Statistical Analysis of Randomized Trials in Tobacco Treatment
Department of Psychiatry, University of California, San Francisco, 94143, USA.Nicotine & Tobacco Research (Impact Factor: 3.3). 09/2001; 3(3):193-202. DOI: 10.1080/14622200110050411
This article considers two important issues in the statistical treatment of data from tobacco-treatment clinical trials: (1) data analysis strategies for longitudinal studies and (2) treatment of missing data. With respect to data analysis strategies, methods are classified as 'time-naïve' or longitudinal. Time-naïve methods include tests of proportions and logistic regression. Longitudinal methods include Generalized Estimating Equations and Generalized Linear Mixed Models. It is concluded that, despite some advantages accruing to 'time-naïve' methods, in most situations, longitudinal methods are preferable. Longitudinal methods allow direct effects of the tests of time and the interaction of treatment with time, and allow model estimates based on all available data. The discussion of missing data strategies examines problems accruing to complete-case analysis, last observation carried forward, mean substitution approaches, and coding participants with missing data as using tobacco. Distinctions between different cases of missing data are reviewed. It is concluded that optimal missing data analysis strategies include a careful description of reasons for data being missing, along with use of either pattern mixture or selection modeling. A standardized method for reporting missing data is proposed. Reference and software programs for both data analysis strategies and handling of missing data are presented.
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- "After the treatment, there was face-to-face follow-up at 6 months. An intent-to-treat model was adopted when it was not possible to locate the participants (Hall et al., 2001). They were considered to be smokers at the same level (in terms of number of cigarettes and nicotine content) as in the pretreatment assessment. "
ABSTRACT: Introduction: Although quitting motivation predicts smoking cessation, there have been inconsistent findings regarding motivation predicting long-term maintenance of abstinence. Moreover, most such research has been conducted in North America and the United Kingdom. The aim of this study was to examine motivation to quit as a predictor of smoking cessation and of abstinence maintenance in a Spanish sample. Method: The sample comprised 286 Spanish smokers undergoing psychological treatment for smoking cessation. Motivation to quit was assessed pre-treatment and post-treatment with the Readiness to Quit Ladder. Abstinence post-treatment and at 6month follow-up was biochemically verified. Results: Participants with higher levels of pre-treatment and post-treatment motivation were more likely to be abstinent at the end of the treatment (OR=1.36) and at 6month follow-up (OR=4.88). Among abstainers at the end of the treatment (61.9%), higher levels of motivation to quit post-treatment predicted maintaining abstinence at 6months (OR=2.83). Furthermore, participants who failed to quit smoking reported higher levels of motivation to quit post-treatment than they had pretreatment (p<.001). Conclusions: Motivation to quit smoking predicted short and long-term cessation, and also predicted long-term maintenance of abstinence. These results have implications for understanding motivational processes of smoking cessation in general, while extending research to Spanish smokers. They may also help in the design of cessation and relapse-prevention interventions. Specifically, the results suggest that motivational enhancement is important throughout the cessation and maintenance periods.Addictive behaviors 02/2016; 53:40-45. DOI:10.1016/j.addbeh.2015.09.017 · 2.76 Impact Factor
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- "For our primary hypothesis test, we assume participants lost to follow-up are smokers. Depending on actual follow-up rates achieved, the results may be presented using recommended new strategies  to deal with missing data. Characteristics associated with nonresponse in smokers are used with SOLAS and SAS programs for implementing multiple imputation procedures. "
ABSTRACT: Background Extended smoking cessation follow-up after hospital discharge significantly increases abstinence. Hospital smoke-free policies create a period of ‘forced abstinence’ for smokers, thus providing an opportunity to integrate tobacco dependence treatment, and to support post-discharge maintenance of hospital-acquired abstinence. This study is funded by the National Heart, Lung, and Blood Institute (1U01HL1053231). Methods/Design The Inpatient Technology-Supported Assisted Referral study is a multi-center, randomized clinical effectiveness trial being conducted at Kaiser Permanente Northwest (KPNW) and at Oregon Health & Science University (OHSU) hospitals in Portland, Oregon. The study assesses the effectiveness and cost-effectiveness of linking a practical inpatient assisted referral to outpatient cessation services plus interactive voice recognition (AR + IVR) follow-up calls, compared to usual care inpatient counseling (UC). In November 2011, we began recruiting 900 hospital patients age ≥18 years who smoked ≥1 cigarettes in the past 30 days, willing to remain abstinent postdischarge, have a working phone, live within 50 miles of the hospital, speak English, and have no health-related barriers to participation. Each site will randomize 450 patients to AR + IVR or UC using a 2:1 assignment strategy. Participants in the AR + IVR arm will receive a brief inpatient cessation consult plus a referral to available outpatient cessation programs and medications, and four IVR follow-up calls over seven weeks postdischarge. Participants do not have to accept the referral. At KPNW, UC participants will receive brief inpatient counseling and encouragement to self-enroll in available outpatient services. The primary outcome is self-reported thirty-day smoking abstinence at six months postrandomization for AR + IVR participants compared to usual care. Additional outcomes include self-reported and biochemically confirmed seven-day abstinence at six months, self-reported seven-day, thirty-day, and continuous abstinence at twelve months, intervention dose response at six and twelve months for AR + IVR recipients, incremental cost-effectiveness of AR + IVR intervention compared to usual care at six and twelve months, and health-care utilization and expenditures at twelve months for AR + IVR recipients compared to UC. Discussion This study will provide important evidence for the effectiveness and cost-effectiveness of linking hospital-based tobacco treatment specialists’ services with discharge follow-up care. Trial Registration ClinicalTrials.gov: NCT01236079Trials 08/2012; 13(1):129. DOI:10.1186/1745-6215-13-129 · 1.73 Impact Factor
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- "To address these concerns, researchers have proposed to use generalized estimating equations (GEE) and generalized linear mixed-effects models (GLMM) . Both GEE and GLMM use repeated outcome measures and take the intra-personal association into account, and provide a means to comparing intervention effects at each time point and to examining whether the effects vary over time . GEE provides population-averaged estimates, and the covariate effects can be interpreted as in standard logistic regression models. "
ABSTRACT: Background: GEE and mixed models are powerful tools to compare treatment effects in longitudinal smoking cessation trials. However, they are not capable of assessing the relapse (from abstinent back to smoking) simultaneously with cessation, which can be studied by transition models. Methods: We apply a first-order Markov chain model to analyze the transition of smoking status measured every 6 months in a 2-year randomized smoking cessation trial, and to identify what factors are associated with the transition from smoking to abstinent and from abstinent to smoking. Missing values due to non-response are assumed non-ignorable and handled by the selection modeling approach. Results: Smokers receiving high-intensity disease management (HDM), of male gender, lower daily cigarette consumption, higher motivation and confidence to quit, and having serious attempts to quit were more likely to become abstinent (OR = 1.48, 1.66, 1.03, 1.15, 1.09 and 1.34, respectively) in the next 6 months. Among those who were abstinent, lower income and stronger nicotine dependence (OR = 1.72 for ≤ vs. > 40 K and OR = 1.75 for first cigarette ≤ vs. > 5 min) were more likely to have relapse in the next 6 months. Conclusions: Markov chain models allow investigation of dynamic smoking-abstinence behavior and suggest that relapse is influenced by different factors than cessation. The knowledge of treatments and covariates in transitions in both directions may provide guidance for designing more effective interventions on smoking cessation and relapse prevention.BMC Medical Research Methodology 07/2012; 12(1):95. DOI:10.1186/1471-2288-12-95 · 2.27 Impact Factor