Healthcare center clustering for Cox's proportional hazards model by fusion penalty

There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data-driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry.

Automated summary

There is a growing research interest in evaluating the performance of healthcare centers based on patient outcomes. Conventional assessments can be done using fixed or random effects models, like provider profiling. A new method is proposed using fusion penalty to cluster healthcare centers based on survival outcomes, providing a data-driven approach for grouping without prior knowledge. An efficient algorithm is developed to implement the proposed method, and its validity is demonstrated through simulation studies and application to kidney transplant registry data.