bifactorial: General information on the package

Description

Factorial clinical trial designs can be used to test for the efficacy of combination drugs with two or more components, where inference on the question if a combination therapy is more efficacious than both of its components is based on the min-test proposed by Laska and Meisner (1989). This is due to regulatoric demands requiring a contribution of all compounds in a combination drug. The AVE- and MAX-approaches proposed by Hung, Chi and Lipicky (1993) test for the existence of any desirable combination. Bootstrap-based methods are implemented as well as classical approaches available from literature to obtain p-values and confidence intervals in such designs. For the min-test, analytical methods use a normality and homoscedasticity assumption on the data (Hung, Chi and Lipicky, 1993 and Hung, 2000). Critical values needed for determination of confidence intervals are calculated using quantiles of the multivariate t-distribution (Bretz, Genz and Hothorn 2001). These methods fail when handling with data that are skewed or heteroscedastic over the treatment groups. Furthermore, no analytical approach is available for the trifactorial case and the AVE- and MAX-tests on binary data. In the bootstrap approach, only the empirical distribution of the data is used and thus the results are valid for any distributional shape, provided that sufficiently large samples are available. Less analytical framework is needed to handle with the distributional properties of the tests. Further information on resampling-based methods and theoretical backgrounds are given in Westfall and Young (1993).

Anyway, the problem of the extremely decreasing power for small values of the so-called nuisance parameters indicating the response differences between the marginal treatment groups cannot be resolved by the bootstrap approach. Any algorithm based on estimates for the nuisance parameters other than the assumption that they are infinite will exceed the given significance level (Snapinn, 1987). The package contains the generic functions mintest and margint to test for mean differences of given numeric data vectors and differences in event rates for binary data applications. Method dispatch is available for objects of class carpet or cube, which will lead to min-test results on a bi- or trifactorial design and corresponding confidence intervals comparing combination treatments with their respective component therapies. Implementations for global tests are also available by the generic functions avetest and maxtest.

Arguments

References

Bretz F, Genz A, Hothorn LA (2001): On the numerical availability of multiple comparison procedures. Biometrical J 43/5, pp. 645-656

Frommolt P, Hellmich M (2009): Resampling in multiple-dose factorial designs. Biometrical J 51(6), pp. 915-31 Hellmich M, Lehmacher W (2005): Closure procedures for monotone bi-factorial dose-response designs. Biometrics 61, pp. 269-276 Hung HMJ, Chi GYH, Lipicky RJ (1993): Testing for the existence of a desirable dose combination. Biometrics 49, pp. 85-94

Hung HMJ, Wang SJ (1997): Large-sample tests for binary outcomes in fixed-dose combination drug studies. Biometrics 53, pp. 498-503 Hung HMJ (2000): Evaluation of a combination drug with multiple doses in unbalanced factorial design clinical trials. Statist Med 19, pp. 2079-2087

Laska EM, Meisner MJ (1989): Testing whether an identified treatment is best. Biometrics 45, pp. 1139-1151

Snapinn SM (1987): Evaluating the efficacy of a combination therapy. Statist Med 6, pp. 657-665 Westfall PH, Young SS (1993): Resampling-based multiple testing. John Wiley & Sons, Inc., New York