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Updated: Feb 8, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Expected value of sample information for Weibull survival data.

Alan Brennan¹, Samer A Kharroubi

¹School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, Yorkshire, UK. a.brennan@sheffield.ac.uk

Health Economics

|March 1, 2007

Summary

Calculating the expected value of sample information (EVSI) is streamlined by new Bayesian approximation methods. The Brennan & Kharroubi method accurately approximates Weibull model benefits, significantly reducing computation time compared to MCMC.

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Area of Science:

Health economics
Biostatistics
Decision analysis

Background:

Expected value of sample information (EVSI) analysis is crucial for optimizing data collection strategies.
Bayesian updating in Weibull models traditionally relies on computationally intensive Markov chain Monte Carlo (MCMC) methods.

Purpose of the Study:

To evaluate and compare five distinct methods for calculating posterior expected net benefits in Weibull models.
To assess the accuracy, computational efficiency, and trade-offs of different EVSI calculation approaches.

Main Methods:

Comparison of two heuristic methods (data lumping, pseudo-normal) and two Bayesian approximation methods (Tierney & Kadane, Brennan & Kharroubi) against MCMC.
Application of these methods in a case study to compute EVSI for 25 distinct study options.

Main Results:

The Brennan & Kharroubi (B&K) method achieved accuracy within +/-1% of MCMC for expected net benefits.
B&K demonstrated superior accuracy in EVSI approximation, with pseudo-normal also showing reasonable performance.
B&K offered a significant computational speed-up (12x faster in the case study) compared to MCMC.

Conclusions:

The Brennan & Kharroubi method provides a computationally efficient and accurate alternative to MCMC for EVSI calculations in Weibull models.
The evaluated methods facilitate EVSI computation for economic models incorporating Weibull survival data and can be extended to other parametric survival distributions and complex models.