Counterfactual-Based Prevented and Preventable Proportions

Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions beca...

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Main Authors: Yamada Kentaro, Kuroki Manabu
Format: Article
Language:English
Published: De Gruyter 2017-09-01
Series:Journal of Causal Inference
Subjects:
Online Access:https://doi.org/10.1515/jci-2016-0020
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author Yamada Kentaro
Kuroki Manabu
author_facet Yamada Kentaro
Kuroki Manabu
author_sort Yamada Kentaro
collection DOAJ
description Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range [0.000,1.000]$[0.000,1.000]$. To solve this problem, this paper proposes counterfactual-based prevented and preventable proportions. When both causal effects and observed probabilities are available, we show that the proposed measures are identifiable under the negative monotonicity assumption. Additionally, when the negative monotonicity assumption is violated, we formulate the bounds on the proposed measures. We also show that negative monotonicity together with exogeneity induces equivalence between the proposed measures and existing measures.
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spelling doaj.art-7799b1dbd11c45c78dfa597d63139cd02022-12-21T21:35:36ZengDe GruyterJournal of Causal Inference2193-36772193-36852017-09-01525677010.1515/jci-2016-0020Counterfactual-Based Prevented and Preventable ProportionsYamada Kentaro0Kuroki Manabu1Department of Statistical Science, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, JapanGraduate School of Engineering, Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, JapanPrevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range [0.000,1.000]$[0.000,1.000]$. To solve this problem, this paper proposes counterfactual-based prevented and preventable proportions. When both causal effects and observed probabilities are available, we show that the proposed measures are identifiable under the negative monotonicity assumption. Additionally, when the negative monotonicity assumption is violated, we formulate the bounds on the proposed measures. We also show that negative monotonicity together with exogeneity induces equivalence between the proposed measures and existing measures.https://doi.org/10.1515/jci-2016-0020prevented fractionpreventable fractionattributable fractionexcess fractionvaccine efficacy
spellingShingle Yamada Kentaro
Kuroki Manabu
Counterfactual-Based Prevented and Preventable Proportions
Journal of Causal Inference
prevented fraction
preventable fraction
attributable fraction
excess fraction
vaccine efficacy
title Counterfactual-Based Prevented and Preventable Proportions
title_full Counterfactual-Based Prevented and Preventable Proportions
title_fullStr Counterfactual-Based Prevented and Preventable Proportions
title_full_unstemmed Counterfactual-Based Prevented and Preventable Proportions
title_short Counterfactual-Based Prevented and Preventable Proportions
title_sort counterfactual based prevented and preventable proportions
topic prevented fraction
preventable fraction
attributable fraction
excess fraction
vaccine efficacy
url https://doi.org/10.1515/jci-2016-0020
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