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Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies
923
Citations
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References
2019
Year
Unmeasured ConfoundingTreatment EffectNegative ResultAdverse EventBiasRandomized Controlled TrialBiostatisticsPublic HealthRetrospective Cohort StudyStatisticsMedical StatisticObservational StudiesReliabilityMeta-analysisOutcomes ResearchE-value AnalysisMarginal Structural ModelsEpidemiologyPotential EffectPatient SafetyTime-varying ConfoundingMedicineReal World EvidenceSensitivity Analyses
This Guide to Statistics and Methods discusses E-value analysis, an alternative approach to sensitivity analyses for unmeasured confounding in observational studies that specifies the degree of unmeasured confounding that would need to be operative to negate observed results in a study.
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