Literature DB >> 35788374

Methods and Study Design for Cancer Health Economics Research: Summary of Discussions From a Breakout Session.

Henry J Henk1, Ya-Chen Tina Shih2, Bijan J Borah3.   

Abstract

The legitimacy of findings from cancer health economics research depends on study design and methods. A breakout session, Methods and Study Design for Cancer Health Economics Research, was convened at the Future of Cancer Health Economics Research Conference to discuss 2 commonly used analytic tools for cancer health economics research: observational studies and decision-analytic modeling. Observational studies include analysis of data collected with the primary purpose of supporting economic evaluation or secondary use of data collected for another purpose. Modeling studies develop a parametrized structure, such as a decision tree, to estimate hypothetical impact. Whereas observational studies focus on what has happened and why, modeling studies address what may happen. We summarize the discussion at this breakout session, focusing on 3 key elements of high-quality cancer health economics research: study design, analytical methods, and addressing uncertainty.
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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Year:  2022        PMID: 35788374      PMCID: PMC9255929          DOI: 10.1093/jncimonographs/lgac013

Source DB:  PubMed          Journal:  J Natl Cancer Inst Monogr        ISSN: 1052-6773


  40 in total

1.  Estimating log models: to transform or not to transform?

Authors:  W G Manning; J Mullahy
Journal:  J Health Econ       Date:  2001-07       Impact factor: 3.883

2.  The logged dependent variable, heteroscedasticity, and the retransformation problem.

Authors:  W G Manning
Journal:  J Health Econ       Date:  1998-06       Impact factor: 3.883

3.  Some cautions on the use of instrumental variables estimators in outcomes research: how bias in instrumental variables estimators is affected by instrument strength, instrument contamination, and sample size.

Authors:  William H Crown; Henry J Henk; David J Vanness
Journal:  Value Health       Date:  2011-10-01       Impact factor: 5.725

4.  Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report--Part II.

Authors:  Emily Cox; Bradley C Martin; Tjeerd Van Staa; Edeltraut Garbe; Uwe Siebert; Michael L Johnson
Journal:  Value Health       Date:  2009-09-10       Impact factor: 5.725

5.  On estimating costs for economic evaluation in failure time studies.

Authors:  A P Hallstrom; S D Sullivan
Journal:  Med Care       Date:  1998-03       Impact factor: 2.983

6.  Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.

Authors:  J Jaime Caro; Andrew H Briggs; Uwe Siebert; Karen M Kuntz
Journal:  Value Health       Date:  2012 Sep-Oct       Impact factor: 5.725

7.  Markov models in medical decision making: a practical guide.

Authors:  F A Sonnenberg; J R Beck
Journal:  Med Decis Making       Date:  1993 Oct-Dec       Impact factor: 2.583

8.  Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine.

Authors:  Gillian D Sanders; Peter J Neumann; Anirban Basu; Dan W Brock; David Feeny; Murray Krahn; Karen M Kuntz; David O Meltzer; Douglas K Owens; Lisa A Prosser; Joshua A Salomon; Mark J Sculpher; Thomas A Trikalinos; Louise B Russell; Joanna E Siegel; Theodore G Ganiats
Journal:  JAMA       Date:  2016-09-13       Impact factor: 56.272

Review 9.  Bayesian methods for evidence synthesis in cost-effectiveness analysis.

Authors:  A E Ades; Mark Sculpher; Alex Sutton; Keith Abrams; Nicola Cooper; Nicky Welton; Guobing Lu
Journal:  Pharmacoeconomics       Date:  2006       Impact factor: 4.981

10.  Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

Authors:  Claire Williams; James D Lewsey; Andrew H Briggs; Daniel F Mackay
Journal:  Med Decis Making       Date:  2016-06-08       Impact factor: 2.583

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