Differences in Relationship Quality Measurement and Covariate Influence Between Individuals in First and Second Marriages: A Propensity Score Analysis

Few studies have examined relationship quality construct measurement and covariate influence differences between first married and remarried individuals. We used multiple-sample confirmatory factor analysis and structural regression invariance testing to assess relationship quality measurement and covariate influence differences between these two groups. We account for selection bias on 11 observed selection variables by comparing outcomes before and after propensity score greedy matching procedures. Using a sample of 2,030 first married and 488 remarried individuals from the Relationship Evaluation Survey, prematching results indicate some significant differences between groups. Postmatching results, however, indicate that all measurement and structural parameters can be constrained to be equal between groups without worsening model fit. Implications, limitations, and future directions for research are discussed.     

Epidural analgesia during birth and adverse neonatal outcomes: A population-based cohort study

BackgroundIn general, epidural analgesia is considered a safe and efficient way to relieve pain during active labour and is increasingly used in childbirth. It is well documented that epidural analgesia during birth has benefits but also adverse effects. However, evidence is limited on how epidural analgesia influences neonatal outcome in a low-risk population of birthing women.AimTo examine low Apgar score, foetal hypoxia and admission to the neonatal intensive care unit in neonates of low-risk women receiving epidural analgesia during birth.MethodsA cohort study using registry data to investigate a population of 23,272 low-risk women giving birth at a university hospital.ResultsEpidural analgesia was used in 21.6% of low-risk women during birth. Low Apgar score, foetal hypoxia, and admission to the neonatal intensive care unit were found in 0.6%, 0.6%, and 10.0%, respectively in neonates of mothers receiving epidural analgesia during birth compared to 0.3%, 0.6%, and 5.6%, respectively in the non-exposed group. Epidural analgesia was associated with low Apgar score, adjusted odds ratio 1.76 (95% CI 1.07–2.90) and admission to the neonatal intensive care unit, adjusted odds ratio 1.43 (95% CI 1.26–1.62). A mediation analysis indicates the impact of epidural analgesia on adverse neonatal outcomes was mediated by obstetric complications like maternal fever, labour augmentation, and foetal malpresentation.ConclusionThis study found use of epidural analgesia during birth in low-risk pregnant women was associated with infant low Apgar score and admission to the neonatal intensive care unit.     

The Impact of Housing Instability on Child Maltreatment: A Causal Investigation

More than a half million children are confirmed as victims of maltreatment by the child welfare system each year. Children from unstably housed families are over-represented in child maltreatment reports, and a growing body of evidence links housing problems to maltreatment and Child Protective Services (CPS) investigation. The present study applies two propensity score analysis approaches—greedy matching and propensity score weighting—to data from the Fragile Families and Child Well-being Study to move toward a causal explanation of child maltreatment behaviors among mothers in low-income households. Utilizing two separate methods to correct for overt selection bias, the present study finds that housing instability leads to a small increase in maltreatment behaviors, yet this small positive net impact on child maltreatment does not fully explain the over-representation of unstably housed families in the child welfare system. Families experiencing housing problems likely have a range of needs that require earlier, targeted intervention to mitigate consequences of poverty, domestic violence, and maternal depression. Child welfare services should invest resources in housing assistance programs in-house as well as through partnerships with local public housing authorities to stabilize families, reduce housing-related strain on caregivers, and promote family preservation.     

Correction of the p-Value after Multiple Tests in a Cox Proportional Hazard Model

We consider a situation which is common in epidemiology, in which several transformations of an explanatory variable are tried in a Cox model and the most significant test is retained. The p-value should then be corrected to take account of the multiplicity of tests. Bonferroni method is often too conservative because the tests may be highly positively correlated. We propose an asymptotically exact correction of the p-value. The method uses the fact that the tests are asymptotically normal to compute numerically the distribution of the maximum of several tests. Counting processes theory is used to derive estimators of the correlations between tests. The method is illustrated by a simulation and an analysis of the relation between concentration of aluminum in drinking water and risk of dementia.     

Digraph Competitions and Cooperative Games

Digraph games are cooperative TU-games associated to domination structures which can be modeled by directed graphs. Examples come from sports competitions or from simple majority win digraphs corresponding to preference profiles in social choice theory. The Shapley value, core, marginal vectors and selectope vectors of digraph games are characterized in terms of so-called simple score vectors. A general characterization of the class of (almost positive) TU-games where each selectope vector is a marginal vector is provided in terms of game semi-circuits. Finally, applications to the ranking of teams in sports competitions and of alternatives in social choice theory are discussed.     

论乐队指挥

指挥是乐队的组织者、领导者 ,是乐队的灵魂。他参与并领导乐队从选定演奏曲目到演出的全过程。其中排练是一项最难、最繁重的工作。排练效果的好坏 ,不但直接影响到演出的质量 ,也关系到乐队的长期建设。而音乐会演出则是音乐艺术真正实现社会价值的重要活动形式。音乐会演出是包括指挥在内的所有乐队成员最重要的工作任务。     

Evaluating second-order probability judgments with strictly proper scoring rules

Empirical studies have demonstrated that uncertainty about event probabilities, also known as ambiguity or second-order uncertainty, can affect decision makers choice preferences. Despite the importance of second-order uncertainty in decision making, almost no effort has been directed towards the development of methods that evaluate the accuracy of second-order probabilities. In this paper, we describe conditions under which strictly proper scoring rules can be used to assess the accuracy of second-order probability judgments. We investigate the effectiveness of using a particular strictly proper scoring rule the ranked probability score - to discourage biased assessments of second-order uncertainty.     

风险投资后期进入对企业创新绩效的影响研究--选择效应抑或增值效应?

由于上市带来的财富效应高于培育创新型企业的增值效应,中国资本市场信息不对称高,知识产权保护较弱,现阶段中国风险投资主要集中于创新型企业后期进入。本文首先用描述性统计揭示了中国风险投资后期进入创新型企业的特征;其次,阐述了中国风险投资后期进入对企业创新选择效应和增值效应的影响机理;最后,以创业板2009-2014年上市企业为例,采用双重差分倾向得分匹配法(PSM-DID),验证风险投资后期进入对创新型企业研发投入和研发绩效的影响,研究结果表明:后期进入的风险投资进入创新型企业前,对研发投入具有显著的选择效应,但对专利申请量的选择效应不显著;后期进入的风险投资进入后,对研发投入和专利申请量的增值效应均不显著。     

Doubly robust empirical likelihood inference in covariate-missing data problems

Missing covariate data occurs often in regression analysis. We study methods for estimating the regression coefficients in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Robins et al. [Estimation of regression coefficients when some regressors are not always observed. J Amer Statist Assoc. 1994;89:846–866] on regression analyses with missing covariates, in which they pioneered the use of two working models, the working propensity score model and the working conditional score model. A recent approach to missing covariate data analysis is the empirical likelihood method of Qin et al. [Empirical likelihood in missing data problems. J Amer Statist Assoc. 2009;104:1492–1503], which effectively combines unbiased estimating equations. In this paper, we consider an alternative likelihood approach based on the full likelihood of the observed data. This full likelihood-based method enables us to generate estimators for the vector of the regression coefficients that are (a) asymptotically equivalent to those of Qin et al. [Empirical likelihood in missing data problems. J Amer Statist Assoc. 2009;104:1492–1503] when the working propensity score model is correctly specified, and (b) doubly robust, like the augmented inverse probability weighting (AIPW) estimators of Robins et al. [Estimation of regression coefficients when some regressors are not always observed. J Am Statist Assoc. 1994;89:846–866]. Thus, the proposed full likelihood-based estimators improve on the efficiency of the AIPW estimators when the working propensity score model is correct but the working conditional score model is possibly incorrect, and also improve on the empirical likelihood estimators of Qin, Zhang and Leung [Empirical likelihood in missing data problems. J Amer Statist Assoc. 2009;104:1492–1503] when the reverse is true, that is, the working conditional score model is correct but the working propensity score model is possibly incorrect. In addition, we consider a regression method for estimation of the regression coefficients when the working conditional score model is correctly specified; the asymptotic variance of the resulting estimator is no greater than the semiparametric variance bound characterized by the theory of Robins et al. [Estimation of regression coefficients when some regressors are not always observed. J Amer Statist Assoc. 1994;89:846–866]. Finally, we compare the finite-sample performance of various estimators in a simulation study.