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Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks
A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observabl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9347664/ https://www.ncbi.nlm.nih.gov/pubmed/35928784 http://dx.doi.org/10.6339/21-jds1026 |
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author | Liu, Yutong Lin, Feng-Chang Lin, Jessica T. Li, Quefeng |
author_facet | Liu, Yutong Lin, Feng-Chang Lin, Jessica T. Li, Quefeng |
author_sort | Liu, Yutong |
collection | PubMed |
description | A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria. |
format | Online Article Text |
id | pubmed-9347664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93476642022-08-03 Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks Liu, Yutong Lin, Feng-Chang Lin, Jessica T. Li, Quefeng J Data Sci Article A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria. 2022-01 2021-12-09 /pmc/articles/PMC9347664/ /pubmed/35928784 http://dx.doi.org/10.6339/21-jds1026 Text en https://creativecommons.org/licenses/by/4.0/Open access article under the CC BY (https://creativecommons.org/licenses/by/4.0/) license. |
spellingShingle | Article Liu, Yutong Lin, Feng-Chang Lin, Jessica T. Li, Quefeng Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title | Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title_full | Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title_fullStr | Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title_full_unstemmed | Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title_short | Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks |
title_sort | dynamic classification of plasmodium vivax malaria recurrence: an application of classifying unknown cause of failure in competing risks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9347664/ https://www.ncbi.nlm.nih.gov/pubmed/35928784 http://dx.doi.org/10.6339/21-jds1026 |
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