Literature DB >> 10424840

Power and sample size in cost-effectiveness analysis.

E M Laska1, M Meisner, C Siegel.   

Abstract

For resource allocation under a constrained budget, optimal decision rules for mutually exclusive programs require that the treatment with the highest incremental cost-effectiveness ratio (ICER) below a willingness-to-pay (WTP) criterion be funded. This is equivalent to determining the treatment with the smallest net health cost. The designer of a cost-effectiveness study needs to select a sample size so that the power to reject the null hypothesis, the equality of the net health costs of two treatments, is high. A recently published formula derived under normal distribution theory overstates sample-size requirements. Using net health costs, the authors present simple methods for power analysis based on conventional normal and on nonparametric statistical theory.

Mesh:

Year:  1999        PMID: 10424840     DOI: 10.1177/0272989X9901900312

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  6 in total

1.  Use of randomised controlled trials for producing cost-effectiveness evidence: potential impact of design choices on sample size and study duration.

Authors:  Martin E Backhouse
Journal:  Pharmacoeconomics       Date:  2002       Impact factor: 4.981

Review 2.  Advantages of using the net-benefit approach for analysing uncertainty in economic evaluation studies.

Authors:  Niklas Zethraeus; Magnus Johannesson; Bengt Jönsson; Mickael Löthgren; Magnus Tambour
Journal:  Pharmacoeconomics       Date:  2003       Impact factor: 4.981

3.  The delusion of reducing sample size.

Authors:  Bernard Bégaud; Annie Fourrier; Nicholas Moore; Yola Moride
Journal:  Eur J Clin Pharmacol       Date:  2003-10-18       Impact factor: 2.953

Review 4.  Sample size determination for cost-effectiveness trials.

Authors:  Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2011-11       Impact factor: 4.981

Review 5.  Estimation, power and sample size calculations for stochastic cost and effectiveness analysis.

Authors:  S D Walter; Amiram Gafni; Stephen Birch
Journal:  Pharmacoeconomics       Date:  2007       Impact factor: 4.981

6.  Sample size and power for cost-effectiveness analysis (part 1).

Authors:  Henry A Glick
Journal:  Pharmacoeconomics       Date:  2011-03       Impact factor: 4.981

  6 in total

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