Online Program

A power comparison between hierarchical and marginal longitudinal logistic regression models: Application to marijuana usage

Wednesday, November 6, 2013 : 10:50 a.m. - 11:10 a.m.

Trent Lalonde, PhD, Department of Applied Statistics and Research Methods, Univeristy of Northern Colorado, Greeley, CO
Kristina Phillips, PhD, Department of Psychological Sciences, University of Northern Colorado, Greeley, CO
Michael Phillips, PhD, Department of Psychological Sciences, University of Northern Colorado, Greeley, CO
Marijuana use is a current problem in the U.S., with high rates among young adults, and a range of associated health and cognitive outcomes. Prior studies have found high co-morbidity between Marijuana addiction and other psychological characteristics such as mood, anxiety, and psychotic disorders. Modeling connections between these variables and usage over time is rare, but provides information about individual-level dynamic relationships. Data for this study were collected using Ecological Momentary Assessment (EMA) methods, where participants respond to text messages three times daily for fourteen consecutive days. EMA will lead to longitudinal data, accompanied by many options for longitudinal data analysis. Modeling the binary variable "usage" suggests a logistic regression model is appropriate. But the longitudinal nature of the data can be accounted for using either hierarchical or marginal models. A hierarchical logistic regression model is one that includes additional random terms, typically associated with clustering by group or individual. A marginal longitudinal logistic regression model will include direct modeling of the response error structure to reflect expected associations due to clustering. Based on pilot data, a power analysis was performed to compare a random-intercept hierarchical logistic regression model, a random-slopes hierarchical logistic regression model, and a GEE marginal logistic regression model. The GEE model was applied using auto-regressive, exchangeable, and independent working correlation structures. A comparison of model performance includes consideration of different conclusions allowable for each model. In general the random-intercept hierarchical logistic regression model shows the strongest properties and ultimately was selected for the analysis.

Learning Areas:

Biostatistics, economics
Social and behavioral sciences

Learning Objectives:
Differentiate between hierarchical and marginal logistic regression models. Compare performance of hierarchical and marginal models with respect to power. Evaluate multiple potential models, and select the most appropriate for the analysis.

Keyword(s): Biostatistics, Marijuana Dependence

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am an Assistant Professor of Applied Statistics and have research and consulting experience working with binary longitudinal data.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.