully control for confounders.
- Randomization is primarily applicable to experimental studies and is less effective with smaller samples.
- In the analysis phase, stratified analysis is complex with multiple confounders, and multivariate analysis requires stringent model assumptions.
Residual Confounding
- Residual confounding refers to unaddressed confounding variables that remain even after some confounding has been controlled.
Confounding Example
- If socioeconomic status (SES) shows different Relative Risks (RR) across stratifications, and an SES-adjusted RR differs significantly, SES is identified as a confounder.
Observed vs True Association
- Positive confounding results in the observed association exaggerating the true association, moving farther from the null value.
- Negative confounding causes the observed association to mask the true association, pulling it toward the null value.
Mediators vs Confounders
- A mediator is part of the causal pathway between an exposure and disease, while a confounder is not.
- It is possible to control for confounding in both design and analytic phases of research.
Random Error
- Random error encompasses unsystematic errors due to chance and can occur in all study types.
- It can create false associations or obscure true associations.
- Causes include measurement error and sampling variability, both contributing to random error in epidemiological studies.
Hypothesis Testing Outcomes
- Null hypothesis (H0) may be true and either not rejected (correct conclusion) or mistakenly rejected (Type I error).
- Alternative hypothesis (HA) may be true and either not confirmed (Type II error) or correctly supported (correct conclusion).
Statistical Practice
- The p-value indicates the probability of observing results due to chance if the null hypothesis is true.
- A confidence interval quantifies random error, indicating variability around a measure and providing a range in which the true effect lies.
- Wider confidence intervals signify greater random error and reduced precision, while narrower intervals indicate less random error and more precision.
Risk Interpretation
- A calculated Relative Risk (RR) of 4.3 indicates that sedentary computer programmers have 4.3 times higher risk of cardiovascular disease compared to non-computer programmers, based on a p-value of 0.02.
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Test your knowledge on validity and alternative explanations in epidemiology with this set of flashcards. Perfect for review before Exam 3. Each card presents key concepts clearly and concisely.