UCLA

The emerging observational health data, such as electronic health records and administrative claims, provide a rich resource for learning about treatment effects and risks. However, computational challenges arise when fitting statistical models to such large-scale and high-dimensional data. In this dissertation, I employ parallel computing techniques to address the computational bottlenecks associated widely used statistical models in observational studies. First, I present a novel parallel scan algorithm to scale up the Cox proportional hazards model and the Fine-Gray model. This advancement significantly accelerates the execution of large-scale comparative effectiveness and safety studies involving millions of patients and thousands of patient characteristics by an order of magnitude. Second, I apply an efficient parallel segmented-scan algorithm to accelerate the computational intensive parts shared by the stratified Cox model, the Cox model with time-varying covariates, and the Cox model with time-varying coefficients. This innovation enables efficient large-scale and high-dimensional Cox modeling with stratification or time-varying effect, delivering an order of magnitude speedup over traditional central processing unit-based methods. Third, I introduce a memory-efficient approach for fitting pooled logistic regression models with massive sample-size data. This approach offers a valuable tool, allowing for pooled logistic regression analysis on massive sample sizes, even when computational resources are limited. I have implemented all of the above work in the open-source R package Cyclops.