Cargando…
Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data
BACKGROUND: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurr...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132199/ https://www.ncbi.nlm.nih.gov/pubmed/25073652 http://dx.doi.org/10.1186/1475-2875-13-293 |
_version_ | 1782330579472089088 |
---|---|
author | Sagara, Issaka Giorgi, Roch Doumbo, Ogobara K Piarroux, Renaud Gaudart, Jean |
author_facet | Sagara, Issaka Giorgi, Roch Doumbo, Ogobara K Piarroux, Renaud Gaudart, Jean |
author_sort | Sagara, Issaka |
collection | PubMed |
description | BACKGROUND: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. METHODS: This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. RESULTS: Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model. With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. CONCLUSION: Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures. |
format | Online Article Text |
id | pubmed-4132199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41321992014-08-18 Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data Sagara, Issaka Giorgi, Roch Doumbo, Ogobara K Piarroux, Renaud Gaudart, Jean Malar J Methodology BACKGROUND: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. METHODS: This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. RESULTS: Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model. With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. CONCLUSION: Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures. BioMed Central 2014-07-29 /pmc/articles/PMC4132199/ /pubmed/25073652 http://dx.doi.org/10.1186/1475-2875-13-293 Text en Copyright © 2014 Sagara et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Sagara, Issaka Giorgi, Roch Doumbo, Ogobara K Piarroux, Renaud Gaudart, Jean Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title | Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title_full | Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title_fullStr | Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title_full_unstemmed | Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title_short | Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
title_sort | modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132199/ https://www.ncbi.nlm.nih.gov/pubmed/25073652 http://dx.doi.org/10.1186/1475-2875-13-293 |
work_keys_str_mv | AT sagaraissaka modellingrecurrenteventscomparisonofstatisticalmodelswithcontinuousanddiscontinuousriskintervalsonrecurrentmalariaepisodesdata AT giorgiroch modellingrecurrenteventscomparisonofstatisticalmodelswithcontinuousanddiscontinuousriskintervalsonrecurrentmalariaepisodesdata AT doumboogobarak modellingrecurrenteventscomparisonofstatisticalmodelswithcontinuousanddiscontinuousriskintervalsonrecurrentmalariaepisodesdata AT piarrouxrenaud modellingrecurrenteventscomparisonofstatisticalmodelswithcontinuousanddiscontinuousriskintervalsonrecurrentmalariaepisodesdata AT gaudartjean modellingrecurrenteventscomparisonofstatisticalmodelswithcontinuousanddiscontinuousriskintervalsonrecurrentmalariaepisodesdata |