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Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination
BACKGROUND: The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Neverthel...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715511/ https://www.ncbi.nlm.nih.gov/pubmed/29202691 http://dx.doi.org/10.1186/s12874-017-0434-1 |
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author | Lusivika-Nzinga, Clovis Selinger-Leneman, Hana Grabar, Sophie Costagliola, Dominique Carrat, Fabrice |
author_facet | Lusivika-Nzinga, Clovis Selinger-Leneman, Hana Grabar, Sophie Costagliola, Dominique Carrat, Fabrice |
author_sort | Lusivika-Nzinga, Clovis |
collection | PubMed |
description | BACKGROUND: The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. METHODS: We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. RESULTS: Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. CONCLUSION: Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0434-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5715511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57155112017-12-08 Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination Lusivika-Nzinga, Clovis Selinger-Leneman, Hana Grabar, Sophie Costagliola, Dominique Carrat, Fabrice BMC Med Res Methodol Research Article BACKGROUND: The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. METHODS: We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. RESULTS: Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. CONCLUSION: Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0434-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-04 /pmc/articles/PMC5715511/ /pubmed/29202691 http://dx.doi.org/10.1186/s12874-017-0434-1 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 | Research Article Lusivika-Nzinga, Clovis Selinger-Leneman, Hana Grabar, Sophie Costagliola, Dominique Carrat, Fabrice Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title | Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title_full | Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title_fullStr | Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title_full_unstemmed | Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title_short | Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
title_sort | performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715511/ https://www.ncbi.nlm.nih.gov/pubmed/29202691 http://dx.doi.org/10.1186/s12874-017-0434-1 |
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