July 3, 2025Biometrics

Sensitivity analysis for attributable effects in case2 studies

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Sensitivity analysis reveals the impact of assumptions on attributable effects in case2 studies, indicating potential biases in treatment inference.

Sensitivity analysis shows that deviations from assumptions significantly affect inferences about the attributable effect in case2 studies, leading to potential biases.
Key findings indicate that unmeasured confounding can impact the perceived relationship between treatment and outcomes in these studies.
The authors present a framework for sensitivity analyses, focusing on the importance of realistic assumption testing in case2 studies.
The research utilizes the 1993 National Mortality Followback Survey to exemplify assumptions in real-data applications, emphasizing their critical role.

Chen et al. (2025) studied this question.

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Sensitivity analysis reveals the impact of assumptions on attributable effects in case2 studies, indicating potential biases in treatment inference.