Literature DB >> 26061469

Synthesis of survival and disease progression outcomes for health technology assessment of cancer therapies.

N J Welton1, S R Willis2, A E Ades3.   

Abstract

Studies of clinical efficacy commonly report more than one clinical endpoint. For example, randomized controlled trials of treatments for cancer will normally report time to disease progression as well as overall survival. It is likely that disease progression will be associated with higher mortality rates. Disease progression rates will also have consequences for the societal economic burden of the disease. Economic evaluation of the cost-effectiveness of different treatment regimes therefore requires the joint estimation of both disease progression and mortality. We describe a model to combine evidence from studies reporting time to event summaries for disease progression and/or mortality, motivated by a systematic review of 1st-line treatment for advanced breast cancer to provide inputs for an economic evaluation as part of the National Institute for Health and Clinical Excellence (NICE) clinical guideline on treatment of advanced breast cancer in England and Wales. The review identified a network of treatment comparisons, which provides the basis for indirect comparison. A variety of outcomes were reported: overall survival, time to progression (overall and responders only), and the proportion of responder, stable, progressive disease, and non-assessable patients. There were only five trials, and not all trials reported all outcomes. The scarcity of the available evidence required us to make strong assumptions in order to identify model parameters. However, this evidence structure often occurs in health technology assessment (HTA) of treatments for cancer. We discuss the validity of the assumptions made, and the potential to assess their validity in other applications of HTA of cancer therapies.
Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Markov Chain Monte Carlo (MCMC); advanced breast cancer; cost‐effectiveness analysis; mixed treatment comparison; multiple outcomes; multi‐parameter evidence synthesis; network meta‐analysis

Year:  2011        PMID: 26061469     DOI: 10.1002/jrsm.21

Source DB:  PubMed          Journal:  Res Synth Methods        ISSN: 1759-2879            Impact factor:   5.273


  8 in total

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2.  Evidence synthesis for decision making 7: a reviewer's checklist.

Authors:  A E Ades; Deborah M Caldwell; Stefanie Reken; Nicky J Welton; Alex J Sutton; Sofia Dias
Journal:  Med Decis Making       Date:  2013-07       Impact factor: 2.583

3.  Evidence synthesis for decision making 5: the baseline natural history model.

Authors:  Sofia Dias; Nicky J Welton; Alex J Sutton; A E Ades
Journal:  Med Decis Making       Date:  2013-07       Impact factor: 2.583

4.  Shared parameter model for competing risks and different data summaries in meta-analysis: Implications for common and rare outcomes.

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Journal:  Res Synth Methods       Date:  2019-08-22       Impact factor: 5.273

5.  The impact of childhood malnutrition on mortality from pneumonia: a systematic review and network meta-analysis.

Authors:  Amir Kirolos; Rachel M Blacow; Arun Parajuli; Nicky J Welton; Alisha Khanna; Stephen J Allen; David A McAllister; Harry Campbell; Harish Nair
Journal:  BMJ Glob Health       Date:  2021-11

6.  Meta-regression models to address heterogeneity and inconsistency in network meta-analysis of survival outcomes.

Authors:  Jeroen P Jansen; Shannon Cope
Journal:  BMC Med Res Methodol       Date:  2012-10-08       Impact factor: 4.615

7.  Evidence synthesis for decision making 6: embedding evidence synthesis in probabilistic cost-effectiveness analysis.

Authors:  Sofia Dias; Alex J Sutton; Nicky J Welton; A E Ades
Journal:  Med Decis Making       Date:  2013-07       Impact factor: 2.583

8.  Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data.

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  8 in total

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