Division of Household Labor and Marital Satisfaction in China, Japan, and Korea

In this study, we compared the association of marital satisfaction with the division of household labor in China, Japan, and Korea. Results revealed that wives’ marital satisfaction was negatively associated with their burden of housework in the three Asian countries, as generally observed in Western countries. However, there were noticeable cross-country differences. Chinese couples were relatively in favor of an egalitarian division of household labor. Japanese couples were supportive of traditional specialization, with wives flexibly shifting their efforts between work outside the house and housework. Korean couples were under pressure from conflicts between the wife’s labor force participation and the traditional division of labor. These findings underscored the importance of the socio-institutional context in the study of marital satisfaction.     

Gender,Parental Control,and Risk Preferences: Evidence from Japanese College Students

Power-control theory, in its original formulation, links patriarchal family structures to parental control, and then to gender differences in risk preference. In previous studies, most of which have been conducted in North America, direct tests of the links have shown that parental control is a key mediating variable explaining the relationship between gender and risk preferences for those raised in patriarchal families. With data from a sample of college students in Japan, the present study tests the hypothesis that the tendency for females to have a lower affinity for risks, globally defined, than males is attributable to different patterns of parental control imposed on daughters compared to sons. We have found that female students have a significantly lower penchant for risk than males, but that such a difference cannot be explained by gender differences in the intensity of instrumental kinds of parental controls, neither mothers' nor fathers' supervision and surveillance.     

Bayesian adaptive patient enrollment restriction to identify a sensitive subpopulation using a continuous biomarker in a randomized phase 2 trial

With the development of molecular targeted drugs, predictive biomarkers have played an increasingly important role in identifying patients who are likely to receive clinically meaningful benefits from experimental drugs (i.e., sensitive subpopulation) even in early clinical trials. For continuous biomarkers, such as mRNA levels, it is challenging to determine cutoff value for the sensitive subpopulation, and widely accepted study designs and statistical approaches are not currently available. In this paper, we propose the Bayesian adaptive patient enrollment restriction (BAPER) approach to identify the sensitive subpopulation while restricting enrollment of patients from the insensitive subpopulation based on the results of interim analyses, in a randomized phase 2 trial with time‐to‐endpoint outcome and a single biomarker. Applying a four‐parameter change‐point model to the relationship between the biomarker and hazard ratio, we calculate the posterior distribution of the cutoff value that exhibits the target hazard ratio and use it for the restriction of the enrollment and the identification of the sensitive subpopulation. We also consider interim monitoring rules for termination because of futility or efficacy. Extensive simulations demonstrated that our proposed approach reduced the number of enrolled patients from the insensitive subpopulation, relative to an approach with no enrollment restriction, without reducing the likelihood of a correct decision for next trial (no‐go, go with entire population, or go with sensitive subpopulation) or correct identification of the sensitive subpopulation. Additionally, the four‐parameter change‐point model had a better performance over a wide range of simulation scenarios than a commonly used dichotomization approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Dependence structures and asymptotic properties of Baker's distributions with fixed marginals

We investigate the properties of Baker's (2008) bivariate distributions with fixed marginals and their multivariate extensions. The properties include the weak convergence to the Fréchet–Hoeffding upper bound, the product-moment convergence, as well as the dependence structures TP₂ (totally positive of order 2), or MTP₂ (multivariate TP₂). In proving the weak convergence, a generalized local limit theorem for binomial distribution is provided.     

Regression Diagnostics of the Semiparametric Proportional Rate Model for Irregularly Spaced Repeated Measurements

Counting process techniques have been successfully introduced to semiparametric inference of repeated measurements. Cheng and Wei (2000 Cheng , S. C. , Wei , L. J. ( 2000 ). Inference for a semiparametric model with panel data . Biometrika 87 : 89 – 97 .[Crossref], [Web of Science ®] , [Google Scholar]) proposed a simple inference procedure for the semiparametric proportional rate model, which reduces to relative risk regression models for binary data. While the baseline mean functions are completely unspecified, it still requires several assumptions for valid inference. In this article, a goodness-of-fit test for it is proposed based on cumulative residuals. Theoretical justification is provided and an illustration with a dataset from a clinical trial is given. Results of simulation studies to evaluate finite sample performance are also provided.     

Estimation of treatment effects based on possibly misspecified Cox regression

In randomized clinical trials, a treatment effect on a time-to-event endpoint is often estimated by the Cox proportional hazards model. The maximum partial likelihood estimator does not make sense if the proportional hazard assumption is violated. Xu and O'Quigley (Biostatistics 1:423-439, 2000) proposed an estimating equation, which provides an interpretable estimator for the treatment effect under model misspecification. Namely it provides a consistent estimator for the log-hazard ratio among the treatment groups if the model is correctly specified, and it is interpreted as an average log-hazard ratio over time even if misspecified. However, the method requires the assumption that censoring is independent of treatment group, which is more restricted than that for the maximum partial likelihood estimator and is often violated in practice. In this paper, we propose an alternative estimating equation. Our method provides an estimator of the same property as that of Xu and O'Quigley under the usual assumption for the maximum partial likelihood estimation. We show that our estimator is consistent and asymptotically normal, and derive a consistent estimator of the asymptotic variance. If the proportional hazards assumption holds, the efficiency of the estimator can be improved by applying the covariate adjustment method based on the semiparametric theory proposed by Lu and Tsiatis (Biometrika 95:679-694, 2008).     

Tracing wealth,cooperation and trust: A comparison of two great plains communities

This research examines the relationships between wealth, cooperation, and trust. Utilizing implications from the social capital literature and democratic theory, we found that trust directly affects patterns of socio-economic interactions, especially shopping. We also found that commuting was widespread and it created a rural sprawl deficit that affected trust. Specifically, the results indicated that trust of others in the community (generalized to a certain degree) tended to encourage people to shop more in town, thus contributing to the development of the community, even when controlling for commuting. Findings from this study suggest that further community level research will yield more specifics about how trust (and social capital) works to increase wealth in a community.     

A Bayesian hierarchical mixture model for platelet-derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer

Summary.  Advances in understanding the biological underpinnings of many cancers have led increasingly to the use of molecularly targeted anticancer therapies. Because the platelet-derived growth factor receptor (PDGFR) has been implicated in the progression of prostate cancer bone metastases, it is of great interest to examine possible relationships between PDGFR inhibition and therapeutic outcomes. We analyse the association between change in activated PDGFR (phosphorylated PDGFR) and progression-free survival time based on large within-patient samples of cell-specific phosphorylated PDGFR values taken before and after treatment from each of 88 prostate cancer patients. To utilize these paired samples as covariate data in a regression model for progression-free survival time, and be cause the phosphorylated PDGFR distributions are bimodal, we first employ a Bayesian hierarchical mixture model to obtain a deconvolution of the pretreatment and post-treatment within-patient phosphorylated PDGFR distributions. We evaluate fits of the mixture model and a non-mixture model that ignores the bimodality by using a supnorm metric to compare the empirical distribution of each phosphorylated PDGFR data set with the corresponding fitted distribution under each model. Our results show that first using the mixture model to account for the bimodality of the within-patient phosphorylated PDGFR distributions, and then using the posterior within-patient component mean changes in phosphorylated PDGFR so obtained as covariates in the regression model for progression-free survival time, provides an improved estimation.     

Mantel-Haenszel estimators for irregular sparse K2×J tables

This paper deals with sparse K2×J(J>2)$K2\times J(J>2)$ tables. Projection-method Mantel–Haenszel (MH) estimators of the common odds ratios have been proposed for K2×J$K2\times J$ tables, which include Greenland's generalized MH estimator as a special case. The method projects log-transformed MH estimators for all K2×2$K2\times 2$ subtables, which were called naive MH estimators, onto a linear space spanned by log odds ratios. However, for sparse tables it is often the case that naive MH estimators are unable to be computed. In this paper we introduce alternative naive MH estimators using a graph that represents K2×J$K2\times J$ tables, and apply the projection to these alternative estimators. The idea leads to infinitely many reasonable estimators and we propose a method to choose the optimal one by solving a quadratic optimization problem induced by the graph, where some graph-theoretic arguments play important roles to simplify the optimization problem. An illustration is given using data from a case–control study. A simulation study is also conducted, which indicates that the MH estimator tends to have a smaller mean squared error than the MH estimator previously suggested and the conditional maximum likelihood estimator for sparse tables.