An Improved Pooled Logistic Regression Implementation

Pooled logistic regression is a popular tool for survival analyses in epidemiology, but can face computational challenges. Commonly, these challenges are addressed through widening time intervals or using a parametric functional form for time. We propose a third option to reduce the computational burden without constraining the functional form for time.

The proposed algorithm operates by restricting the long data set to rows that correspond to unique event times. However, our approach is only compatible when modeling time most flexibly with disjoint indicators. We compared the standard implementation to the proposed implementation in SAS, R, and Python using a publicly available data set.

For the example considered, both implementations provided the same point estimates, but the proposed implementation was between 6 and 68 times faster depending on the software.

The proposed implementation can greatly simplify estimation of pooled logistic regression models, which is especially important when relying on the bootstrap for inference.