Department of Behavioural Science and Health, University College London, London, UK.
Addiction. 2019 May;114(5):787-797. doi: 10.1111/add.14549. Epub 2019 Jan 29.
It is useful, for theoretical and practical reasons, to be able to specify functions for continuous abstinence over time in smoking cessation attempts. This study aimed to find the best-fitting models of mean proportion abstinent with different smoking cessation pharmacotherapies up to 52 weeks from the quit date.
We searched the Cochrane Database of Systematic Reviews to identify randomized controlled trials (RCTs) of pharmacological treatments to aid smoking cessation. For comparability, we selected trials that provided 12 weeks of treatment. Continuous abstinence rates for each treatment at each follow-up point in trials were extracted along with methodological details of the trial. Data points for each pharmacotherapy at each follow-up point were aggregated where the total across contributing studies included at least 1000 participants per data point. Continuous abstinence curves were modelled using a range of different functions from the quit date to 52-week follow-up. Models were compared for fit using R and Bayesian information criterion (BIC).
Studies meeting our selection criteria covered three pharmacotherapies [varenicline, nicotine replacement therapy (NRT) and bupropion] and placebo. Power functions provided the best fit (R > 0.99, BIC < 17.0) to continuous abstinence curves from the target quit date in all cases except for varenicline, where a logarithmic function described the curve best (R = 0.99, BIC = 21.2). At 52 weeks, abstinence rates were 22.5% (23.0% modelled) for varenicline, 16.7% (16.0% modelled) for bupropion, 13.0% (12.4% modelled) for NRT and 8.3% (8.9% modelled) for placebo. For varenicline, bupropion, NRT and placebo, respectively, 55.9, 65.0, 62.3 and 56.5% of participants who were abstinent at the end of treatment were still abstinent at 52 weeks.
Mean continuous abstinence rates up to 52 weeks from initiation of smoking cessation attempts in clinical trials can be modelled using simple power functions for placebo, nicotine replacement therapy and bupropion and a logarithmic function for varenicline. This allows accurate prediction of abstinence rates from any time point to any other time point up to 52 weeks.
能够指定戒烟尝试中随时间连续戒烟的功能，这在理论和实践上都是有用的。本研究旨在找到最佳拟合模型，以衡量不同戒烟药物治疗至戒烟日期后 52 周的平均持续戒烟率。
我们检索了 Cochrane 系统评价数据库，以确定辅助戒烟的药物治疗随机对照试验（RCT）。为了可比性，我们选择了提供 12 周治疗的试验。从试验中提取每个治疗在每个随访点的连续戒烟率以及试验的方法学细节。在每个随访点，对每种药物治疗的数据点进行了汇总，只要参与研究的总人数至少为 1000 人/数据点。使用从戒烟日期到 52 周随访的一系列不同函数对连续戒烟曲线进行建模。使用 R 和贝叶斯信息准则（BIC）比较模型的拟合度。
符合我们选择标准的研究涵盖了三种药物治疗方法[伐尼克兰、尼古丁替代疗法（NRT）和安非他酮]和安慰剂。除了伐尼克兰，幂函数在所有情况下都能提供最佳拟合（R>0.99，BIC<17.0），对数函数能最好地描述从目标戒烟日期开始的连续戒烟曲线（R=0.99，BIC=21.2）。在 52 周时，戒烟率分别为：伐尼克兰 22.5%（23.0%建模），安非他酮 16.7%（16.0%建模），NRT 13.0%（12.4%建模），安慰剂 8.3%（8.9%建模）。分别为，在治疗结束时持续戒烟的参与者中，有 55.9%、65.0%、62.3%和 56.5%的参与者在 52 周时仍在戒烟。
临床试验中，从戒烟开始至 52 周，使用简单的幂函数对安慰剂、尼古丁替代疗法和安非他酮进行建模，对数函数对伐尼克兰进行建模，可以模拟平均持续戒烟率。这允许从任何时间点准确预测到 52 周的戒烟率。
