A placebo-controlled randomized trial of D-cycloserine augmentation of cue exposure therapy for smoking cessation

Recent studies underscore the importance of studying d-cycloserine (DCS) augmentation under conditions of adequate cue exposure treatment (CET) and protection from reconditioning experiences. In this randomized trial, we evaluated the efficacy of DCS for augmenting CET for smoking cessation under these conditions.

Sixty-two smokers attained at least 18 hours abstinence following 4 weeks of smoking cessation treatment and were randomly assigned to receive a single dose of DCS (n=30) or placebo (n=32) prior to each of two sessions of CET. Mechanistic outcomes were self-reported cravings and physiologic reactivity to smoking cues. The primary clinical outcome was 6-week, biochemically-verified, continuous tobacco abstinence.

DCS, relative to placebo, augmentation of CET resulted in lower self-reported craving to smoking pictorial and in vivo cues (d = 0.8 to 1.21) in a relevant subsample of participants who were reactive to cues and free from smoking-related reconditioning experiences. Select craving outcomes were correlated with smoking abstinence, and DCS augmentation was associated with a trend toward a higher continuous abstinence rate (33% vs. 13% for placebo augmentation).

DCS augmentation of CET can significantly reduce cue-induced craving, supporting the therapeutic potential of DCS augmentation when applied under appropriate conditions for adequate extinction learning.     

Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.