A novel data-driven approach for personalizing smoking cessation pharmacotherapy

项目摘要 吸烟导致三分之一的癌症死亡。美国大约14%的成年人 现在的吸烟者。虽然一些食品和药物管理局（FDA）批准的戒烟 存在药物疗法[例如，伐尼克兰，安非他酮，尼古丁替代疗法（NRT）]，利用率 吸烟率仍然很低，很大一部分吸烟者对现有的治疗没有反应。个性化的治疗 建议吸烟者根据他们的吸烟情况提供戒烟药物治疗。 个体特征可以提高FDA批准的戒烟药物治疗的利用率 成功戒烟的人。我们的目标是开发一种算法，基于人口统计学和临床数据 在治疗前进行评估，以估计个体吸烟者对FDA批准的药物治疗的可能反应 用于戒烟，包括伐尼克兰、安非他酮和尼古丁替代疗法（NRT）。车型将 说明药物不良反应和不依从的可能性。治疗的个体估计值 响应将通过复杂的分析建模（例如，机器学习技术） 数据来自一项单一的大规模随机对照试验（EAGLES试验由辉瑞公司进行， GlaxoSmithKline，美国样本，N = 4207）。EAGLES试验提供了一个丰富的数据集， FDA批准的药物在一个大的和临床代表性的样本头对头。在EAGLES试验中， 参与者被随机分配接受伐尼克兰（1mg，每日两次），安非他酮（150mg，每日两次）， NRT贴剂（21 mg/天，逐渐减少）或安慰剂药丸胶囊/贴剂，持续12周。戒烟 测量第9周至第12周的结果。我们建议使用多种统计技术（例如， 机器学习）来优化用于预测个体特定戒烟的可能性的模型 成功应对每一次治疗。与EAGLES试验中的主要分析一致，我们将定义 治疗成功，因为一氧化碳证实在第9周至第12周期间持续戒酒。 其次，我们还将检查第9周至第24周期间的持续禁欲。我们会培养一个病人 和面向提供商的移动的应用原型，实现最佳拟合算法并前瞻性地预测 新患者通过各种药物治疗戒烟的可能性。该移动的应用程序将允许一个新的 患者根据最终模型中认为相关的预测因子完成一组简化的评估。 应用程序原型的开发将使我们能够在未来的研究中完成用户测试和改进。 最后，我们将开发一个R包，以方便统计人员使用类似的模型来实现。 其他疾病数据。

PROJECT ABSTRACT Cigarette smoking contributes to one-third of cancer deaths. Approximately 14% of adults in the United States are current tobacco smokers. Though several Food and Drug Administration (FDA)-approved smoking cessation pharmacotherapies exist [e.g., varenicline, bupropion, nicotine replacement therapy (NRT)], utilization rates remain low and a substantial portion of smokers do not respond to existing treatments. A personalized treatment recommendation in which smokers are provided with a smoking cessation pharmacotherapy based on their individual characteristics may improve both utilization of FDA-approved smoking cessation pharmacotherapies and quit success among smokers. Our goal is to develop an algorithm, based on demographic and clinical data assessed prior to treatment, to estimate individual smokers' likely response to FDA-approved pharmacotherapies for smoking cessation, including varenicline, bupropion, and nicotine replacement therapy (NRT). Models will account for the likelihood of adverse effects of medication and non-adherence. Individual estimates of treatment response will be obtained through sophisticated analytic modeling (e.g., machine learning techniques) of existing data from a single, large-scale randomized controlled trial (EAGLES trial conducted by Pfizer and GlaxoSmithKline, United States sample, N=4207). The EAGLES trial provides a rich dataset comparing three FDA-approved medications head-to-head in a large and clinically representative sample. In the EAGLES trial, participants were randomly assigned to receive varenicline (1 mg twice daily), bupropion (150 mg twice daily), NRT patch (21 mg per day with taper), or placebo pill capsules/patches for 12 weeks. Smoking cessation outcomes at weeks 9 through 12 were measured. We propose to use multiple statistical techniques (e.g., machine learning) to optimize a model for predicting an individual's likelihood of specific smoking cessation success in response to each treatment. Consistent with the primary analyses in the EAGLES trial, we will define treatment success as carbon monoxide-confirmed continuous abstinence during weeks 9 through 12. Secondarily, we will also examine continuous abstinence during weeks 9 through 24. We will develop a patient and provider-facing mobile app prototype that implements the best-fitting algorithm and prospectively predicts new patients' likelihood of smoking cessation with various pharmacotherapies. The mobile app will allow a new patient to complete a reduced set of assessments based on the predictors deemed relevant in the final model. The development of an app prototype will position us to complete user testing and refinement in a future study. Finally, we will develop a R package to facilitate implementation of similar models by statisticians working with other disease data.