Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action.

Many Bayes factors have been proposed for comparing population means in two-sample (independent samples) studies. Recently, Wang and Liu (2015) presented an "objective" Bayes factor (BF) as an alternative to a "subjective" one presented by Gönen et al. (2005).

Teaching Principal Components Using Correlations.

Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation.

Closed Testing in Pharmaceutical Research: Historical and Recent Developments.

In pharmaceutical research, making multiple statistical inferences is standard practice. Unless adjustments are made for multiple testing, the probability of making erroneous determinations of significance increases with the number of inferences. Closed testing is a flexible and easily explained approach to controlling the overall error rate that has seen wide use in pharmaceutical research, particularly in clinical trials settings.

Kurtosis as Peakedness, 1905 - 2014. .

The incorrect notion that kurtosis somehow measures "peakedness" (flatness, pointiness or modality) of a distribution is remarkably persistent, despite attempts by statisticians to set the record straight. This article puts the notion to rest once and for all. Kurtosis tells you virtually nothing about the shape of the peak - its only unambiguous interpretation is in terms of tail extremity; i.

Analysis and correction of crosstalk effects in pathway analysis.

Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent.

On using the bootstrap for multiple comparisons.

There are many ways to bootstrap data for multiple comparisons procedures. Methods described here include (i) bootstrap (parametric and nonparametric) as a generalization of classical normal-based MaxT methods, (ii) bootstrap as an approximation to exact permutation methods, (iii) bootstrap as a generator of realistic null data sets, and (iv) bootstrap as a generator of realistic non-null data sets. Resampling of MinP versus MaxT is discussed, and the use of the bootstrap for closed testing is also presented.

How well do multiple testing methods scale up when both n and k increase?

With increasingly massive data sets in biopharmaceutical research, particularly in genomic and related applications, there is concern about how well multiple comparisons methods "scale up" with increasing number of tests (k). Familywise error rate-controlling methods do not scale up well, and false discovery rate-controlling methods do scale up well with increasing k. But neither method scales up well with increasing sample size (n) when testing point nulls.

Permutational Multiple Testing Adjustments With Multivariate Multiple Group Data.

We consider the multiple comparison problem where multiple outcomes are each compared among several different collections of groups in a multiple group setting. In this case there are several different types of hypotheses, with each specifying equality of the distributions of a single outcome over a different collection of groups. Each type of hypothesis requires a different permutational approach.

Multiple McNemar tests.

Methods for performing multiple tests of paired proportions are described. A broadly applicable method using McNemar's exact test and the exact distributions of all test statistics is developed; the method controls the familywise error rate in the strong sense under minimal assumptions. A closed form (not simulation-based) algorithm for carrying out the method is provided.

Multiple testing with minimal assumptions.

Resampling-based multiple testing methods that control the Familywise Error Rate in the strong sense are presented. It is shown that no assumptions whatsoever on the data-generating process are required to obtain a reasonably powerful and flexible class of multiple testing procedures. Improvements are obtained with mild assumptions.

Clinical trials simulation: a statistical approach.

A generic template for clinical trials simulations that are typically required by statisticians is developed. Realistic clinical trials data sets are created using a unifying model that allows general correlation structures for endpoint*timepoint data and nonnormal distributions (including time-to-event), and computationally efficient algorithms are presented. The model allows for patient dropout and noncompliance.

Decision-theoretic views on simultaneous testing of superiority and noninferiority.

In a simultaneous testing of noninferiority and superiority in clinical trials, there is no multiplicity penalty. Ng (2003), however, argues that even though there is no inflation of the Type I error rate, this type of simultaneous testing is problematic because it may lead to loss of power in the subsequent confirmatory trial. And he recommends to conduct only one test chosen on the basis of the sponsor's preliminary assessment.

Development and validation of an age-risk score for mortality predication after thermal injury.

Background: In burn patients, the risk of mortality typically decreases as children mature, reaches a nadir at age 21, rises linearly thereafter, and levels off in old age. We hypothesized that a single "age-risk score" (AGESCORE), incorporating a cubic functional form, can be used in predictive models for mortality after burns.

Methods: Data from 6,395 thermally injured patients admitted to a single burn center between January 1, 1950, and December 31, 1999, were used.

Industry, government, and academic panel discussion on multiple comparisons in a "real" phase three clinical trial.

A Food and Drug Administration (FDA)/Industry/Academic Panel Discussion on multiplicity aspects of a real Phase III clinical trial was held at the Third International Conference on Multiple Comparisons, August 6, 2002, in Bethesda, Maryland. The goal was to develop some consensus among industry, government, and academic statisticians concerning requirements and methods for multiplicity management in typical clinical trials. The session was tape-recorded; this article mostly comes from an edited transcript.

Gatekeeping strategies for clinical trials that do not require all primary effects to be significant.

In this paper we describe methods for addressing multiplicity issues arising in the analysis of clinical trials with multiple endpoints and/or multiple dose levels. Efficient 'gatekeeping strategies' for multiplicity problems of this kind are developed. One family of hypotheses (comprising the primary objectives) is treated as a 'gatekeeper', and the other family or families (comprising secondary and tertiary objectives) are tested only if one or more gatekeeper hypotheses have been rejected.

Bayesian multiple testing for two-sample multivariate endpoints.

In clinical studies involving multiple variables, simultaneous tests are often considered where both the outcomes and hypotheses are correlated. This article proposes a multivariate mixture prior on treatment effects, that allows positive probability of zero effect for each hypothesis, correlations among effect sizes, correlations among binary outcomes of zero versus nonzero effect, and correlations among the observed test statistics (conditional on the effects). We develop a Bayesian multiple testing procedure, for the multivariate two-sample situation with unknown covariance structure, and obtain the posterior probabilities of no difference between treatment regimens for specific variables.

Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals.

There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype.