Statistical Methods for Composite Endpoints

Win Ratio and Beyond

Author

Lu Mao

Course Info

This is a companion site for the same-titled workshop at the 2024 Society for Clinical Trials (SCT) Annual Meeting given on May 19, 2024 at Boston Marriott Copley Place (map) in Boston, MA.

Time and Place

  • Sun, May 19 | 8:00 AM - 12:00 PM
  • Room: Suffolk (3rd Floor)

Instructor Profile

Lu Mao, PhD \(\hspace{0.2em}\) X GitHub LinkedIn
  • Associate Professor of Biostatistics at UW-Madison
  • Methodologic research
    • R01HL149875: Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials (12/01/2019 – 07/31/2028)
    • DMS2015526: Randomized Trials with Non-Compliance (07/01/2020 – 06/30/2024)
  • Collaborative research
    • Cardiovascular disease, cancer, radiology, behavioral health interventions
  • Teaching
  • Editorial service

Learning Outcomes

  • Understand the statistical and scientific challenges with composite endpoints as well as regulatory guidelines/requirements
  • Learn the basics of statistical methodology, e.g., testing, power analysis, nonparametric estimation, and semiparametric regression to address these challenges
  • Get hands-on experience with real data using publicly available R-packages

Syllabus

  • 1. Introduction (30 min)
    • 1.1 Examples & guidelines
    • 1.2 Traditional methods and limitations
    • 1.3 Win ratio methods and limitations
  • 2. Hypothesis Testing (40 min)
    • 2.1 Win ratio by Pocock et al. (2012)
    • 2.2 Statistical properties
    • 2.3 Handling recurrent events (R-package WR)
    • 2.4 Sample size calculation (R-package WR)
  • 3. Nonparametric Estimation (60 min)
    • 3.1 Restricted win ratio
    • 3.2 Average win time analysis (R-package rmt)
      • 3.1.1 Restricted mean time in favor of treatment
      • 3.1.2 Estimation, inference, and graphics
      • 3.1.3 Real Examples
    • 3.3 While-alive loss rate (R-package WA)
      • 3.2.1 Definition, interpretation, and estimation
      • 3.2.3 Real Examples
  • 4. Semiparametric Regression (30 min)
    • 4.1 Proportional win-fractions model (R-package WR)
      • 4.1.1 Model assumptions
      • 4.1.2 Estimation, inference, and model diagnostics
      • 4.1.3 Real Examples
    • 4.2 Generalized proportional odds model (tentative)
      • 4.2.1 Model specification and possible estimation
  • 5. Discussions (20 min)
    • 5.1. Open problems
      • 5.1.1 Covariate adjustment
      • 5.1.2 Interim monitoring
      • 5.1.3 Intercurrent events
    • 5.2. Course summary

R-Packages

To proceed, install/load the following packages:

# install packages --------------------------------
install.packages("Wcompo")
install.packages("WR")
install.packages("rmt")
install.packages("WA")
# load packages -----------------------------------
library(tidyverse)
library(survival)
library(Wcompo)
library(WR)
library(rmt)
library(WA)