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