One size fits all: A generalized algorithm to fitting GLMs with censored predictors in R

Ever since the discovery of therapies that target the genetic root cause of Huntington disease, researchers have worked to test if these therapies can slow or halt the disease symptoms. A first step towards achieving this is modeling how symptoms progress to know when the best time is to initiate a therapy. Because symptoms are most detectable before and after a clinical diagnosis, modeling how symptoms progress has been problematic since the time to clinical diagnosis is often censored (i.e., for patients who have not yet been diagnosed). This creates a pressing statistical challenge for modeling how symptoms (the outcome) change before and after time to clinical diagnosis (a censored predictor). Strategies to tackle this challenge include fitting a generalized linear model with a censored covariate using maximum likelihood estimation. Still, implementation of these models can be taxing because each new setting (i.e., different outcome models and distributions for the censored predictor) requires a new algorithm to be derived. To this end, we have created the glmCensRd package, which includes generalized linear model fitting functions for a multitude of outcome and (censored) predictor specifications and various random and non-random censoring types. The glmCensRd package makes fitting generalized linear models in R as accessible with censored predictors as without. We provide multiple intuitive examples and demonstrate its impact in fitting a variety of clinically meaningful models from data that are currently being used to design clinical trials for Huntington disease.

Huntington disease

Limit of detection

Maximum likelihood estimation

Random censoring

R package

Survival analysis 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022