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This deck focuses on Study Design And Bias, giving you a quick way to review the definitions, rules, and examples that matter most for NAPLEX.
Study Study Design And Bias in NAPLEX with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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What bias occurs when baseline differences between groups distort the treatment effect estimate?
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Selection bias. Non-random group differences at baseline can confound the observed effect, leading to inaccurate conclusions about treatment impact.
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This deck focuses on Study Design And Bias, giving you a quick way to review the definitions, rules, and examples that matter most for NAPLEX.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: Selection bias. Non-random group differences at baseline can confound the observed effect, leading to inaccurate conclusions about treatment impact.
Answer: RR=RiskunexposedRiskexposed. This formula quantifies the multiplicative increase in risk attributable to exposure by comparing group incidences.
Answer: Cohort study. Selecting exposed individuals first enables following them to observe outcomes, ideal for uncommon exposures in populations.
Answer: OR=OddsunexposedOddsexposed. It expresses the ratio of exposure probabilities given the outcome, useful for approximating risk in certain designs.
Answer: Keeping group assignment unknown to reduce bias. By masking participants, investigators, or both, it minimizes performance and detection biases that could skew results.
Answer: Randomized controlled trial (RCT). This design minimizes selection bias by randomly allocating participants, ensuring comparable groups for evaluating intervention effects.
Answer: Attrition bias. When dropout rates vary by group and relate to the outcome, it can introduce systematic error in effect estimates.
Answer: Odds ratio. Since sampling is by outcome, it calculates odds of exposure in cases versus controls, approximating relative risk for rare events.
Answer: Detection (ascertainment) bias. Systematic variations in how outcomes are measured or diagnosed between groups can lead to over- or underestimation of effects.
Answer: Performance bias. Unequal provision of additional treatments or differences in compliance post-assignment can confound the true intervention effect.
Answer: Sampling (selection) bias. Non-random or biased participant selection can lead to results that do not generalize to the broader intended population.
Answer: Relative risk (risk ratio). In cohorts, incidence in exposed and unexposed groups directly yields this measure of how exposure modifies outcome risk.
Answer: Outcome change due to expectations, not active treatment. Psychological expectations of benefit can produce perceived or actual improvements, necessitating placebo controls for isolation.
Answer: Balance confounders between groups on average. Randomization ensures that known and unknown confounders are distributed evenly, allowing causal inference about the intervention.
Answer: Publication bias. Selective reporting of positive findings skews the literature, overestimating effects in systematic reviews and meta-analyses.
Answer: Meta-analysis. This method combines data from several studies statistically, increasing precision and power to detect effects across heterogeneous evidence.
Answer: A third factor distorts the exposure–outcome association. An extraneous variable linked to both exposure and outcome can create a spurious or masked relationship in the analysis.
Answer: Associated with exposure and independently affects outcome. To confound, the factor must correlate with exposure, influence outcome independently, and not mediate the causal path.
Answer: Intention-to-treat analysis. This approach preserves randomization benefits, providing a pragmatic estimate of effectiveness in real-world adherence scenarios.
Answer: Case-control study. Starting with cases allows efficient recruitment for infrequent outcomes, making it suitable for rare disease investigations.
Answer: Hiding the upcoming assignment from enrollers. It prevents investigators from influencing enrollment based on anticipated group assignment, thus preserving randomization integrity.
Answer: Differential accuracy of reported past exposure. Participants with the outcome may remember or report exposures differently than controls, leading to biased association measures.