eprintid: 10113418
rev_number: 15
eprint_status: archive
userid: 608
dir: disk0/10/11/34/18
datestamp: 2020-10-29 14:49:35
lastmod: 2021-10-10 23:20:22
status_changed: 2020-10-29 14:49:35
type: article
metadata_visibility: show
creators_name: Baio, G
title: Survhe: Survival analysis for health economic evaluation and cost-effectiveness modeling
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Survival analysis, health economic evaluation, probabilistic sensitivity analysis, R.
note: This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
abstract: Survival analysis features heavily as an important part of health economic evaluation, an increasingly important component of medical research. In this setting, it is important to estimate the mean time to the survival endpoint using limited information (typically from randomized trials) and thus it is useful to consider parametric survival models. In this paper, we review the features of the R package survHE, specifically designed to wrap several tools to perform survival analysis for economic evaluation. In particular, survHE embeds both a standard, frequentist analysis (through the R package flexsurv) and a Bayesian approach, based on Hamiltonian Monte Carlo (via the R package rstan) or integrated nested Laplace approximation (with the R package INLA). Using this composite approach, we obtain maximum flexibility and are able to pre-compile a wide range of parametric models, with a view of simplifying the modelers’ work and allowing them to move away from non-optimal work flows, including spreadsheets (e.g., Microsoft Excel).
date: 2020-10
date_type: published
official_url: http://dx.doi.org/10.18637/jss.v095.i14
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1824154
doi: 10.18637/jss.v095.i14
lyricists_name: Baio, Gianluca
lyricists_id: GBAIO87
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Journal of Statistical Software
volume: 95
number: 14
citation:        Baio, G;      (2020)    Survhe: Survival analysis for health economic evaluation and cost-effectiveness modeling.                   Journal of Statistical Software , 95  (14)      10.18637/jss.v095.i14 <https://doi.org/10.18637/jss.v095.i14>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10113418/1/v95i14.pdf