Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model

Abstract Background Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the...

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Autores principales:	Xiaolei Lin, Robin Mermelstein, Donald Hedeker
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Mixed cumulative logit model Multivariate longitudinal outcomes Non-proportional odds assumption Substance usage Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/d2474f9951a241a097645597f562c764
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spelling	oai:doaj.org-article:d2474f9951a241a097645597f562c7642021-11-08T11:16:00ZAnalysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model10.1186/s12874-021-01444-11471-2288https://doaj.org/article/d2474f9951a241a097645597f562c7642021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01444-1https://doaj.org/toc/1471-2288Abstract Background Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate substance use data. Methods We describe how the binary random slope logistic regression model can be extended to the multi-category ordinal outcomes. We also describe how the proportional odds assumption can be relaxed by allowing differential covariate effects on different cumulative logits for multiple outcomes. Data are analyzed from a P01 study that evaluates the usage levels of cigarettes, alcohol and marijuana repeatedly across 8 measurement waves during 7 consecutive years. Results 1263 subjects participated in the study with informed consent, among whom 56.6% are females. Males and females show significant differences in terms of the time trend for substance use. Specifically, males showed steeper trends on cigarette and marijuana use over time compared to females, while less so for alcohol. For all three substances, age effects appear to be different for different cumulative logits, indicating the violation of proportional odds assumption. Conclusions The multivariate mixed cumulative logit model offers the most flexibility and allows one to examine the inter-substance association when proportional odds assumption is violated.Xiaolei LinRobin MermelsteinDonald HedekerBMCarticleMixed cumulative logit modelMultivariate longitudinal outcomesNon-proportional odds assumptionSubstance usageMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mixed cumulative logit model Multivariate longitudinal outcomes Non-proportional odds assumption Substance usage Medicine (General) R5-920
spellingShingle	Mixed cumulative logit model Multivariate longitudinal outcomes Non-proportional odds assumption Substance usage Medicine (General) R5-920 Xiaolei Lin Robin Mermelstein Donald Hedeker Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
description	Abstract Background Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate substance use data. Methods We describe how the binary random slope logistic regression model can be extended to the multi-category ordinal outcomes. We also describe how the proportional odds assumption can be relaxed by allowing differential covariate effects on different cumulative logits for multiple outcomes. Data are analyzed from a P01 study that evaluates the usage levels of cigarettes, alcohol and marijuana repeatedly across 8 measurement waves during 7 consecutive years. Results 1263 subjects participated in the study with informed consent, among whom 56.6% are females. Males and females show significant differences in terms of the time trend for substance use. Specifically, males showed steeper trends on cigarette and marijuana use over time compared to females, while less so for alcohol. For all three substances, age effects appear to be different for different cumulative logits, indicating the violation of proportional odds assumption. Conclusions The multivariate mixed cumulative logit model offers the most flexibility and allows one to examine the inter-substance association when proportional odds assumption is violated.
format	article
author	Xiaolei Lin Robin Mermelstein Donald Hedeker
author_facet	Xiaolei Lin Robin Mermelstein Donald Hedeker
author_sort	Xiaolei Lin
title	Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
title_short	Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
title_full	Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
title_fullStr	Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
title_full_unstemmed	Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
title_sort	analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model
publisher	BMC
publishDate	2021
url	https://doaj.org/article/d2474f9951a241a097645597f562c764
work_keys_str_mv	AT xiaoleilin analysisofmultivariatelongitudinalsubstanceuseoutcomesusingmultivariatemixedcumulativelogitmodel AT robinmermelstein analysisofmultivariatelongitudinalsubstanceuseoutcomesusingmultivariatemixedcumulativelogitmodel AT donaldhedeker analysisofmultivariatelongitudinalsubstanceuseoutcomesusingmultivariatemixedcumulativelogitmodel
_version_	1718442281870032896

Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model

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