This paper proposes to use the notion, composite maximum contrast method, for formulating recently proposed decision procedures for judging toxicity of chemical substances based on in vitro experiments. It is defined as the composite procedure which judges the substance in question to be positive if and only if two or more maximum contrast methods, such as Dunnett test and trend test, simultaneously realized statistical significance with a certain significance levels. SAS/IML programs are also introduced for calculating type I and/or type II error probabilities of composite maximum contrast methods. A real example of data analysis in a validation study of in vitro BALB/c 3T3 cell transformation assay, to which a composite maximum contrast method is applied, is also shown.
In this paper, a fast algorithm for computing inbreeding coefficients in large populations in which a list of ancestors for each animal is required is modified for more quick calculation. With the current algorithm, an efficient procedure to make the list of the ancestors is used, which is based on grouping the ancestors of each animal chronologically by the use of longest ancestral path. A simulation study is carried out to compare the computational properties of the presented algorithm with those of the original and another typical algorithms. It is found that the presented algorithm becomes considerably fast relative to the original one, as the average number of generations and the number of animals increase. It is also shown that the current algorithm takes less time than another typical algorithm compared, when less than 19 generations are evaluated. The results obtained clearly indicate that the current algorithm requires much less computer time and less memory than the original and another typical algorithms, respectively.
It is very important to provide safety information of new drugs to physicians and patients as soon as possible after the early postmarketing period. For that purpose, it is important to appropriately collect and analyze the spontaneous reports accumulated in databases of companies and regulatory agencies. This paper reviews the analytical methods to assess spontaneous reports. Bate et al. (1998) presented Bayesian Confidence Propagation Neural Network (BCPNN) Method used by Uppsala Monitoring Centre (UMC) of the World Health Organization (WHO). DuMouchel (1999) presented Gamma-Poisson Shrinker (GPS) Program of U. S. Food and Drug Administration (FDA), and Evans et al. (2001) presented Proportional Reporting Ratios (PRR) of the Medicines Control Agency (MCA). Furthermore, DuMouchel and Pregibon (2001) extended the GPS Program, proposing the Multi-Item Gamma Poisson Shrinker (MGPS) Program, which then became the standard method for the FDA. This report also reviews the practical problems (e.g. database, duplication cases, code of Medical Dictionary for Regulatory Activities (MedDRA)) encountered in Japan.