Wednesday, February 5, 2025
2/5/2025
Project Summary Among neurological disorders, the fastest growing is now Parkinson's disease (PD), surpassing Alzheimer's dis- ease. PD manifests as a heterogeneous clinical syndrome and this variability in the clinical phenotype highlights the need to tailor the type and/or the dosage of treatment to the specific and changing needs of individuals living with PD. The main goal of individualized, or precision, medicine is to use patient characteristics to determine an individualized treatment strategy (ITS) to promote wellness. Due to the complex nature of PD coupled with phenotypic heterogeneity, formulating successful individualized approaches to medical care is a complex prob- lem that may benefit from a more data-driven approach. One of the challenges in developing reliable ITSs is that the analyses require studies with fairly large sample sizes and longitudinal assessment of subjects over a relatively long period of time. The data set must also include various prescribing patterns to allow the analytic method to learn the effects of different treatment sequences (strategies). These important requirements preclude investigators from using data from a single clinical study to construct data-driven ITSs. Existing guidelines for symptomatic drug therapy for PD can best be described as "permissive". The relative lack of comparative evidence for different classes of drugs has created challenges in devising recommendations to follow any specific therapeutic strategy. We fill this important gap by proposing a two phase study. The first phase (R61) focuses on creating a harmonized and curated dataset by integrating data from six clinical trials and the PPMI observational study that, in aggregate, involved 4,705 patients followed from 23.5 to 96 months. To the best of our knowledge, such comprehensive data harmonization has not been done before in PD and it can provide an excellent source of information for future studies as well. In the second phase (R33), we will leverage the harmonized data set to develop high quality ITSs for PD with respect to several clinical outcomes including UPDRS score, quality of life, and Schwab and England (SE) ADL measured at 24 and 48 months of follow-up. Specifically, the goals of the R33 phase are to (Aim 1) compare commonly used sequences of drug classes for PD; (Aim 2) identify the best individualized treatment strategies to inform optimal sequences of drug classes for PD. In pursuit of these aims, we will propose robust, rigorous and computationally efficient statistical machine learning methods for constructing data-driven optimal ITSs for PD. The proposal expands the scope of existing methods in developing ITSs by relaxing certain unrealistic assumptions and through the use of flexible modeling techniques (e.g., machine learning methods) while maintaining valid statistical inference. These new methods will be integrated into easy-to-use, publicly available software in the R language (Aim 3). This will maximize the adoption of the proposed methodology by other investigators and allow researchers to analyze other PD datasets with a goal of constructing an ITS for PD. Furthermore, because the methods are not disease-specific, our methods and software will enable similar exploration for other diseases.
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