Cluster Analysis: CV Example
Issue: A major pharmaceutical client sought to better understand the utilization of anti-hypertensive agents among patients with a diagnosis of hypertension with concomitant diabetes. The client also wanted to know if the utilization patterns that were observed were consistent with current treatment guidelines and to determine where their product fit in the overall treatment landscape. […]
CART Modeling: Oncology Example
Issue: A major pharmaceutical client sought to better understand distinct patient phenotypes that had a high likelihood of experiencing treatment initiation, treatment discontinuation, treatment switching and high total disease specific costs of their target product as well as comparator therapies among patients treated for chronic myeloid leukemia (CML). Solution: We conducted a Classification and Regression […]
CART Modeling: RA Example
Issue: A major pharmaceutical client sought to create a data mining approach that would help them identify target populations of greatest need for their early development products. Solution: We conducted a Classification and Regression Tree (CART) analysis for Rheumatoid Arthritis to identify high risk patients that would inform clinical and market access strategies. Using claims […]
Machine Learning: Peripheral Artery Disease Example
Issue: A pharmaceutical client approached DMS to evaluate whether the use of a specific anti-thrombotic agent was related to secondary prevention of thromboembolic complications associated with symptomatic peripheral artery disease (PAD) or was used as primary prevention of thromboembolic complications after a stent procedure among patients that also had concomitant coronary artery disease (CAD) with […]
Machine Learning: CV Example
Issue: A major pharmaceutical client sought to better characterize the risk strata based on ejection fraction (EF) and associated risk drivers associated with U.S Medicare patients that had decompensated acute heart failure (AHF). Key outcomes of interest were re-admission rates and mortality risk. Solution: We constructed a Classification and Regression Tree (CART) model which is […]
Supervised Machine Learning: Oncology Example
Issue: A major pharmaceutical client sought to better identify drivers associated with early mortality and late survival among patients evaluated in a large multinational RCT of patients with esophageal cancer. Solution: After completing a comprehensive data pre-processing step, we constructed a series of machine learning (ML) predictive models to determine if treatment regimens in study […]
Unsupervised Machine Learning: CV Example
Issue: A major pharmaceutical client sought to better understand the utilization of anti-hypertensive agents among patients with a diagnosis of hypertension with concomitant diabetes. The client also wanted to know if the utilization patterns that were observed were consistent with current treatment guidelines and to determine where their product fit in the overall treatment landscape. […]
