Predictive Adherence Modeling (PAM) Study

临床医生和患者的一个主要问题是患者依从性。在睡眠医学领域， 阻塞性睡眠呼吸暂停（OSA）患者对这种疾病的金标准治疗的依从性各不相同： 持续气道正压通气（CPAP）治疗。拟议的预测依从性建模（PAM）研究 将使用两个大型OSA数据集[NHLBI支持的呼吸暂停正压长期有效性研究 （APPLES）和AHRQ支持的电子数据技术比较成果管理 （COMET）研究]和三个NIDA支持的数据集，以实现三个具体目标：（1）构建一个 用于推导依从性预后定义的基于校准的通用方法。我们的目标是开发这个 持续气道正压通气治疗阻塞性睡眠呼吸暂停综合征 作为试验台。(2)开发一个依从性的预测模型。持续的依从性测量（例如，是说 每晚的坚持时间），以便结果保持完整的分辨率和最高的信息 内容，这最大限度地提高了预测模型区分不同行为患者的机会。 依从性也将作为一个多变量的结果和预测建模方法， 将使用多元结果，除了现代正规化的方法，将允许筛选， 候选预测因子的广泛列表。该项目将包括专门设计的方法， 预测的相互作用，如回归树，我们将允许非线性预测，通过使用各种 样条基扩展、基于树的方法和神经网络技术。将采用包围方法， 例如boosting，其中拟合许多不同的回归模型，然后将其组合以利用其 预测结果的集体能力，并且将通过使用验证来纠正过拟合 技术.使用这些方法将使团队能够识别更强大的预测模型， 预测性能将在其他数据集中得到维持。此外，将组合前述技术 以便构建优化粘附预测的模型。最后，现有的统计方法将 扩展并适应于粘附预测的具体问题，开发新的统计技术 根据需要(3)建立一套统计工具，以促进开发依从性预测模型 在任何医学领域。计划是开发一套统计工具，以促进 任何医学领域的依从性预测模型，其中包括三个基本要素： 以非统计学家医学人员能够理解的语言描述该套统计方法 专业人士(b)将为这套统计工具提供一套方便用户的代码。此代码 将以两种语言提供，SAS®和免费软件R。(c)该代码将包括一些可视化 工具，以促进解释和利用预测模型的临床从业者。

A major problem for both clinicians and patients is patient adherence. In the field of sleep medicine, patients with obstructive sleep apnea (OSA) have variable adherence to the gold standard treatment for this condition: continuous positive airway pressure (CPAP) therapy. The proposed Predictive Adherence Modeling (PAM) Study will use two large OSA datasets [the NHLBI-supported Apnea Positive Pressure Long-term Efficacy Study (APPLES) and the AHRQ-supported Comparative Outcomes Management with Electronic Data Technology (COMET) Study] and three NIDA-supported datasets, to accomplish three specific aims: (1) To construct a general, calibration-based approach for deriving prognostic definitions of adherence. The goal is to develop this approach by using adherence to continuous positive airway pressure for patients with obstructive sleep apnea as a testbed. (2) To develop a predictive model for adherence. Continuous measures of adherence (e.g., mean hours of adherence per night), will be used so that the outcome is kept at full resolution and highest information content, which maximizes opportunities for predictive models to distinguish among patients of differing behaviors. Adherence will also be operationalized as a multivariate outcome and predictive-modeling methods for multivariate outcomes will be used, in addition to modern regularized methods that will allow sifting through extensive lists of candidate predictors. The project will include methods that are specially designed to explore predictive interactions, such as regression trees, and we will allow for nonlinear predictors through use of various spline basis expansions, tree-based methods, and neural net technology. Ensemble methods will be employed, such as boosting, wherein many different regression models are fit and then combined to capitalize on their collective ability to predict outcome, and there will be correction for overfitting through use of validation techniques. Using these methods will allow the team to identify predictive models that are more robust, in that predictive performance will be sustained in other data sets. Further, the preceding techniques will be combined in order to construct models that optimize prediction of adherence. Finally, existing statistical methodology will be extended and adapted to the specific problem of adherence prediction, developing new statistical technology as needed. (3) To build a suite of statistical tools that will facilitate development of predictive models of adherence in any field of medicine. The plan is to develop a suite of statistical tools that will facilitate development of predictive models of adherence in any field of medicine, which will include three essential elements: (a) A description of the statistical methods contained within the suite in language accessible to non-statistician medical professionals. (b) A user-friendly package of code will be provided for the suite of statistical tools. This code will be provided in two languages, SAS® and the freeware R. (c) The code will include a number of visualization tools to facilitate interpretation and utilization of predictive models by clinical practitioners.