Cargando…

Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs

BACKGROUND: Predictive models within epilepsy are frequently developed via Cox’s proportional hazards models. These models estimate risk of a specified event such as 12-month remission. They are relatively simple to produce, have familiar output, and are useful to answer questions about short-term p...

Descripción completa

Detalles Bibliográficos
Autores principales: Bonnett, Laura J., Hutton, Jane L., Marson, Anthony G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158047/
https://www.ncbi.nlm.nih.gov/pubmed/32293277
http://dx.doi.org/10.1186/s12874-020-00965-5
_version_ 1783522459168800768
author Bonnett, Laura J.
Hutton, Jane L.
Marson, Anthony G.
author_facet Bonnett, Laura J.
Hutton, Jane L.
Marson, Anthony G.
author_sort Bonnett, Laura J.
collection PubMed
description BACKGROUND: Predictive models within epilepsy are frequently developed via Cox’s proportional hazards models. These models estimate risk of a specified event such as 12-month remission. They are relatively simple to produce, have familiar output, and are useful to answer questions about short-term prognosis. However, the Cox model only considers time to first event rather than all seizures after starting treatment for example. This makes assessing change in seizure rates over time difficult. Variants to the Cox model exist enabling recurrent events to be modelled. One such variant is the Prentice, Williams and Peterson – Total Time (PWP-TT) model. An alternative is the negative binomial model for event counts. This study aims to demonstrate the differences between the three approaches, and to consider the benefits of the PWP-TT approach for assessing change in seizure rates over time. METHODS: Time to 12-month remission and time to first seizure after randomisation were modelled using the Cox model. Risk of seizure recurrence was modelled using the PWP-TT model, including all seizures across the whole follow-up period. Seizure counts were modelled using negative binomial regression. Differences between the approaches were demonstrated using participants recruited to the UK-based multi-centre Standard versus New Antiepileptic Drug (SANAD) study. RESULTS: Results from the PWP-TT model were similar to those from the conventional Cox and negative binomial models. In general, the direction of effect was consistent although the variables included in the models and the significance of the predictors varied. The confidence intervals obtained via the PWP-TT model tended to be narrower due to the increase in statistical power of the model. CONCLUSIONS: The Cox model is useful for determining the initial response to treatment and potentially informing when the next intervention may be required. The negative binomial model is useful for modelling event counts. The PWP-TT model extends the Cox model to all included events. This is useful in determining the longer-term effects of treatment policy. Such a model should be considered when designing future clinical trials in medical conditions typified by recurrent events to improve efficiency and statistical power as well as providing evidence regarding changes in event rates over time.
format Online
Article
Text
id pubmed-7158047
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-71580472020-04-20 Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs Bonnett, Laura J. Hutton, Jane L. Marson, Anthony G. BMC Med Res Methodol Research Article BACKGROUND: Predictive models within epilepsy are frequently developed via Cox’s proportional hazards models. These models estimate risk of a specified event such as 12-month remission. They are relatively simple to produce, have familiar output, and are useful to answer questions about short-term prognosis. However, the Cox model only considers time to first event rather than all seizures after starting treatment for example. This makes assessing change in seizure rates over time difficult. Variants to the Cox model exist enabling recurrent events to be modelled. One such variant is the Prentice, Williams and Peterson – Total Time (PWP-TT) model. An alternative is the negative binomial model for event counts. This study aims to demonstrate the differences between the three approaches, and to consider the benefits of the PWP-TT approach for assessing change in seizure rates over time. METHODS: Time to 12-month remission and time to first seizure after randomisation were modelled using the Cox model. Risk of seizure recurrence was modelled using the PWP-TT model, including all seizures across the whole follow-up period. Seizure counts were modelled using negative binomial regression. Differences between the approaches were demonstrated using participants recruited to the UK-based multi-centre Standard versus New Antiepileptic Drug (SANAD) study. RESULTS: Results from the PWP-TT model were similar to those from the conventional Cox and negative binomial models. In general, the direction of effect was consistent although the variables included in the models and the significance of the predictors varied. The confidence intervals obtained via the PWP-TT model tended to be narrower due to the increase in statistical power of the model. CONCLUSIONS: The Cox model is useful for determining the initial response to treatment and potentially informing when the next intervention may be required. The negative binomial model is useful for modelling event counts. The PWP-TT model extends the Cox model to all included events. This is useful in determining the longer-term effects of treatment policy. Such a model should be considered when designing future clinical trials in medical conditions typified by recurrent events to improve efficiency and statistical power as well as providing evidence regarding changes in event rates over time. BioMed Central 2020-04-15 /pmc/articles/PMC7158047/ /pubmed/32293277 http://dx.doi.org/10.1186/s12874-020-00965-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Bonnett, Laura J.
Hutton, Jane L.
Marson, Anthony G.
Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title_full Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title_fullStr Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title_full_unstemmed Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title_short Modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
title_sort modelling seizure rates rather than time to an event within clinical trials of antiepileptic drugs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158047/
https://www.ncbi.nlm.nih.gov/pubmed/32293277
http://dx.doi.org/10.1186/s12874-020-00965-5
work_keys_str_mv AT bonnettlauraj modellingseizureratesratherthantimetoaneventwithinclinicaltrialsofantiepilepticdrugs
AT huttonjanel modellingseizureratesratherthantimetoaneventwithinclinicaltrialsofantiepilepticdrugs
AT marsonanthonyg modellingseizureratesratherthantimetoaneventwithinclinicaltrialsofantiepilepticdrugs