Survival Data Analysis using Average Hazard

Performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed by Uno and Horiguchi (2023) .

survAH: Survival Data Analysis using Average Hazard

The survAH package performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR).

Installation

To install the package from the CRAN:

install.packages("survAH")

To install the most updated version:

devtools::install_github("uno1lab/survAH")

Usage

Please see the package vignette on the website https://www.uno1lab.com/survAH/

Reference

Uno H and Horiguchi M. Ratio and difference of average hazard with survival weight: new measures to quantify survival benefit of new therapy. Statistics in Medicine. 2023;42(7):936-952. https://doi.org/10.1002/sim.9651

Uno H, Tian L, Horiguchi M, Hattori S, Kehl KL. Regression models for average hazard. Biometrics. 2024; 80(2):ujae037. https://doi.org/10.1093/biomtc/ujae037

Horiguchi M, Tian L, Kehl KL, Uno H. Assessing delayed treatment benefits of immunotherapy using long-term average hazard: a novel test/estimation approach. Lifetime Data Anal. 2025; 31(4):784-809. https://doi.org/10.1007/s10985-025-09671-0

Qian Z, Tian L, Horiguchi M, Uno H. A novel stratified analysis method for testing and estimating overall treatment effects on time-to-event outcomes using average hazard with survival weight. Stat Med. 2025; 44(7):e70056. https://doi.org/10.1002/sim.70056