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Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach.

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Title: Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach.
Authors: Burgette, Lane F. (AUTHOR), Cabreros, Irineo (AUTHOR), Han, Bing (AUTHOR), Paddock, Susan M. (AUTHOR)
Source: American Journal of Drug & Alcohol Abuse. 2021, Vol. 47 Issue 5, p559-568. 10p.
Subjects: Substance abuse, Temperance, Inferential statistics, Integrated software, Mixtures
Abstract: Background: In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations. Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data. Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan. Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings. Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency. [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Drug & Alcohol Abuse is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Label: Title
  Group: Ti
  Data: Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Burgette%2C+Lane+F%2E%22">Burgette, Lane F.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cabreros%2C+Irineo%22">Cabreros, Irineo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Bing%22">Han, Bing</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paddock%2C+Susan+M%2E%22">Paddock, Susan M.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Drug+%26+Alcohol+Abuse%22">American Journal of Drug & Alcohol Abuse</searchLink>. 2021, Vol. 47 Issue 5, p559-568. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Substance+abuse%22">Substance abuse</searchLink><br /><searchLink fieldCode="DE" term="%22Temperance%22">Temperance</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+software%22">Integrated software</searchLink><br /><searchLink fieldCode="DE" term="%22Mixtures%22">Mixtures</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations. Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data. Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan. Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings. Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of American Journal of Drug & Alcohol Abuse is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00952990.2021.1946070
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Temperance
        Type: general
      – SubjectFull: Inferential statistics
        Type: general
      – SubjectFull: Integrated software
        Type: general
      – SubjectFull: Mixtures
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            NameFull: Cabreros, Irineo
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            NameFull: Han, Bing
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              Text: 2021
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            – TitleFull: American Journal of Drug & Alcohol Abuse
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