Tutorial in Biostatistics: The use of generalized additive models to evaluate alcohol consumption as an exposure variable. (1st April 2020)
- Record Type:
- Journal Article
- Title:
- Tutorial in Biostatistics: The use of generalized additive models to evaluate alcohol consumption as an exposure variable. (1st April 2020)
- Main Title:
- Tutorial in Biostatistics: The use of generalized additive models to evaluate alcohol consumption as an exposure variable
- Authors:
- White, Laura F
Jiang, Wenqing
Ma, Yicheng
So-Armah, Kaku
Samet, Jeffrey H.
Cheng, Debbie M - Abstract:
- Highlights: Analytics to quantify alcohol consumption can oversimplify the original measure. Generalized additive models (GAMs) are a useful tool for this problem. GAMs permit thorough exploration of the relationship between alcohol and outcomes. To make GAMs more accessible to researchers, we provide code and an example. Abstract: Alcohol consumption is a commonly studied risk factor for many poor health outcomes. Various instruments exist to measure alcohol consumption, including the AUDIT-C, Single Alcohol Screening Questionnaire (SASQ) and Timeline Followback. The information gathered by these instruments is often simplified and analyzed as a dichotomous measure, risking the loss of information of potentially prognostic value. We discuss generalized additive models (GAM) as a useful tool to understand the association between alcohol consumption and a health outcome. We demonstrate how this analytic strategy can guide the development of a regression model that retains maximal information about alcohol consumption. We illustrate these approaches using data from the Russia ARCH (Alcohol Research Collaboration on HIV/AIDS) study to analyze the association between alcohol consumption and biomarker of systemic inflammation, interleukin-6 (IL-6). We provide SAS and R code to implement these methods. GAMs have the potential to increase statistical power and allow for better elucidation of more nuanced and non-linear associations between alcohol consumption and important healthHighlights: Analytics to quantify alcohol consumption can oversimplify the original measure. Generalized additive models (GAMs) are a useful tool for this problem. GAMs permit thorough exploration of the relationship between alcohol and outcomes. To make GAMs more accessible to researchers, we provide code and an example. Abstract: Alcohol consumption is a commonly studied risk factor for many poor health outcomes. Various instruments exist to measure alcohol consumption, including the AUDIT-C, Single Alcohol Screening Questionnaire (SASQ) and Timeline Followback. The information gathered by these instruments is often simplified and analyzed as a dichotomous measure, risking the loss of information of potentially prognostic value. We discuss generalized additive models (GAM) as a useful tool to understand the association between alcohol consumption and a health outcome. We demonstrate how this analytic strategy can guide the development of a regression model that retains maximal information about alcohol consumption. We illustrate these approaches using data from the Russia ARCH (Alcohol Research Collaboration on HIV/AIDS) study to analyze the association between alcohol consumption and biomarker of systemic inflammation, interleukin-6 (IL-6). We provide SAS and R code to implement these methods. GAMs have the potential to increase statistical power and allow for better elucidation of more nuanced and non-linear associations between alcohol consumption and important health outcomes. … (more)
- Is Part Of:
- Drug and alcohol dependence. Volume 209(2020)
- Journal:
- Drug and alcohol dependence
- Issue:
- Volume 209(2020)
- Issue Display:
- Volume 209, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 209
- Issue:
- 2020
- Issue Sort Value:
- 2020-0209-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-04-01
- Subjects:
- Generalized additive models -- Alcohol consumption -- AUDIT-C
Drug abuse -- Periodicals
Alcoholism -- Periodicals
616.86 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03768716 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.drugalcdep.2020.107944 ↗
- Languages:
- English
- ISSNs:
- 0376-8716
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3627.890000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13513.xml