Literature DB >> 17960950

It's time to choose the study design!: net benefit analysis of alternative study designs to acquire information for evaluation of health technologies.

Oren Shavit1, Moshe Leshno, Assaf Goldberger, Amir Shmueli, Amnon Hoffman.   

Abstract

Uncertainty in the decision-making process for reimbursement of health technologies could be reduced if additional information were available. Although methods to evaluate the monetary value of the uncertainty have been previously described, an economic evaluation of alternative methods to acquire additional information has not yet been thoroughly explored. Should resources be allocated to a retrospective study design or to a randomised controlled trial (RCT) when additional information is deemed justified? We propose an approach for cost-effectiveness analysis of designs of future studies that are required to evaluate health technologies for reimbursement. Biases inherent in study designs are the main factor that differentiates the ability of the studies to predict the technology's benefit. By quantifying this inherent-bias effect, the incremental effectiveness of future studies can be evaluated. Economic consequences of decisions regarding prioritization of the technologies, along with the expected costs incurred by the study's execution, account for the cost component of the equation. Deducting the result retrieved for the retrospective design from that of the RCT design gives the net information benefit.

Mesh:

Year:  2007        PMID: 17960950     DOI: 10.2165/00019053-200725110-00002

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  29 in total

1.  Randomized trials or observational tribulations?

Authors:  S J Pocock; D R Elbourne
Journal:  N Engl J Med       Date:  2000-06-22       Impact factor: 91.245

2.  Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals.

Authors:  K Claxton
Journal:  Health Econ       Date:  1999-05       Impact factor: 3.046

3.  A rational framework for decision making by the National Institute For Clinical Excellence (NICE).

Authors:  Karl Claxton; Mark Sculpher; Michael Drummond
Journal:  Lancet       Date:  2002-08-31       Impact factor: 79.321

4.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.

Authors:  R B D'Agostino
Journal:  Stat Med       Date:  1998-10-15       Impact factor: 2.373

Review 5.  Complexity and contradiction in clinical trial research.

Authors:  R I Horwitz
Journal:  Am J Med       Date:  1987-03       Impact factor: 4.965

6.  Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials.

Authors:  K F Schulz; I Chalmers; R J Hayes; D G Altman
Journal:  JAMA       Date:  1995-02-01       Impact factor: 56.272

7.  Randomized versus historical controls for clinical trials.

Authors:  H Sacks; T C Chalmers; H Smith
Journal:  Am J Med       Date:  1982-02       Impact factor: 4.965

8.  Bias in treatment assignment in controlled clinical trials.

Authors:  T C Chalmers; P Celano; H S Sacks; H Smith
Journal:  N Engl J Med       Date:  1983-12-01       Impact factor: 91.245

9.  Developing improved observational methods for evaluating therapeutic effectiveness.

Authors:  R I Horwitz; C M Viscoli; J D Clemens; R T Sadock
Journal:  Am J Med       Date:  1990-11       Impact factor: 4.965

10.  Expected value of sample information calculations in medical decision modeling.

Authors:  A E Ades; G Lu; K Claxton
Journal:  Med Decis Making       Date:  2004 Mar-Apr       Impact factor: 2.583

View more
  1 in total

1.  Assessing the value of a future study.

Authors:  Afschin Gandjour
Journal:  Pharmacoeconomics       Date:  2008       Impact factor: 4.981

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.