Quantifying degrees of necessity and of sufficiency in cause-effect relationships with dichotomous and survival outcomes

DOI10.1002/SIM.8331zbMATH Open1546.62262WikidataQ92447542 ScholiaQ92447542MaRDI QIDQ6628731

Michael Schemper, Andreas Gleiss

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

logistic regressionCox regressionsufficient conditionnecessary conditionattributable riskexplained variation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• A new approach to estimate correlation coefficients in the presence of censoring and proportional hazards
• Nonparametric Estimation from Incomplete Observations
• Attributable risk function in the proportional hazards model for censored time-to-event
• Predictive Accuracy and Explained Variation in Cox Regression
• Attributable fractions: fundamental concepts and their visualization
• Partitioning methods for multifactorial risk attribution
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Related Items (2)

Explained variation and degrees of necessity and of sufficiency for competing risks survival data ⋮ Degrees of necessity and of sufficiency: further results and extensions, with an application to Covid-19 mortality in Austria

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