Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials

When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all‐comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker‐stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker‐defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker‐stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker‐defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker‐based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker‐negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker‐positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided.     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

Trading transforms of non-weighted simple games and integer weights of weighted simple games

Theory and Decision - This study investigates simple games. A fundamental research question in this field is to determine necessary and sufficient conditions for a simple game to be a weighted...     

A note on variable selection in functional regression via random subspace method

Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors. The functional model can be represented by certain multiple linear regression model via basis expansions of functional variables. Based on this model and random subspace method of Mielniczuk and Teisseyre (Comput Stat Data Anal 71:725–742, 2014), two simple variable selection procedures for scalar response functional regression model are proposed. The final functional model is selected by using generalized information criteria. Monte Carlo simulation studies conducted and a real data example show very satisfactory performance of new variable selection methods under finite samples. Moreover, they suggest that considered procedures outperform solutions found in the literature in terms of correctly selected model, false discovery rate control and prediction error.     

Nonlinear regression modeling via the lasso-type regularization

We consider the problem of constructing nonlinear regression models with Gaussian basis functions, using lasso regularization. Regularization with a lasso penalty is an advantageous in that it estimates some coefficients in linear regression models to be exactly zero. We propose imposing a weighted lasso penalty on a nonlinear regression model and thereby selecting the number of basis functions effectively. In order to select tuning parameters in the regularization method, we use a deviance information criterion proposed by Spiegelhalter et al. (2002), calculating the effective number of parameters by Gibbs sampling. Simulation results demonstrate that our methodology performs well in various situations.     

A polynomial-time perfect sampler for the <Emphasis Type="Italic">Q</Emphasis>-Ising with a vertex-independent noise

We present a polynomial-time perfect sampler for the Q-Ising with a vertex-independent noise. The Q-Ising, one of the generalized models of the Ising, arose in the context of Bayesian image restoration in statistical mechanics. We study the distribution of Q-Ising on a two-dimensional square lattice over n vertices, that is, we deal with a discrete state space {1,…,Q} ⁿ for a positive integer Q. Employing the Q-Ising (having a parameter β) as a prior distribution, and assuming a Gaussian noise (having another parameter α), a posterior is obtained from the Bayes’ formula. Furthermore, we generalize it: the distribution of noise is not necessarily a Gaussian, but any vertex-independent noise. We first present a Gibbs sampler from our posterior, and also present a perfect sampler by defining a coupling via a monotone update function. Then, we show O(nlog n) mixing time of the Gibbs sampler for the generalized model under a condition that β is sufficiently small (whatever the distribution of noise is). In case of a Gaussian, we obtain another more natural condition for rapid mixing that α is sufficiently larger than β. Thereby, we show that the expected running time of our sampler is O(nlog n).     

Prediction in a mixed Poisson cluster model

Motivated by insurance applications, a mixed Poisson cluster model is considered, where the cluster center process is a mixed Poisson process and descendant processes are additive processes. Each point of the center process represents a claim’s reported time and descendant processes are interpreted as processes of the corresponding payments or number of payments. In this study, we focus on the process aggregating all separate claim’s payment processes. Given the past observations, we study prediction of future increments and their mean-squared errors, also revealing the dependency between future increments from non-reported (IBNR) claims and the past available information. In the existing literature, they are independent since models were considered with a purely Poissonian center process. We derive computationally reasonable expressions for predictors and their variances.     

Bioaccessibility of Fukushima‐Accident‐Derived Cs in Soils and the Contribution of Soil Ingestion to Radiation Doses in Children

Ingestion of contaminated soil is one potential internal exposure pathway in areas contaminated by the Fukushima Daiichi Nuclear Power Plant accident. Doses from this pathway can be overestimated if the availability of radioactive nuclides in soils for the gastrointestinal tract is not considered. The concept of bioaccessibility has been adopted to evaluate this availability based on in vitro tests. This study evaluated the bioaccessibility of radioactive cesium from soils via the physiologically‐based extraction test (PBET) and the extractability of those via an extraction test with 1 mol/L of hydrochloric acid (HCl). The bioaccessibility obtained in the PBET was 5.3% ± 1%, and the extractability in the tests with HCl was 16% ± 3%. The bioaccessibility was strongly correlated with the extractability. This result indicates the possibility that the extractability in HCl can be used as a good predictor of the bioaccessibility with PBET. In addition, we assessed the doses to children from the ingestion of soil via hand‐to‐mouth activity based on our PBET results using a probabilistic approach considering the spatial distribution of radioactive cesium in Date City in Fukushima Prefecture and the interindividual differences in the surveyed amounts of soil ingestion in Japan. The results of this assessment indicate that even if children were to routinely ingest a large amount of soil with relatively high contamination, the radiation doses from this pathway are negligible compared with doses from external exposure owing to deposited radionuclides in Fukushima Prefecture.     

A queuing/management consideration on Japanese production systems

A single-stage lot/cell production under a Poisson arrival and exponential service in a batch is considered. The three economic queuing models of push and pull types are presented, an economic comparison of push versus pull types is considered, and a strategic management/design consideration to the lot production is given. First, the total expected operating cost is given for the three queuing models including the Omote-Kanban type similar to VMI. Second, the push versus pull system is discussed from a view of setup time, inventory or operating cost, and it is ascertained that the three types are alternative. Finally, a strategic management basis for economic traffic, leadtime setting is given, and discussed by the introduction of production matrix on 2-stage design.     

Kindling Backfires: Cultivating a National Antislavery Movement, 1836–1838

‘Kindling Backfires’ introduces the reader to a cohort of 70 white abolitionists who could be counted as members of the upwardly mobile, evangelical middle class in the USA of the 1830s. Instead of beginning in 1816 with the rise of the American Colonization Society and other voluntary reform associations, this study focuses on the roughly two-year period of greatest coordinated effort by 70 abolitionist agents to craft a national antislavery movement, 1836–1838. This brief period coincided with the largest expansion in numbers of local societies and enrollment of new members achieved by the American Anti-Slavery Society (AA-SS). The limited role of women as agents – and their essential role as the most active of recruits – also receives study. Finally, the myriad forms of opposition to the agency, from rowdy mobs to sexual scandal to national economic recession are plumbed as their cumulative effect compelled the AA-SS to discontinue most of their agents' commissions in late 1837.