Matrix decomposition in meta-analysis for extraction of adverse event pattern and patient-level safety profile

The purpose of assessing adverse events (AEs) in clinical studies is to evaluate what AE patterns are likely to occur during treatment. In contrast, it is difficult to specify which of these patterns occurs in each patient. To tackle this challenging issue, we constructed a new statistical model including nonnegative matrix factorization by incorporating background knowledge of AE-specific structures such as severity and drug mechanism of action. The model uses a meta-analysis framework for integrating data from multiple clinical studies because insufficient information is derived from a single trial. We demonstrated the proposed method by applying it to real data consisting of three Phase III studies, two mechanisms of action, five anticancer treatments, 3317 patients, 848 AE types, and 99,546 AEs. The extracted typical treatment-specific AE patterns coincided with medical knowledge. We also demonstrated patient-level safety profiles using the data of AEs that were observed by the end of the second cycle.     

A Short-term Longitudinal Study of Children's Playground Games in Primary School: Implications for Adjustment to School and Social Adjustment in the USA and the UK

In this study we described development of boys’ and girls’ games on the school playground at recess as they progressed across the first year of primary school in London (UK) and Minneapolis (USA) in order to document age‐related trends in games during this period and to examine the predictive role of games in children's adjustment to school and more general social adjustment. Consistent with expectations, games accounted for a significant portion of children’s, but especially boys’, behavior during recess. American children played games more frequently, especially simple games, relative to English children. Consistent with expectation, the complexity of games increased, especially for boys, across the school year for children in both countries. We also examined the extent to which facility with games forecasted adjustment to school and social adjustment. Games predicted both, but only in the USA sample. Results are discussed in terms of games being an important developmental task for children of this age. Implications for future research and educational policy are discussed.     

Tax reform and endogenous gender bargaining power

The conventional studies with unitary representation of the household have been seeking for the condition in which tax systems achieve improvements of both fertility and female labor supply. Such analyses may not be enough to capture the important aspects of family behaviors since their common-preference assumption is frequently inconsistent with observed economies. By making use of a family bargaining framework, we argue that the heterogeneity in parental preferences among household members influences the result of existing studies on tax systems. Our results show that when women prefer larger family size than men, the tax reform towards individual taxation raises the fertility rate even more. In this case, female labor supply can be still increased in spite of enhanced fertility due to bargaining allocation.     

An adaptive biomarker basket design in phase II oncology trials

The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.     

Fertility and endogenous gender bargaining power

We develop an intra-household bargaining model to examine the feedback effect of household fertility decisions on gender bargaining power. In our model, the household balance of power is endogenously determined reflecting social interactions, i.e., the fertility choices by the other couples in society. We show the presence of multiple equilibria in fertility outcome: one equilibrium characterized by patriarchal society with a high fertility rate, and another in which women are sufficiently empowered and the fertility rate is low. In other circumstances, this study also demonstrates a positive relationship between female wage rates and the fertility outcomes. Finally, we discuss its policy implications, comparing the effects of two family policies: the child allowance and the subsidies for market childcare.     

A note on the orthant probability of the elliptically contoured distribution

When a scale matrix satisfies certain conditions, the orthant probability of the elliptically contoured distribution with the scale matrix is expressed as the same probability of the equicorrelated normal distribution.     

Coalitionally strategy-proof rules in allotment economies with homogeneous indivisible goods

We consider the allotment problem of homogeneous indivisible goods among agents with single-peaked and risk-averse von Neumann–Morgenstern expected utility functions. We establish that a rule satisfies coalitional strategy-proofness, same-sideness, and strong symmetry if and only if it is the uniform probabilistic rule. By constructing an example, we show that if same-sideness is replaced by respect for unanimity, this statement does not hold even with the additional requirements of no-envy, anonymity, at most binary, peaks-onlyness and continuity. We would like to thank the associate editor and two anonymous referees whose comments and suggestions significantly improve this paper. We are also grateful to Masaki Aoyagi, Kazuhiko Hashimoto, Hervé Moulin, Hiroo Sasaki, Koji Takamiya, William Thomson, Takuma Wakayama as well as other participants at the Eighth International Meeting of the Society for Social Choice and Welfare in Istanbul, the 2006 Autumn Annual Meeting of the Japanese Economic Association in Osaka, and Yokohama National University Seminar of Economic Theory for their helpful comments. We acknowledge financial supports from the Japan Society for the Promotion of Science via the Research Fellowship for Young Scientist (Hatsumi) and the Grant-in-Aid for Scientific Research (Serizawa).     

Benchmarked linear shrinkage prediction in the Fay–Herriot small area model

The empirical best linear unbiased predictor (EBLUP) is a linear shrinkage of the direct estimate toward the regression estimate and useful for the small area estimation in the sense of increasing precision of estimation of small area means. However, one potential difficulty of EBLUP is that the overall estimate for a larger geographical area based on a sum of EBLUP is not necessarily identical to the corresponding direct estimate like the overall sample mean. To fix this problem, the paper suggests a new method for benchmarking EBLUP in the Fay–Herriot model without assuming normality of random effects and sampling errors. The resulting benchmarked empirical linear shrinkage (BELS) predictor has novelty in the sense that coefficients for benchmarking are adjusted based on the data from each area. To measure the uncertainty of BELS, the second-order unbiased estimator of the mean squared error is derived.     

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.