# Semiparametric Regression for Discrete Time-to-Event Data

## TL;DR

This paper introduces semiparametric regression methods for analyzing discrete or grouped time-to-event data, emphasizing their implementation with open source software and illustrating with unemployment duration data.

## Contribution

It presents novel semiparametric extensions, including smooth nonlinear and tree-based methods, for discrete time-to-event regression analysis.

## Key findings

- Effective application of semiparametric models to unemployment data
- Demonstration of open source software for these methods
- Comparison of different regression approaches

## Abstract

Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here we focus on the analysis of time-to-event outcomes that are either intrisically discrete or grouped versions of continuous event times. In the literature, there exists a variety of regression methods for such data. This tutorial provides an introduction to how these models can be applied using open source statistical software. In particular, we consider semiparametric extensions comprising the use of smooth nonlinear functions and tree-based methods. All methods are illustrated by data on the duration of unemployment of U.S. citizens.