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Noninferiority testing in cost-minimization studies: Practical issues concerning power analysis

Published online by Cambridge University Press:  28 March 2006

Mark M. Span ,
Elisabeth M. TenVergert ,
Christian S. van der Hilst  and
Ronald P. Stolk
Mark M. Span
Affiliation:
University Medical Center Groningen, University of Groningen
Elisabeth M. TenVergert
Affiliation:
University Medical Center Groningen, University of Groningen
Christian S. van der Hilst
Affiliation:
University Medical Center Groningen, University of Groningen
Ronald P. Stolk
Affiliation:
University Medical Center Groningen, University of Groningen
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Abstract

Objectives: In cost-minimization studies, it is important to establish noninferiority in the clinical effect of the treatments under investigation. The relationship between the proportion of patients reaching the end point in a study, equivalence limit (δ), and power is investigated in the context of cost-minimization studies with dichotomous clinical end points. Two formulations of the null-hypothesis, absolute and relative formulations of δ, will be explored.

Methods: Sensitivity analysis was performed, in which the effect of the predicted proportions and δ on the power in a noninferiority setting was investigated. The patterns found are discussed in terms of the practical relevance within the cost-minimization framework.

Results: Sensitivity analyses show different patterns of results for both null-hypotheses. The differences in these results originate from the way δ is expressed. By expressing δ as absolute difference, power grows quite fast when sample proportions are smaller than expected. In the case of a proportional δ at small sample proportions, the power to establish noninferiority remains low.

Conclusions: To obtain valid results from a cost-minimization study, care has to be taken to adapt the correct methodology for noninferiority testing in clinical outcomes. Defining δ in terms of absolute differences between treatments can lead to obscured results. Although conservative, the expression of δ as a proportion of the effectiveness of the treatment as usual is found to be closer to clinical practice. The inflated δ, resulting from smaller clinical effects than expected when absolute formulation is applied, thus can be avoided.

Keywords

Medical economicsCost-minimizationNoninferioritySensitivity analysis
Type
RESEARCH REPORTS
Information
International Journal of Technology Assessment in Health Care , Volume 22 , Issue 2 , April 2006 , pp. 261 - 266
Copyright
© 2006 Cambridge University Press

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References

Alderson P, Chalmers I. 2003 Survey of claims of no effect in abstracts of Cochrane reviews. BMJ. 326: 475.Google Scholar
Altman DG, Bland JM. 1995 Absence of evidence is not evidence of absence. BMJ. 311: 485.Google Scholar
Boucher M, Rodger M, Johnson JA, Tierney M. 2003 Shifting from inpatient to outpatient treatment of deep vein thrombosis in a tertiary care center: A cost-minimization analysis. Pharmacotherapy. 23: 301– 309.Google Scholar
Bourguignon C, Destrumelle A, Koch S, et al. 2003 Disposable versus reusable biopsy forceps in GI endoscopy: A cost-minimization analysis. Gastrointest Endosc. 58: 226.Google Scholar
Briggs AH, O'Brien BJ. 2001 The death of cost-minimization analysis? Health Econ. 10: 179– 184.Google Scholar
Committee For Proprietary Medicinal Products. 2001 Points to consider on switching between superiority and non-inferiority. Br J Clin Pharmacol. 52: 223– 228.
Committee For Proprietary Medicinal Products. 2004. Points to consider on the choice of non-inferiority margin. Draft working paper. EU: CPMP;
Drummond M, Sculpher M, Torrance G, O'Brien B, Stoddart G. 2005. Methods for the economic evaluation of health care programmes. 3rd ed. New York: Oxford University Press;
Ebbutt AF, Frith L. 1998 Practical issues in equivalence trials. Stat Med. 17: 1691– 1701.Google Scholar
Fink JM, Long JK, Goldman MP. 2003; Cost-minimization analysis of lipid amphotericin B products. Abstracts of the Interscience Conference on Antimicrobial Agents and Chemotherapy. Chicago: 43: 26.
Freiman JA, Chalmers TC, Smith H, Kübler RR. 1992: The importance of beta, the type II error, and sample size in the design and interpretation of the randomized controlled trial: Survey of two sets of “negative” trials. In: Bailar JC, Mosteller F, eds. Medical uses of statistics. 2nd ed. Boston: NEJM Books; 357– 373.
Gallant J, Staszewski S, Pozniak A, et al. 2004 Efficacy and safety of tenofovir DF vs stavudine in combination therapy in antiretroviral-naive patients: A 3-year randomized trial. JAMA. 292: 191– 201.Google Scholar
Gallant J, Staszewski S, Pozniak A, et al. 2004 Tenofovir, equivalence, and noninferiority–Reply. JAMA. 292: 1951– 195.Google Scholar
Hagens VE, Vermeulen KM, TenVergert EM, et al. 2004 Rate control is more cost-effective than rhythm control for patients with persistent atrial fibrillation—results from the RAte Control versus Electrical cardioversion (RACE) study. Eur Heart J. 25: 1542– 1549.Google Scholar
Harrell FE. 1998 The inappropriate use of hypothesis testing to infer safety of calcium channel blockers. Cardiovasc Drugs Ther. 12: 151– 153.Google Scholar
Hauschke D. 2001 Choice of Delta: A special case. Drug Inf J. 35: 875– 879.Google Scholar
Jones B, Jarvis P, Lewis JA, Ebbutt AF. 1996 Trials to assess equivalence: The importance of rigorous methods. BMJ. 313: 36– 39.Google Scholar
Laster LL, Johnson MF. 2003 Non-inferiority trials: The ‘at least as good as’ criterion. Stat Med. 22: 187– 200.Google Scholar
Munzel U, Hauschke D. 2003 A nonparametric test for proving noninferiority in clinical trials with ordered categorical data. Pharmaceut Statist. 2: 31– 37.Google Scholar
Parienti J. 2004 Tenofovir, equivalence, and noninferiority. JAMA. 292: 1951.Google Scholar
Phillips KF. 2003 A new test of non-inferiority for anti-infective trials. Stat Med. 22: 201– 212.Google Scholar
R Development Core Team 2005. R: A language and environment for statistical computing. ISBN 3-900051-07-0. Vienna, Austria: Foundation for Statistical Computing; Available at: http://www.R-project.org.
Ramrakha Jones VS, Herd RM. 2003 Treating Bowen's disease: A cost-minimization study. Br J Dermatol. 148: 1167– 1172.Google Scholar
Rohmel J, Mansmann U. 1999 Unconditional non-asymptotic one-sided tests for independent binomial proportions when the interest lies in showing non-inferiority and/or superiority. Biom J. 41: 149– 170.Google Scholar
Rothmann MD, Tsou HH. 2003 On non-inferiority analysis based on delta-method confidence intervals. J Biopharm Stat. 13: 565– 583.Google Scholar
Szegedi A, Kohnen R, Dienel A, Kieser M. 2005 Acute treatment of moderate to severe depression with hypericum extract WS 5570 (St John's wort): Randomised controlled double blind non-inferiority trial versus paroxetine. BMJ. 330: 503.Google Scholar
Talwalkar JA, Angulo P, Johnson CD, Petersen BT, Lindor KD. 2004 Cost-minimization analysis of MRC versus ERCP for the diagnosis of primary sclerosing cholangitis. Hepatology. 40: 39– 45.Google Scholar
The European and Israeli Study Group on Highly Purified Menotropin versus Recombinant Follicle-Stimulating Hormone. 2002 Efficacy and safety of highly purified menotropin versus recombinant follicle-stimulating hormone in in vitro fertilization/intracytoplasmic sperm injection cycles: A randomized, comparative trial. Fertil Steril. 78: 520– 528.
Tubert-Bitter P, Manfredi R, Lellouch J, Begaud B. 2000 Sample size calculations for risk equivalence testing in pharmacoepidemiology. J Clin Epidemiol. 53: 1268– 1274.Google Scholar
Ware JH, Antman EM. 1997 Equivalence trials. N Engl J Med. 337: 1159– 1161.Google Scholar
Wiens BL. 2002 Choosing an equivalence limit for noninferiority or equivalence studies. Control Clin Trials. 23: 2– 14.Google Scholar