This study assessed the effect of treating nicotine dependence in smokers undergoing inpatient treatment for other addictions. It was a prospective, nonrandomized, controlled trial with a 1-year outcome. The subjects were smoking patients (50 controls, 51 in intervention group) in an inpatient addictions treatment unit in a medical center. The enrollment of subjects was sequential: controls were enrolled first; after a 6-week washout period, intervention subjects were enrolled. Controls received usual care, and the intervention group received nicotine dependence treatment consisting of a consultation, 10 intervention sessions, and a structured relapse prevention program. Smoking cessation rate and abstinence from alcohol or other drug use were the main outcome measures. The confirmed smoking cessation rate at 1 year was 11.8% in the intervention group and 0.0% in the control group (p = 0.027). Nicotine dependence intervention did not seen to interfere with abstinence from alcohol or other drugs (1-year relapse rate was 31.4% in the intervention group and 34.0% in controls). In this study, nicotine dependence treatment provided as part of addictive disorders treatment enhanced smoking cessation and did not have a substantial adverse effect on abstinence from the nonnicotine drug of dependence.

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Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the notion of threshold, which may have relevance for many of the variables that are of interest to investigators in the field of personality. Furthermore, investigators with experience in multiple linear regression or contingency table analysis should have little trouble in transitioning to logistic regression. Logistic regression programs are readily available in the major statistical packages, all of which provide fairly standard output.