Healthcare provider profiling is of nationwide importance. To improve quality of care and reduce costs for patients, the Centers for Medicare and Medicaid Services (CMS) monitors Medicare-certified healthcare providers (e.g. dialysis facilities, transplant centers and surgeons) nationwide with various quality measures of patient outcomes (e.g. readmission, mortality and hospitalization). This monitoring can help patients make more informed decisions, and can also aid stakeholders and payers in identifying providers where improvement may be needed, and even fining or closing those with extremely poor outcomes. Therefore, it is important that the quality measures for profiling providers be accurate.

A stuctured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model.

The time-varying effects model is a flexible and powerful tool for modeling the dynamic changes of covariate effects. However, in survival analysis, its computational burden increases quickly as the number of sample sizes or predictors grows. Traditional methods that perform well for moderate sample sizes and low-dimensional data do not scale to massive data. We propose a block-wise steepest ascent procedure by leveraging the block structure of parameters inherent from the basis expansions for each coefficient function. The algorithm iteratively updates the optimal block-wise search direction, along which the increment of the partial likelihood is maximized. The proposed method can be interpreted from the perspective of the Minorization-Maximization algorithm and increases the partial likelihood until convergence.

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. Existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches.