Confounding and Bias Flashcards
What is confounding in research studies?
Confounding occurs when an extraneous variable is associated with both the exposure and outcome of interest, leading to a distortion of the true relationship between them.
How does confounding differ from bias?
Confounding arises from systematic errors in study design or conduct that distort the association between exposure and outcome, while bias refers to systematic errors that affect the validity of study results.
Explain the difference between selection bias and information bias.
Selection bias occurs when participants are selected in a manner that systematically distorts the association between exposure and outcome, while information bias arises from errors in data collection or measurement.
What measures can be taken to minimize confounding in observational studies?
To minimize confounding in observational studies, researchers can use techniques such as matching, stratification, multivariable regression analysis, and restriction of study populations.
How does randomization help control for confounding in experimental studies?
Randomization helps control for confounding in experimental studies by ensuring that participants are allocated to treatment groups in a manner that is independent of potential confounders, thereby balancing covariates across groups.
Describe the role of stratification in controlling for confounding.
Stratification involves analyzing data separately within subgroups defined by potential confounders, allowing for a more precise estimation of the exposure-outcome relationship within each stratum.
What is residual confounding, and how can it be addressed?
Residual confounding refers to confounding that remains even after adjusting for known confounders, and it can be addressed by collecting additional data on unmeasured confounders or using sensitivity analysis to assess its potential impact.
What is observer bias, and how can it affect study outcomes?
Observer bias occurs when the knowledge or expectations of the researcher influence the outcome assessment, leading to systematic errors in study results.
How can blinding be used to minimize observer bias?
Blinding involves concealing information about the exposure or outcome status from investigators or participants to minimize observer bias in study conduct or outcome assessment.
What is recall bias, and in what types of studies is it commonly observed?
Recall bias occurs when participants in a study with a retrospective design inaccurately recall past exposures or events, leading to differential misclassification of exposure and potentially biasing study results.
How does measurement error contribute to bias in research studies?
Measurement error refers to inaccuracies or imprecision in the measurement of exposure, outcome, or covariates, which can introduce bias by distorting the observed association between them.
What is selection bias, and how can it affect study validity?
Selection bias occurs when participants are systematically selected or excluded from a study in a manner that distorts the true association between exposure and outcome, compromising the validity of study findings.
What is attrition bias, and how does it impact longitudinal studies?
Attrition bias occurs when participants drop out of a longitudinal study disproportionately based on their exposure or outcome status, leading to biased estimates of association over time.
Explain the concept of confounding by indication in healthcare research.
Confounding by indication occurs when the indication for treatment is associated with both the exposure and the outcome, leading to a spurious association between exposure and outcome in pharmacoepidemiological studies.
What strategies can be used to minimize bias in retrospective studies?
Strategies to minimize bias in retrospective studies include using standardized data collection tools, blinding outcome assessors, matching or adjusting for potential confounders, and conducting sensitivity analyses.
How does confounding differ between observational and experimental studies?
Confounding can arise in both observational and experimental studies, but experimental studies have the advantage of randomization, which helps control for confounding by ensuring comparability between treatment groups.
What is publication bias, and how can it affect the interpretation of research findings?
Publication bias occurs when studies with significant or positive results are more likely to be published than those with non-significant or negative results, leading to an overestimation of the true effect size and distorting the evidence base.
Describe the concept of reverse causation and its implications for causal inference.
Reverse causation occurs when the apparent cause of an outcome is actually the result of that outcome, leading to incorrect inference about the directionality of the relationship between exposure and outcome.
How can sensitivity analysis be used to assess the impact of confounding?
Sensitivity analysis involves assessing the robustness of study results by varying assumptions, methods, or inclusion criteria to determine the extent to which they are influenced by confounding or other sources of bias.
What is information bias, and how can it be minimized in data collection?
Information bias arises from errors in the measurement or classification of exposure, outcome, or covariates, and it can be minimized through careful study design, standardized data collection protocols, and blinding of outcome assessors.
What role do sensitivity and specificity play in minimizing bias in diagnostic testing?
Sensitivity and specificity measure the accuracy of diagnostic tests in correctly identifying true positives and true negatives, respectively, and they help minimize bias by providing information about the reliability of test results.
How can matching be used to control for confounding in observational studies?
Matching involves selecting control participants who are similar to cases based on potential confounders, thereby ensuring comparability between exposure groups and minimizing confounding in observational studies.
Explain the concept of collider bias and its implications for causal inference.
Collider bias occurs when conditioning on a common effect of exposure and outcome introduces bias into the association between exposure and outcome, leading to spurious or distorted results in causal inference.
What is response bias, and how can it affect survey-based research?
Response bias occurs when participants provide inaccurate or biased responses to survey questions, leading to systematic errors in study results and compromising the validity of findings.
