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Timeline followback (TLFB) is often used in addiction research to monitor recent substance use, such as the number of abstinent days in the past week. TLFB data usually take the form of binomial counts that exhibit overdispersion and zero inflation. Motivated by a 12‐week randomized trial evaluating the efficacy of varenicline tartrate for smoking cessation among adolescents, we propose a Bayesian zero‐inflated beta‐binomial model for the analysis of longitudinal, bounded TLFB data. The model comprises a mixture of a point mass that accounts for zero inflation and a beta‐binomial distribution for the number of days abstinent in the past week. Because treatment effects appear to level off during the study, we introduce random changepoints for each study group to reflect group‐specific changes in treatment efficacy over time. The model also includes fixed and random effects that capture group‐ and subject‐level slopes before and after the changepoints. Using the model, we can accurately estimate the mean trend for each study group, test whether the groups experience changepoints simultaneously, and identify critical windows of treatment efficacy. For posterior computation, we propose an efficient Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis–Hastings steps. Our application shows that the varenicline group has a short‐term positive effect on abstinence that tapers off after week 9. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of rural-urban residence on children with obstructive sleep-disordered breathing (SDB) who were candidates for tonsillectomy with or without adenoidectomy (TA). Study Design: Retrospective cohort study. Setting: Tertiary children's hospital. Methods: A cohort of otherwise healthy children aged 2 to 18 years with a diagnosis of obstructive SDB between April 2016 and December 2018 who were recommended TA were included. Rural-urban designation was defined by ZIP code approximation of rural-urban commuting area codes. The main outcome was association of rurality with time to TA and loss to follow-up using Cox and logistic regression analyses. Results: In total, 213 patients were included (mean age 6 ± 2.9 years, 117 [55%] male, 69 [32%] rural dwelling). Rural-dwelling children were more often insured by Medicaid than private insurance (P <.001) and had a median driving distance of 74.8 vs 16.8 miles (P <.001) compared to urban-dwelling patients. The majority (94.9%) eventually underwent recommended TA once evaluated by an otolaryngologist. Multivariable logistic regression analysis did not reveal any significant predictors for loss to follow-up in receiving TA. Cox regression analysis that adjusted for age, sex, insurance, and race showed that rural-dwelling patients had a 30% reduction in receipt of TA over time as compared to urban-dwelling patients (hazard ratio, 0.7; 95% CI, 0.50-0.99). Conclusion: Rural-dwelling patients experienced longer wait times and driving distance to TA. This study suggests that rurality should be considered a potential barrier to surgical intervention and highlights the need to further investigate geographic access as an important determinant of care in pediatric SDB. [ABSTRACT FROM AUTHOR]