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.
When a patient is prescribed a medical treatment (e.g., medication, device) by their clinician, a major question is if the patient will adhere to the prescribed treatment. The goal of the proposed project is to develop a model to predict who best adhere to prescribed treatment, since it has been shown that adherence to treatment is related to improvements in quality of life and reduction in the consequences of disease, visits to clinics and hospitals, and health care costs. This project will focus initially on developing this model for predicting adherence to a device used to treat a breathing disorder during sleep (sleep apnea) as a testbed, but statistical tools will be developed so that clinicians in other areas of medicine will be able to develop their own models for predicting adherence in their patients.
| Lee, Chuen Peng; Holmes, Tyson; Neri, Eric et al. (2018) Deception in clinical trials and its impact on recruitment and adherence of study participants. Contemp Clin Trials 72:146-157 |
