A novel data-driven approach for personalizing smoking cessation pharmacotherapy

项目摘要 吸烟占癌症死亡人数的三分之一。大约14%的美国成年人 都是现在的吸烟者。尽管有几家食品和药物管理局(FDA)批准了戒烟 存在药物疗法[例如，伐伦尼克林、安非他酮、尼古丁替代疗法(NRT)]、使用率 仍然很低，很大一部分吸烟者对现有的治疗方法没有反应。个性化治疗 为吸烟者提供戒烟药物治疗的建议 个体特征可能会提高FDA批准的戒烟药物疗法的利用率 并在吸烟者中戒烟成功。我们的目标是开发一种基于人口统计学和临床数据的算法 在治疗前进行评估，以估计吸烟者对FDA批准的药物疗法的可能反应 用于戒烟，包括伐伦尼克林、安非他酮和尼古丁替代疗法(NRT)。模特们将 说明药物不良反应和不坚持服药的可能性。个人对治疗的估计 将通过现有的复杂分析建模(例如，机器学习技术)获得响应 数据来自一项单一的大规模随机对照试验(由辉瑞和 葛兰素史克，美国样本，N=4207)。Eagles试验提供了丰富的数据集，比较了 FDA批准的药物在一个具有临床代表性的大样本中面对面。在老鹰队的审判中， 受试者被随机分配给瓦伦尼克林(每天两次，1毫克)，安非他酮(150毫克，每天两次)， NRT贴片(每天21毫克，渐进式)，或安慰剂胶囊/贴片，疗程12周。戒烟 测量第9至12周的结果。我们建议使用多种统计技术(例如， 机器学习)来优化预测个人特定戒烟可能性的模型 对每一种治疗的反应都是成功的。与Eagles试验中的主要分析一致，我们将定义 一氧化碳治疗成功-证实在9到12周内持续戒烟。 其次，我们还将检查第9至24周的持续禁欲情况。我们会培养出一位病人 和面向提供商的移动应用原型，实现最佳匹配算法并前瞻性预测 新患者通过各种药物治疗戒烟的可能性。这款移动应用程序将允许新的 患者根据最终模型中被认为相关的预测因素完成一组简化的评估。 APP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，以促进统计学家实施类似的模型 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.