Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking

• Published in: Biometrics Vol. 72; no. 1; pp. 46 - 55
• Main Authors: Rathbun, Stephen L., Shiffman, Saul
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.03.2016; International Biometric Society
• Subjects: Affect; Algorithms; BIOMETRIC METHODOLOGY; biometry; Cigarette smoking; Computer Simulation; Data acquisition; data collection; Drug addiction; Ecological monitoring; Electronic devices; Electronic equipment; emotions; environmental factors; Fully exponential Laplace approximation; Humans; Incidence; Models, Statistical; Modified EM algorithm; Mood; Motivation; Personal digital assistants; Probability sample; Psychometrics - methods; Random covariate effects; Recurrence; Reproducibility of Results; Sensitivity and Specificity; Smartphones; Smoking; smoking (habit); Smoking - epidemiology; Smoking - psychology; Smoking cessation; Smoking Cessation - psychology; Smoking Cessation - statistics & numerical data; Smoking Prevention; Social Environment; Random covariate effects; Modified EM algorithm; Fully exponential Laplace approximation; Probability sample
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12416

Cigarette smoking is a prototypical example of a recurrent event. The pattern of recurrent smoking events may depend on time-varying covariates including mood and environmental variables. Fixed effects and frailty models for recurrent events data assume that smokers have a common association with time-varying covariates. We develop a mixed effects version of a recurrent events model that may be used to describe variation among smokers in how they respond to those covariates, potentially leading to the development of individual-based smoking cessation therapies. Our method extends the modified EM algorithm of Steele (1996) for generalized mixed models to recurrent events data with partially observed time-varying covariates. It is offered as an alternative to the method of Rizopoulos, Verbeke, and Lesaffre (2009) who extended Steele's (1996) algorithm to a joint-model for the recurrent events data and time-varying covariates. Our approach does not require a model for the time-varying covariates, but instead assumes that the time-varying covariates are sampled according to a Poisson point process with known intensity. Our methods are well suited to data collected using Ecological Momentary Assessment (EMA), a method of data collection widely used in the behavioral sciences to collect data on emotional state and recurrent events in the every-day environments of study subjects using electronic devices such as Personal Digital Assistants (PDA) or smart phones.