Literature DB >> 17191271

Multilevel models for estimating incremental net benefits in multinational studies.

Richard Grieve1, Richard Nixon, Simon G Thompson, John Cairns.   

Abstract

Multilevel models (MLMs) have been recommended for estimating incremental net benefits (INBs) in multicentre cost-effectiveness analysis (CEA). However, these models have assumed that the INBs are exchangeable and that there is a common variance across all centres. This paper examines the plausibility of these assumptions by comparing various MLMs for estimating the mean INB in a multinational CEA. The results showed that the MLMs that assumed the INBs were exchangeable and had a common variance led to incorrect inferences. The MLMs that included covariates to allow for systematic differences across the centres, and estimated different variances in each centre, made more plausible assumptions, fitted the data better and led to more appropriate inferences. We conclude that the validity of assumptions underlying MLMs used in CEA need to be critically evaluated before reliable conclusions can be drawn. Copyright 2006 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2007        PMID: 17191271     DOI: 10.1002/hec.1198

Source DB:  PubMed          Journal:  Health Econ        ISSN: 1057-9230            Impact factor:   3.046


  8 in total

1.  The analysis of multinational cost-effectiveness data for reimbursement decisions: a critical appraisal of recent methodological developments.

Authors:  Andrea Manca; Mark J Sculpher; Ron Goeree
Journal:  Pharmacoeconomics       Date:  2010       Impact factor: 4.981

2.  Cost-effectiveness of open versus laparoscopic appendectomy: a multilevel approach with propensity score matching.

Authors:  Laura Haas; Tom Stargardt; Jonas Schreyoegg
Journal:  Eur J Health Econ       Date:  2011-10-08

3.  Bayesian modelling of healthcare resource use in multinational randomized clinical trials.

Authors:  Aline Gauthier; Andrea Manca; Susan Anton
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

4.  Transferability of health cost evaluation across locations in oncology: cluster and principal component analysis as an explorative tool.

Authors:  Lionel Perrier; Alessandra Buja; Giuseppe Mastrangelo; Patrick Sylvestre Baron; Françoise Ducimetière; Petrus J Pauwels; Carlo Riccardo Rossi; François Noël Gilly; Amaury Martin; Bertrand Favier; Fadila Farsi; Mathieu Laramas; Vincenzo Baldo; Olivier Collard; Dominic Cellier; Jean-Yves Blay; Isabelle Ray-Coquard
Journal:  BMC Health Serv Res       Date:  2014-11-18       Impact factor: 2.655

5.  Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries.

Authors:  Christian E H Boehler; Joanne Lord
Journal:  Med Decis Making       Date:  2015-04-15       Impact factor: 2.583

6.  Prioritizing health system and disease burden factors: an evaluation of the net benefit of transferring health technology interventions to different districts in Zimbabwe.

Authors:  Shepherd Shamu; Simbarashe Rusakaniko; Charles Hongoro
Journal:  Clinicoecon Outcomes Res       Date:  2016-11-24

7.  SImplification of Medications Prescribed to Long-tErm care Residents (SIMPLER): study protocol for a cluster randomised controlled trial.

Authors:  Janet K Sluggett; Esa Y H Chen; Jenni Ilomäki; Megan Corlis; Sarah N Hilmer; Jan Van Emden; Choon Ean Ooi; Kim-Huong Nguyen; Tracy Comans; Michelle Hogan; Tessa Caporale; Susan Edwards; Lyntara Quirke; Allan Patching; J Simon Bell
Journal:  Trials       Date:  2018-01-12       Impact factor: 2.279

8.  Cost-effectiveness of a structured medication review approach for multimorbid older adults: Within-trial analysis of the OPERAM study.

Authors:  Paola Salari; Cian O'Mahony; Séverine Henrard; Paco Welsing; Arjun Bhadhuri; Nadine Schur; Marie Roumet; Shanthi Beglinger; Thomas Beck; Katharina Tabea Jungo; Stephen Byrne; Stefanie Hossmann; Wilma Knol; Denis O'Mahony; Anne Spinewine; Nicolas Rodondi; Matthias Schwenkglenks
Journal:  PLoS One       Date:  2022-04-11       Impact factor: 3.240

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.