Two-stage Testing of Safety: A Statistical View

Abstract

Sample sizes given in regulatory guidelines are not based on statistical reasoning. However, from an ethical, scientific, and regulatory point of view, a mutagenicity experiment must have a reasonable chance of supporting the decision as to whether a result is negative or positive. Consequently, the sample size should be based on type I and type II errors, the underlying variability, and the specific size of a treatment effect. A two-stage adaptive interim analysis is presented, which permits an adaptive choice of sample size after an interim analysis of the data from the first stage. Because the sample size of the first stage is considered to be a minimum requirement, this stage can also be regarded as a pilot study.

Keywords

interim analysis mutagenicity assay proof of safety two-stage testing

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References

Hothorn

L.A.

, & Hauschke

(2000). Identifying the maximum safe dose: a multiple testing approach. Journal of Biopharmaceutical Statistics 10, 15–30.

Hayashi

, Tice

R.R.

, & MacGregor

J.T.

(1994). In vivo rodent erythrocyte micronucleus assay. Mutation Research 312, 293–304.

Hauschke

, Hayashi

, Lin

K.K.

, Lovell

D.P.

, Robinson

W.D.

, & Yoshimura

(1997). Recommendations for biostatistics of mutagenicity studies. Drug Information Journal 31, 323–326.

Cariello

N.F.

, & Piegorsch

W.W.

(1996). The Ames test: the two-fold rule revisited. Mutation Research 369, 23–31.

Takanashi

, & Urano

(1998). Statistical procedures for estimating the detection limit and determination limit of the Ames Salmonella mutagenicity assay. Science of the Total Environment 221, 31–42.

Oberly

T.J.

, Hoffman

W.P.

, & Garriott

M.L.

(1996). An evaluation of the twofold rule for assessing a positive response in the L5178Y TK+/- mouse lymphoma assay. Mutation Research 369, 221–232.

Sasabuchi

(1988). A multivariate one-sided test with composite hypotheses determined by linear inequalities when the covariance matrix has an unknown scale factor. Memoirs of the Faculty of Science, Kyushu University, Series A, Mathematics 42, 9–19.

Hauschke

, Kieser

, & Hothorn

L.A.

(1999). Proof of safety in toxicology based on the ratio of two means for normally distributed data. Biometrical Journal 41, 295–304.

Bauer

, & Koehne

(1994). Evaluation of experiments with adaptive interim analysis. Biometrics, 50, 1029–1041.

10.

Hewett

J.E.

, & Bair

(1986). A two-stage test for ordered means in the Poisson case with an example from mutagenicity testing. Biometrics 42, 647–651.

11.

Jonckheere

A.R.

(1954). A distribution-free k-sample test against ordered alternatives. Biometrika 41, 133–145.

12.

Hewett

J.E.

, & Spurrier

J.D.

(1979). Some two-stage k-sample tests. Journal of the American Statistical Association 74, 398–404.

13.

Edler

(1994). Biostatistical issues in the design and analysis of multiple or repeated genotoxicity assays. Environmental Health Perspectives 102, Suppl. 1, 53–59.

14.

Mueller

H.H.

, & Schaefer

(1999). Optimization of testing times and critical values in sequential equivalence testing. Statistics in Medicine 18, 1769–1788.

15.

Fieller

(1954). Some problems in interval estimation. Journal of the Royal Statistical Society B16, 175–185.

16.

Jennison

, & Turnbull

(1989). Interim analysis: the repeated confidence interval approach. Journal of the Royal Statistical Society B51, 305–361.

17.

Simpson

D.G.

, & Margolin

B.H.

(1986). Recursive nonparametric testing for dose–response relationships subject to downturns at higher doses. Biometrika 73, 589–596.