Are head-to-head trials of biologics needed? The role of value of information methods in arthritis research.

@article{Welton2011AreHT,
  title={Are head-to-head trials of biologics needed? The role of value of information methods in arthritis research.},
  author={Nicky J Welton and Jason Madan and Anthony Ades},
  journal={Rheumatology},
  year={2011},
  volume={50 Suppl 4},
  pages={
          iv19-25
        }
}
Reimbursement decisions are typically based on cost-effectiveness analyses. While a cost-effectiveness analysis can identify the optimum strategy, there is usually some degree of uncertainty around this decision. Sources of uncertainty include statistical sampling error in treatment efficacy measures, underlying baseline risk, utility measures and costs, as well as uncertainty in the structure of the model. The optimal strategy is therefore only optimal on average, and a decision to adopt this… 
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Citation Type

Calculating the Expected Value of Sample Information Using Efficient Nested Monte Carlo: A Tutorial.
  • Anna Heath, G. Baio
  • Medicine
    Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
  • 2018
Planning future studies based on the precision of network meta-analysis results.
TLDR
The presented methodology can aid investigators making informed and evidence-based decisions about planning new studies by calculating the required sample size for each possible study design and number of study arms and presenting visual tools that can help trialists select the optimal study design.
Consensus Decision Models for Biologics in Rheumatoid and Psoriatic Arthritis: Recommendations of a Multidisciplinary Working Party
TLDR
Consensus guidance that fully reflects current evidence and clinical understanding was gained successfully and research needs have been identified.
Sample Size Estimation for Non-Inferiority Trials: Frequentist Approach versus Decision Theory Approach
TLDR
It is argued that the decision theory approach of sample size estimation is most suitable for sample size calculation of non-inferiority trials.
Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials
TLDR
A planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis is used as an example of the approach, and it is demonstrated that one may avoid “Type III errors”—inadvertently leaving out the best treatment—with little loss in power compared to a two-arm design.
Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression
TLDR
A method to reduce the computation time required to calculate the expected value of sample information (EVSI) across different sample sizes is developed, which will allow researchers to realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models.
Effectiveness of biologics in rheumatology: improving the evidence base.
TLDR
The aim of the workshop reported in this supplement was to explore the role of both network meta-analysis and value of information (VOI): methods that are a major area of interest for the Bristol MRC Trials Methodology Hub.
The expected value of perfect information in unrepeatable decision-making
A Systematic and Critical Review of the Evolving Methods and Applications of Value of Information in Academia and Practice
TLDR
A systematic and critical review of the evolving methods and applications of value of information (VOI) in academia and practice and discusses where future research needs to be directed.
Why the findings of published multiple treatment comparison meta-analyses of biologic treatments for rheumatoid arthritis are different: an overview of recurrent methodological shortcomings
TLDR
The quality of published multiple treatment comparison meta-analyses on biologic disease modifying antirheumatic drugs for rheumatoid arthritis is evaluated to identify methodological issues that can explain the discrepancies in findings.
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