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Titolo: Sample size calculations for risk equivalence testing in pharmacoepidemiology
Autore: TubertBitter, P; Manfredi, R; Lellouch, J; Begaud, B;
 Indirizzi:
 INSERM, U472, F94807 Villejuif, France INSERM Villejuif France F94807INSERM, U472, F94807 Villejuif, France Univ Bordeaux 2, Dept Pharmacol, F33076 Bordeaux, France Univ Bordeaux 2Bordeaux France F33076 rmacol, F33076 Bordeaux, France
 Titolo Testata:
 JOURNAL OF CLINICAL EPIDEMIOLOGY
fascicolo: 12,
volume: 53,
anno: 2000,
pagine: 1268  1274
 SICI:
 08954356(200012)53:12<1268:SSCFRE>2.0.ZU;29
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 NULL HYPOTHESIS; RELATIVE RISK; TRIALS; POWER;
 Keywords:
 pharmacoepidemiology; cohort studies; equivalence; sample size; adverse drug reactions; risk comparison;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Clinical Medicine
 Life Sciences
 Citazioni:
 18
 Recensione:
 Indirizzi per estratti:
 Indirizzo: TubertBitter, P INSERM, U472, 16 Ave Paul Vaillant Couturier, F94807 Villejuif, France INSERM 16 Ave Paul Vaillant Couturier Villejuif France F94807



 Citazione:
 P. TubertBitter et al., "Sample size calculations for risk equivalence testing in pharmacoepidemiology", J CLIN EPID, 53(12), 2000, pp. 12681274
Abstract
Equivalence testing has been widely discussed and is commonly used in pharmacokinetics (bioequivalence) and clinical trials (therapeutic equivalence). It can also be applied to pharmacoepidemiology, where the aim may be to test with a known risk (onegroup design) or with another drug (twogroup design). Whether the approach is twosided or onesided, predefined equivalence limits are required. The definition of the equivalence region can be based on either risk difference or risk ratio. Risk equivalence resting is complicated by the binary nature of the outcome, its low frequency, and by theabsence of commonly defined equivalence limits for differences or ratios. In this context, we consider usable formulae for sample sizes. In most cases, at least when the risk studied is large enough (above 1/1,000), it appears that these formulae result in sample sizes that may be acceptable for practical purposes. For example, demonstrating equivalence with a known risk of 0.01, a 20% maximal risk difference, and a onesided test (alpha = 0.05 and beta = 0.2) requires: under the onegroup design (known risk), 15,309 patients; and under the twogroup design, 30,617 patients per group. This approach is the appropriate way to conclude equivalence, rather than the commonly used approach of difference testing and concluding equivalence when the null hypothesis of equality is not rejected. (C) 2000 Elsevier Science Inc. All rights reserved.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 20/01/20 alle ore 22:38:03