EA3: A softmax algorithm for evidence appraisal aggregation.

Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act-approved in 2016 by the US Congress-permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising t...

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Autores principales:	Francesco De Pretis, Jürgen Landes
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fefc75da2f96405887d5f84980e261b8
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id	oai:doaj.org-article:fefc75da2f96405887d5f84980e261b8
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spelling	oai:doaj.org-article:fefc75da2f96405887d5f84980e261b82021-12-02T20:10:30ZEA3: A softmax algorithm for evidence appraisal aggregation.1932-620310.1371/journal.pone.0253057https://doaj.org/article/fefc75da2f96405887d5f84980e261b82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253057https://doaj.org/toc/1932-6203Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act-approved in 2016 by the US Congress-permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA3. Our algorithm employs the softmax function-a generalisation of the logistic function to multiple dimensions-which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.Francesco De PretisJürgen LandesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253057 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Francesco De Pretis Jürgen Landes EA3: A softmax algorithm for evidence appraisal aggregation.
description	Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act-approved in 2016 by the US Congress-permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA3. Our algorithm employs the softmax function-a generalisation of the logistic function to multiple dimensions-which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.
format	article
author	Francesco De Pretis Jürgen Landes
author_facet	Francesco De Pretis Jürgen Landes
author_sort	Francesco De Pretis
title	EA3: A softmax algorithm for evidence appraisal aggregation.
title_short	EA3: A softmax algorithm for evidence appraisal aggregation.
title_full	EA3: A softmax algorithm for evidence appraisal aggregation.
title_fullStr	EA3: A softmax algorithm for evidence appraisal aggregation.
title_full_unstemmed	EA3: A softmax algorithm for evidence appraisal aggregation.
title_sort	ea3: a softmax algorithm for evidence appraisal aggregation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fefc75da2f96405887d5f84980e261b8
work_keys_str_mv	AT francescodepretis ea3asoftmaxalgorithmforevidenceappraisalaggregation AT jurgenlandes ea3asoftmaxalgorithmforevidenceappraisalaggregation
_version_	1718375041085734912

EA3: A softmax algorithm for evidence appraisal aggregation.

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