Weighted p-value adjustments for animal carcinogenicity trend test

A typical animal carcinogenicity experiment routinely analyzes approximately 10-30 tumor sites. Comparisons of tumor responses between dosed and control groups and dose-related trend tests are often evaluated for each individual tumor site/type separately. p-Value adjustment approaches have been proposed for controlling the overall Type I error rate or familywise error rate (FWE). However, these adjustments often result in reducing the power to detect a dose effect. This paper proposes using weighted adjustments by assuming that each tumor can be classified as either class A or class B based on prior considerations. The tumors in class A, which are considered as more critical endpoints, are given less adjustment. Two weighted methods of adjustments are presented, the weighted p adjustment and weighted alpha adjustment. A Monte Carlo simulation shows that both weighted adjustments control the FWE well. Furthermore, the power increases if a treatment-dependent tumor is analyzed as in class A tumors and the power decreases if it is analyzed as in class B tumors. A data set from a National Toxicology Program (NTP) 2-year animal carcinogenicity experiment with 13 tumor types/sites observed in male mice was analyzed using the proposed methods. The modified poly-3 test was used to test for increased carcinogenicity since it has been adopted by the NTP as a standard test for a dose-related trend. The unweighted adjustment analysis concluded that there was no statistically significant dose-related trend. Using the Food and Drug Administration classification scheme for the weighted adjustment analyses, two rare tumors (with background rates of 1% or less) were analyzed as class A tumors and 11 common tumors (with background rates higher than 1%) as class B. Both weighted analyses showed a significant dose-related trend for one rare tumor.     

Tests for Genetic Differentiation

When testing for genetic differentiation the joint null hypothesis that there is no allele frequency difference at any locus is of interest. Common approaches to test this hypothesis are based on the summation of χ² statistics over loci and on the Bonferroni correction, respectively. Here, we also consider the Simes adjustment and a recently proposed truncated product method (TPM) to combine P‐values. The summation and the TPM (using a relatively large truncation point) are powerful when there are differences in many or all loci. The Simes adjustment, however, is powerful when there are differences regarding one or a few loci only. As a compromise between the different approaches we introduce a combination between the Simes adjustment and the TPM, i.e. the joint null hypothesis is rejected if at least one of the two methods, Simes and TPM, is significant at the α/2‐level. Simulation results indicate that this combination is a robust procedure with high power over the different types of alternatives.     

A note on controlling the number of false positives

Summary :   Lehmann and Romano (2005, Annals of Statistics 33, 1138–1154) discuss a Bonferroni-type procedure that bounds the probability that the number of false positives is larger than a specified number. We note that this procedure will have poor power as compared to a multivariate permutation test type procedure when the experimental design accommodates a permutation test. An example is given involving gene expression microarray data of breast cancer tumors.     

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.     

Tables and Applications of the Bonferroni t-Statistics: A Revision of Dunn's Simultaneous t-Tests

Tables for Bonferroni-adjusted significance levels of Student's t are provided for r = 3(1)30(5)50 etc. and alphas of 0.05, 0.01 and 0.001. The tables are suggested for various applications, for example in replacing analysis of variance of k samples by r simultaneous t tests. Use of the tables is shown by numerical examples. The adjustment of alpha for improving the efficiency of testing is made by WILKINSON for orthogonal comparisons and by HOLM for nonorthogonal comparisons.     

Estimation and Multiple Comparisons When There Are Missing Values with an Application in Immunology

A method is suggested for handling multiple comparisons in repeated measurement situations with completely random missing values. Exact results are obtained for the situation with normally distributed observations in the case of compound symmetry. The method uses grouping with respect to the positions of the missing values. It is most efficient and best suited when there are not too many measurement occasions in the longitudinal investigation.