Flexible semiparametric mode regression for time-to-event data. (December 2022)

• Record Type: Journal Article
• Authors: Seipp, Alexander; Uslar, Verena; Weyhe, Dirk; Timmer, Antje; Otto-Sobotka, Fabian
• Is Part Of: Statistical methods in medical research. Volume 31:Number 12(2022)
• Page Start: 2352
• Page End: 2367
• Publication Date: 2022-12
• Subjects: Iteratively weighted least squares -- P-splines -- inverse probability weights -- inverse probability of censoring -- pancreatic cancer; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221122406
• Languages: English
• ISSNs: 0962-2802

Abstract

The distribution of time-to-event outcomes is usually right-skewed. While for symmetric and moderately skewed data the mean and median are appropriate location measures, the mode is preferable for heavily skewed data as it better represents the center of the distribution. Mode regression has been introduced for uncensored data to model the relationship between covariates and the mode of the outcome. Starting from nonparametric kernel density based mode regression, we examine the use of inverse probability of censoring weights to extend mode regression to handle right-censored data. We add a semiparametric predictor to add further flexibility to the model and we construct a pseudo Akaike's information criterion to select the bandwidth and smoothing parameters. We use simulations to evaluate the performance of our proposed approach. We demonstrate the benefit of adding mode regression to one's toolbox for analyzing survival data on a pancreatic cancer data set from a prospectively maintained cancer registry.