HOSA Epidemiology Flashcards
What is the primary focus of epidemiology?
Study of disease distribution
Which type of study is used to investigate outbreaks of infectious diseases?
Outbreak investigation
What is one of the main goals of the CDC’s disaster epidemiology?
Prevent injuries and deaths
Which agency provides fact sheets on infectious diseases?
WHO
Name a key component of environmental medicine education resources.
Response to environmental hazards
What is the primary objective of a cross-sectional study in epidemiology?
To assess the prevalence of disease
Which organization provides guidelines on measuring health and disease?
World Health Organization
What is the first step in responding to patients exposed to environmental hazards?
Assess the exposure level
Which type of epidemiological study is best for establishing causation?
Randomized controlled trial
What is a crucial element of disaster epidemiology according to the CDC?
Rapid needs assessment
Which resource provides an introduction to basic epidemiology concepts?
PH 101 Series
What is the role of applied research in disaster settings?
To prevent injuries and deaths
Which career focuses on the study of disease spread in populations?
Epidemiologist
What is a common method used in measuring disease frequency?
Incidence rate
What kind of information is provided by WHO Fact Sheets?
Infectious diseases
What is the main focus of environmental medicine?
Response to environmental hazards
How does the CDC utilize surveillance in disaster settings?
To monitor health-related events
What does the term “epidemiology” primarily refer to?
Study of disease distribution
In what scenario would a case-control study be most effective?
Investigating rare diseases
Which component is essential for rapid needs assessment in disasters?
Timely data collection
What is the purpose of public health careers?
Protecting and improving community health
How is the prevalence of a disease calculated?
Total cases/total population
What type of epidemiological study involves following a group over time?
Cohort study
What is a key goal of the CDC’s field investigations?
Identify the source of disease
Which element is crucial for effective consultation and training in disaster epidemiology?
Knowledge transfer
What is the main aim of the WHO’s Basic Epidemiology guide?
Educate about health and disease
How does measuring incidence differ from prevalence?
Incidence refers to new cases
Which study design is used to compare individuals with and without a disease?
Case-control study
What is an essential characteristic of a randomized controlled trial?
Random allocation of participants
How do WHO Fact Sheets assist in public health?
Provide reliable disease information
What is the primary concern of environmental medicine?
Exposure to environmental hazards
What is the goal of surveillance in disaster epidemiology?
Monitor health trends
How can a cohort study be described?
Following a group over time
What is a key aspect of public health careers?
Community health improvement
Which organization is responsible for global health guidelines?
World Health Organization
What is the primary difference between active and passive surveillance systems?
Active requires proactive data collection
Explain the primary objective of an ecological study in epidemiology.
Analyze population-level data
What statistical measure is used to quantify the risk associated with exposure?
Relative Risk
Which type of bias is most likely to occur in a case-control study?
Recall bias
What is the primary disadvantage of using cross-sectional studies for causation?
Temporal ambiguity
How does the Bradford Hill criteria assist in epidemiological studies?
Establish causal relationships
Which factor is crucial for determining the sample size in a cohort study?
Expected incidence rate
What is the significance of a p-value in epidemiological research?
Indicates statistical significance
In disaster epidemiology, what is the role of rapid needs assessment?
Identify urgent health needs
Which type of epidemiological study is best suited for studying rare diseases?
Case-control study
How is confounding controlled in epidemiological studies?
Randomization
What is the primary function of disease registries in epidemiology?
Track disease patterns over time
Describe the difference between incidence density and cumulative incidence.
Incidence density accounts for time
Which method is used to analyze the spread of a disease over time and space?
Spatial-temporal analysis
What is the main challenge in using ecological data for individual-level inferences?
Ecological fallacy
In what way does a randomized controlled trial differ from an observational study?
Manipulation of variables
How does the CDC apply epidemiology in disaster settings?
Conduct field investigations
What is the importance of blinding in a study?
Reduce bias from participants
How are cohort studies advantageous over case-control studies?
Less susceptible to bias
Explain the concept of “dose-response” in epidemiology.
Relationship between exposure and effect
What is a potential drawback of using self-reported data in epidemiological studies?
Susceptibility to recall bias
How does the concept of “herd immunity” relate to epidemiology?
Reduces disease spread in populations
What is the significance of “confidence intervals” in research findings?
Indicate precision of estimates
What is one method used to address confounding in study designs?
Stratification
How does a nested case-control study differ from a traditional case-control study?
Cases and controls come from a cohort
Which concept explains the reduction in disease incidence due to a vaccine?
Herd immunity
What is the primary objective of a systematic review in epidemiology?
Summarize existing research
Describe an advantage of using a meta-analysis.
Combine results for stronger evidence
How can publication bias impact epidemiological research?
Overrepresentation of positive findings
What is a distinguishing feature of a double-blind study?
Both participants and researchers unaware
How does the concept of “attrition bias” affect study results?
Loss of participants affects validity
What is the primary purpose of using a control group in research?
Provide a baseline for comparison
How can researchers mitigate the effects of selection bias?
Randomization of participants
What is a key challenge in the interpretation of longitudinal data?
Attrition over time
How does the concept of “specificity” apply to diagnostic tests?
Ability to identify true negatives
What is a potential issue with using convenience sampling?
Susceptibility to sampling bias
How is a “case-fatality rate” defined in epidemiology?
Proportion of deaths among cases
What is the role of “external validity” in research findings?
Generalizability to other settings
What is a limitation of using historical data in epidemiological studies?
Potential for incomplete or biased data
How does the “Hawthorne effect” influence study outcomes?
Participants alter behavior due to observation
What statistical method can be used to assess the relationship between multiple variables?
Multivariate analysis
What is a key consideration when designing a questionnaire for data collection?
Minimizing bias
How does the “placebo effect” impact clinical trials?
Participants experience changes without active treatment
What is the purpose of “stratified sampling” in research?
Ensures representation of subgroups
How can “information bias” be minimized in epidemiological studies?
Standardized data collection procedures
What is the impact of “lead-time bias” on survival rates in screening programs?
It artificially inflates survival times
How does “Berkson’s bias” affect hospital-based studies?
Overestimates association strength
What is the primary concern when using surrogate endpoints in trials?
They may not accurately reflect actual outcomes
Why is “intention-to-treat analysis” important in clinical trials?
Preserves randomization benefits
How can “recall bias” distort findings in retrospective studies?
Differential accuracy of recalled information
What is a limitation of using “proxy respondents” in data collection?
They may not accurately represent the participant’s experiences
How does “confounding by indication” occur in observational studies?
Treatment choice is influenced by disease severity
What is the purpose of “propensity score matching” in observational studies?
To reduce confounding by equating groups on covariates
How can “publication bias” be minimized in systematic reviews?
Including unpublished studies
What is the significance of a “forest plot” in meta-analysis?
It visually summarizes study estimates
How does “selection bias” affect cohort studies?
It can lead to non-representative samples
What is the impact of “misclassification bias” on study results?
It distorts the true association between exposure and outcome
How can “ecological fallacy” mislead interpretations of data?
It assumes population-level data applies to individuals
Why is “power analysis” crucial in study design?
To determine the necessary sample size for detecting an effect
How does “attrition” affect the validity of longitudinal studies?
Loss of participants over time can bias results
What is a challenge of using “historical controls” in research?
Differences in data collection methods over time
How is “effect modification” identified in epidemiological studies?
By interaction between a third variable and the exposure-outcome relationship
What does “heterogeneity” indicate in meta-analysis findings?
Variability in study outcomes
How can “overmatching” in case-control studies obscure true associations?
By controlling for variables related to both exposure and outcome
What is the role of “sensitivity analysis” in epidemiological research?
To assess the robustness of study conclusions
How does “reverse causation” pose a challenge in observational studies?
The outcome may influence the exposure
What is the significance of a “confidence interval” that includes zero in a study’s findings?
It suggests a lack of statistical significance
How does “loss to follow-up” affect cohort study results?
It can bias the estimated association between exposure and outcome
What is the importance of “inter-rater reliability” in data collection?
Ensures consistency in measurements across different observers
How can “time-varying confounding” complicate longitudinal study analyses?
When confounders change over time and affect exposure and outcome
What is the role of “risk difference” in epidemiological studies?
It measures the absolute change in risk between groups
Why is “blinding” crucial in randomized controlled trials?
To prevent bias from influencing participants and researchers
How does “survivorship bias” affect study conclusions?
By focusing only on those who have survived, skewing results
What is the primary objective of “regression analysis” in epidemiology?
To evaluate the relationship between variables
How can “attrition bias” be minimized in longitudinal studies?
By maintaining participant engagement and follow-up
What is the impact of “information bias” on epidemiological research?
It can lead to inaccurate estimates of exposure or outcome
How does “confounding by lifestyle” occur in observational studies?
When lifestyle factors affect both exposure and outcome
What is the significance of a “P-value” less than 0.05 in research?
It indicates statistical significance
How is “interaction” assessed in epidemiological studies?
Through statistical tests exploring joint effects of variables
What is a potential consequence of “overadjustment” in regression models?
It may obscure true relationships by removing variability
Why is “external validity” a consideration in study findings?
To ensure findings are applicable to other populations and settings
How does “non-differential misclassification” affect study results?
It biases results toward the null hypothesis
What is the purpose of “subgroup analysis” in clinical trials?
To explore differences in treatment effects among specific groups
What is the implication of “null hypothesis” rejection in research?
Evidence suggests a significant effect exists
How can “measurement error” impact epidemiological studies?
It can lead to biased estimates of exposure or outcome
What is the impact of “underpowered studies” in research findings?
They may fail to detect true effects due to small sample sizes
How does “protopathic bias” occur in observational studies?
When treatment is initiated for symptoms of an undiagnosed disease
Why is “internal validity” crucial in epidemiological research?
To ensure the study accurately reflects the true relationship between variables
How does “immortal time bias” affect cohort study results?
Misclassification of exposure time as unexposed
What are the implications of “left-censoring” in survival analysis?
Biases survival estimates by excluding early events
How can “multiple testing” inflate type I error rates in epidemiological studies?
By increasing the likelihood of false positives
What is the primary concern when using “composite endpoints” in clinical trials?
They may obscure individual component effects
How does “regression to the mean” affect study outcomes?
Extreme values are likely to be less extreme on subsequent measurement
How can “competing risks” complicate the analysis of survival data?
They may prevent the occurrence of the event of interest
How does “endogeneity” complicate causal inference in observational studies?
When predictor and error term are correlated
What is the impact of “surveillance bias” on study findings?
Increased detection of outcomes due to increased surveillance
How can “period effects” confound longitudinal study results?
Changes in outcome related to specific time periods
What is the significance of “inverse probability weighting” in observational studies?
Addresses confounding by creating a pseudo-population
How does “latent variable modeling” assist in handling measurement error?
By estimating the unobserved variable through observed indicators
Why is “finite population correction” necessary in sampling?
Corrects for sampling without replacement from a finite population
What is the role of “bootstrapping” in statistical analysis?
Estimates the sampling distribution by resampling with replacement
How does “time-lag bias” affect the interpretation of cumulative meta-analyses?
Delayed publication of studies with negative results
What is the consequence of “collider stratification bias” in causal inference?
Conditioning on a collider introduces bias
How can “non-response bias” skew survey-based study results?
Differences in characteristics between responders and non-responders
How does “truncation” affect the validity of survival analyses?
Excludes individuals from analysis based on their survival time
What is the purpose of “sensitivity analysis” in dealing with unmeasured confounders?
Evaluates how results change under different assumptions
How does “variance inflation factor” (VIF) aid in detecting multicollinearity?
Quantifies how much variance is inflated due to multicollinearity
What is the impact of “digit preference bias” in epidemiological data?
Rounding or reporting errors due to preference for certain numbers
How can “g-methods” like g-formula, g-estimation, and IPW address time-varying confounding?
They estimate causal effects in the presence of time-varying confounders
How does “ecological bias” arise in group-level studies?
Incorrectly attributing group-level associations to individuals
What is the role of “marginal structural models” in epidemiological research?
They adjust for time-dependent confounding in longitudinal studies
How can “partial verification bias” skew diagnostic test evaluations?
When only a subset of individuals receive the gold standard test
What is the effect of “lead-time bias” in evaluating screening programs?
It can make survival appear longer than it actually is
How does “attrition” impact the results of longitudinal cohort studies?
Attrition can lead to biased estimates if not random
What is the consequence of “observer bias” in data collection?
Systematic differences in data collection between groups
How can “cluster sampling” introduce biases in epidemiological studies?
It may result in non-representative samples if clusters are not similar
What is the purpose of “propensity score analysis” in observational studies?
To reduce bias by equating groups based on covariates
How does “incidence-prevalence bias” affect cross-sectional studies?
It may lead to overrepresentation of long-duration cases
How does “non-differential misclassification” affect epidemiological findings?
It tends to bias results toward the null hypothesis
What is the impact of “overdispersion” in Poisson regression models?
It leads to underestimated standard errors and overly narrow confidence intervals
How does “inverse probability weighting” address confounding in observational studies?
By creating a pseudo-population where confounding is balanced
How can “ecological fallacy” mislead interpretations of group-level data?
Assuming population-level associations apply at the individual level
What is “confounding by indication” and how does it affect observational studies?
When treatment is prescribed based on disease severity, causing bias
How does “immortal time bias” influence observational study results?
It can lead to an overestimation of treatment effects
What is the purpose of “instrumental variable analysis” in epidemiology?
To estimate causal effects when randomization is not possible
How does “confounding by indication” occur in observational studies?
When the treatment choice is influenced by the patient’s condition
How is “multicollinearity” detected in regression models?
By examining high variance inflation factors (VIF)
What is the significance of “temporal trends” in longitudinal studies?
They may confound the association between exposure and outcome
How does “detection bias” arise in epidemiological studies?
Differences in outcome detection between study groups
How can “overmatching” in case-control studies obscure true associations?
By controlling for variables related to both exposure and outcome
What is the impact of “survivorship bias” on study conclusions?
Focusing only on those who have survived can skew results
How does “regression to the mean” affect the interpretation of study outcomes?
Extreme values tend to normalize on subsequent measurements
What is the role of “propensity score matching” in reducing bias in observational studies?
It equates groups on observed covariates to reduce bias
How can “non-differential misclassification” affect the results of epidemiological studies?
It tends to bias results toward the null hypothesis
How does “confounding by severity” complicate treatment outcome interpretations?
Treatment decisions based on severity may distort effects
What is the consequence of “secular trends” in epidemiological studies?
They can obscure the true association between exposure and outcome
How can “non-compliance” in a randomized controlled trial affect validity?
It may lead to biased estimates of treatment effect
Why is “propensity score calibration” used in observational studies?
To adjust for residual confounding not captured by measured covariates
What is the impact of “temporal clustering” on disease outbreak investigations?
It may suggest a false association between exposure and disease
How does “differential attrition” affect longitudinal studies?
It can introduce bias if loss to follow-up is related to exposure or outcome
How can “spatial autocorrelation” complicate the analysis of geospatial data?
Nearby locations may have similar values, violating independence assumptions
What is the purpose of using “instrumental variables” in causal inference?
To estimate causal effects when randomization is not feasible
How does “left truncation” affect survival analysis?
It may bias estimates by excluding individuals who experienced the event before a certain time
What is the effect of “measurement error” in exposure assessment?
It can bias the estimated association between exposure and outcome
How can “latent class analysis” aid in identifying unobserved subgroups?
By categorizing individuals into mutually exclusive classes based on response patterns
How does “sample selection bias” occur in epidemiological research?
When the study sample is not representative of the population
What is the purpose of “quantile regression” in statistical analysis?
To estimate the relationship between variables at different points in the distribution
How does “reverse causation” affect interpretations in observational studies?
The outcome may influence the exposure, rather than the reverse
How can “cross-level bias” mislead multilevel modeling results?
By incorrectly attributing effects to the wrong level of analysis
What is the impact of “heteroscedasticity” on regression analysis?
It can lead to inefficient and biased estimates
How does “density sampling” control for time in nested case-control studies?
By matching controls to cases on time of entry into the cohort
What is the effect of “recall bias” in retrospective studies?
Differential accuracy of recalled information can bias results
How can “intermediate variables” complicate causal pathway analysis?
They may mediate the relationship between exposure and outcome, obscuring direct effects
What is the consequence of “model overfitting” in predictive analytics?
The model may perform well on training data but poorly on new data
How does “missing data” impact the validity of epidemiological studies?
It can introduce bias if the missingness is related to exposure or outcome
What is the significance of “Bayesian inference” in epidemiology?
It provides a probabilistic framework for updating beliefs in light of new data
How can “non-differential misclassification” affect epidemiological results?
It tends to bias estimates toward the null hypothesis
What is the role of “geocoding” in spatial epidemiology?
To assign geographic coordinates to data for spatial analysis
How does “immortal person-time” influence cohort study results?
It can lead to overestimation of treatment effects by including unexposed time as exposed
What is the impact of “multiple imputation” in handling missing data?
It reduces bias by creating multiple complete datasets for analysis
How does “endogeneity” complicate causal inference in observational studies?
When the predictor variable is correlated with the error term
What is the consequence of “non-collapsibility” in odds ratios?
Odds ratio can change when adjusting for covariates, even without confounding
How can “specification error” affect regression model accuracy?
Incorrect model assumptions can lead to biased and inconsistent estimates
What is the purpose of “marginal structural models” in epidemiological research?
To estimate causal effects in the presence of time-dependent confounding
How does “non-response bias” influence survey results?
Differences in characteristics between respondents and non-respondents can bias results
What is the effect of “differential misclassification” on study outcomes?
It can lead to biased estimates and incorrect conclusions
How can “intermediate variables” obscure the direct effect in causal pathways?
They mediate the relationship between exposure and outcome, complicating analysis
How does “left censoring” affect the analysis of survival data?
It can bias estimates by excluding subjects who experienced the event before the study began
What is the significance of “latent variable modeling” in dealing with measurement error?
It estimates unobserved variables through observed indicators
How does “survivorship bias” affect the interpretation of cohort study results?
Focusing on surviving individuals can skew results and overlook failures
What is the impact of “model misspecification” in statistical analysis?
It can lead to biased and inconsistent parameter estimates
How can “instrumental variable analysis” address confounding in observational studies?
By using instruments to estimate causal effects when randomization is not possible
What is the purpose of “inverse probability weighting” in longitudinal studies?
To create a pseudo-population where confounding is balanced
How does “differential loss to follow-up” affect longitudinal study validity?
It can bias results if related to both exposure and outcome
What is the significance of “spatial autocorrelation” in geospatial analysis?
It indicates similarity of values at nearby locations, affecting independence assumptions
How does “digit preference bias” impact the accuracy of self-reported data?
Rounding errors due to preferences for certain numbers can skew results
What is the role of “marginal structural models” in dealing with time-dependent confounding?
They estimate causal effects by adjusting for confounders that vary over time
How can “population stratification” confound genetic association studies?
Differences in allele frequencies between subpopulations can mimic genetic associations
How does “confounding by indication” affect observational study interpretations?
It occurs when treatment choice is influenced by prognosis, leading to bias
What is the impact of “non-collapsibility” on interpreting odds ratios?
Odds ratios may change with adjustment, even without confounding
How can “density dependence” in population studies lead to biased conclusions?
Population growth rates depend on population density, influencing resource availability
What is the consequence of “measurement bias” in epidemiological data collection?
Systematic errors can lead to incorrect estimation of exposure or outcome
How do “time-varying covariates” complicate survival analysis?
They require advanced modeling techniques to account for changes over time
What is the purpose of “counterfactual reasoning” in causal inference?
To compare observed outcomes with hypothetical scenarios to estimate causal effects
How does “systematic sampling error” occur in epidemiological research?
It arises from consistent errors in the sampling process, leading to biased estimates
How can “time-dependent confounding” bias causal estimates in longitudinal studies?
Confounders that change over time can distort the exposure-outcome relationship
What is the impact of “missing data mechanisms” on statistical analysis?
Different mechanisms (MCAR, MAR, MNAR) require different handling strategies to avoid bias
How does “multilevel modeling” address hierarchical data structures in epidemiology?
By accounting for data clustering at different levels, reducing bias
What is the role of “propensity scores” in addressing selection bias?
They equate groups on observed covariates to reduce bias in observational studies
How can “spatial heterogeneity” in exposure lead to biased epidemiological estimates?
Variability in exposure across locations can confound associations with outcomes
What is the consequence of “information leakage” in randomized controlled trials?
It can lead to bias if information about group assignments is inadvertently shared
How does “inverse probability weighting” handle time-varying confounding in cohort studies?
By weighting individuals inversely to their probability of receiving treatment, balancing confounders
What is the significance of “latent variable models” in handling complex data structures?
They allow for the estimation of unobserved variables influencing observed data
How can “overadjustment” in statistical models obscure true associations?
By controlling for variables that are intermediates or colliders, distorting the causal path
What is the role of “survival analysis” in handling censored data?
It models time-to-event data accounting for right-censoring and time-varying covariates
How does “attrition bias” affect the validity of randomized controlled trials?
Differential loss to follow-up can bias treatment effect estimates
What is the impact of “exposure misclassification” on study results?
It can lead to biased estimates of the association between exposure and outcome
How can “heteroscedasticity” complicate regression analysis?
Non-constant variance across observations can lead to inefficient estimates
What is the purpose of “stratified randomization” in clinical trials?
To ensure balance of important covariates across treatment groups
How does “immortal time bias” arise in cohort studies?
When unexposed person-time is incorrectly classified as exposed, inflating treatment effect
What is the significance of “inverse probability of treatment weighting” in causality?
It creates a pseudo-population that balances confounders across treatment groups
How can “non-differential misclassification” affect the strength of observed associations?
It generally biases associations toward the null, weakening observed relationships
What is the role of “Cox proportional hazards model” in survival analysis?
It estimates the hazard ratio for covariates while accounting for censored data
How does “confounding by calendar time” distort epidemiological findings?
Temporal changes unrelated to the exposure can confound associations with outcomes
What is the impact of “spatial autocorrelation” on geostatistical analyses?
It violates independence assumptions, affecting the validity of statistical inferences
How can “selection bias” occur in retrospective cohort studies?
When selection of participants is related to both exposure and outcome, biasing results
What is the consequence of “collider bias” in causal inference?
Conditioning on a common effect of two variables can induce a spurious association
How does “digit preference bias” arise in self-reported health data?
Rounding or clustering at preferred numbers leads to inaccurate reports
What is the role of “g-estimation” in addressing time-varying confounding?
It estimates causal effects by simulating potential outcomes under different interventions
How can “loss to follow-up” impact the results of longitudinal studies?
It can lead to biased estimates if related to both exposure and outcome
What is the impact of “confounding by indication” on treatment effect estimates?
It biases estimates when treatment choice is related to prognosis
How does “interaction” complicate the analysis of epidemiological data?
It indicates that the effect of one variable depends on the level of another
What is the purpose of “instrumental variable analysis” in observational studies?
To estimate causal effects when confounding is present and randomization is not possible
How can “missing data” mechanisms affect the conclusions of a study?
Different mechanisms (MCAR, MAR, MNAR) require specific handling to avoid biased results
What is the consequence of “spatial heterogeneity” in exposure assessment?
It can lead to biased exposure-outcome associations if not properly accounted for
How does “temporal bias” affect the interpretation of time-to-event data?
Changes in risk over time can confound the association between exposure and outcome
What is the significance of “latent variable models” in complex causal pathways?
They estimate the influence of unobserved factors on observed variables
How does “attrition bias” arise in randomized controlled trials?
Differential dropout rates between treatment groups can bias results
What is the role of “Bayesian hierarchical models” in multi-level data analysis?
They provide a framework for modeling data with complex dependency structures
How can “overfitting” affect the predictive accuracy of statistical models?
The model may capture noise rather than true signal, reducing generalizability
What is the impact of “regression to the mean” in repeated measures?
It can lead to spurious associations if not properly accounted for
How does “density-dependent selection” influence population dynamics?
Competition for resources leads to selection pressures based on population density
How does “Mendelian randomization” help infer causality in observational studies?
Uses genetic variants as proxies for modifiable exposures to infer causality
What is the effect of “temporal misalignment” in spatio-temporal epidemiological models?
It can lead to biased estimates when spatial and temporal data are not synchronized
How does “case-crossover design” address transient exposures in epidemiology?
It compares exposure status during “hazard” period to control periods within the same individual
What is the role of “causal diagrams” (DAGs) in epidemiological research?
They help visualize and identify potential sources of bias and confounding
How can “survival bias” affect the interpretation of longevity studies?
By focusing on those who survive longer, potentially skewing results
What is the consequence of “ecological bias” in group-level epidemiological studies?
It can lead to incorrect inferences if group-level data is applied to individuals
How does “bias amplification” occur in epidemiological research?
When flawed data collection or analysis methods exaggerate existing biases
What is the significance of “non-inferiority trials” in clinical research?
They determine if a new treatment is not worse than an existing treatment by a specified margin
How can “genetic confounding” obscure associations in genome-wide association studies (GWAS)?
Genetic variants linked to multiple traits may confound associations
What is the purpose of “directed acyclic graphs” (DAGs) in causal inference?
They map out causal relationships to identify confounders and biases
How does “left truncation” affect the analysis of cohort data?
It excludes individuals who experience the event before study entry, potentially biasing results
What is the impact of “information bias” in self-reported data?
It can lead to systematic errors in estimating exposure or outcome due to inaccurate recall
How does “transmission heterogeneity” influence infectious disease modeling?
Variation in how individuals transmit disease can affect outbreak dynamics
How can “selection on the dependent variable” introduce bias in study conclusions?
By selecting cases based on outcomes, leading to biased associations
What is the role of “propensity score stratification” in observational studies?
It divides the sample into strata based on propensity scores to control for confounding
How does “survivorship bias” affect studies of historical data?
By focusing on data from entities that survived, potentially skewing results
What is the consequence of “measurement error” in exposure assessment?
It can lead to misclassification and biased estimates of exposure-outcome relationships
How can “spatial interpolation” assist in epidemiological mapping?
It estimates values for locations without data, aiding in visualizing spatial patterns
What is “overdispersion” and how does it affect Poisson regression models?
Greater variability in data than expected, leading to underestimated standard errors
How does “non-response bias” impact the validity of survey-based studies?
Differences in characteristics between responders and non-responders can bias results
What is the role of “sensitivity analysis” in evaluating robustness of epidemiological findings?
To assess how results change under different assumptions or scenarios
How can “exposure misclassification” lead to bias in epidemiological studies?
It causes inaccuracies in categorizing exposure status, potentially distorting associations
What is the impact of “temporal trends” in longitudinal epidemiological studies?
They can confound associations if changes over time are not correctly modeled
How does “spatial clustering” affect the interpretation of disease incidence data?
It may indicate non-random distribution of cases, suggesting potential sources
What is “cross-level bias” and how does it occur in multilevel models?
It arises when effects at one level are incorrectly attributed to another, skewing results
How can “instrumental variable analysis” mitigate confounding in non-experimental studies?
By using instruments that influence exposure but not outcome directly, to estimate causal effects
What is the consequence of “digit preference bias” in epidemiological surveys?
It leads to rounding errors due to preferences for certain numbers, affecting accuracy
How does “ascertainment bias” affect case-control study results?
Differential detection of cases and controls can bias the estimated association
What is the significance of “latent class analysis” in identifying unobserved population subgroups?
It classifies individuals into distinct groups based on response patterns, revealing hidden structure
How can “density sampling” reduce bias in nested case-control studies?
By matching controls to cases on time of entry into the cohort, controlling for time
What is the effect of “seasonal variation” on disease incidence data?
It can introduce periodic changes in incidence, confounding associations if not modeled
How does “time-varying covariate” complicate survival analysis?
It requires models that account for changes in covariates over time to avoid bias
What is the role of “inverse probability of censoring weights” in survival analysis?
They adjust for informative censoring, helping to recover unbiased estimates
How can “geographic information systems” (GIS) enhance epidemiological research?
They provide tools for mapping and analyzing spatial data, revealing patterns and relationships
What is the impact of “heteroscedasticity” on linear regression models?
It leads to inefficient and biased estimates, as variance is non-constant across observations
How does “left censoring” affect the analysis of time-to-event data?
It biases estimates by excluding individuals who experienced the event before study entry
What is the significance of “non-differential misclassification” in epidemiological studies?
It generally biases results toward the null, weakening observed associations
How can “confounding by indication” distort treatment effects in observational studies?
Treatment choices based on prognosis may confound associations with outcomes
What is “non-collapsibility” and how does it affect interpretation of odds ratios?
The odds ratio can change with adjustment, even without confounding, complicating interpretation
How does “transmission heterogeneity” influence the dynamics of infectious disease spread?
Variation in transmission rates among individuals affects outbreak potential and control strategies
What is the role of “propensity score matching” in reducing bias in observational studies?
It equates groups on observed covariates to reduce confounding, allowing causal inference
How can “overdispersion” affect the fit of Poisson regression models?
It leads to underestimated variance, resulting in overly narrow confidence intervals
What is the consequence of “measurement error” in risk factor assessment?
It can lead to misclassification and biased estimates of exposure-outcome relationships
How does “attrition bias” impact the validity of longitudinal studies?
Differential loss to follow-up can bias estimates if related to both exposure and outcome
