Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance

Monitoring clinical trials in nonfatal diseases where ethical considerations do not dictate early termination upon demonstration of efficacy often requires examining the interim findings to assure that the protocol-specified sample size will provide sufficient power against the null hypothesis when the alternative hypothesis is true. The sample size may be increased, if necessary to assure adequate power. This paper presents a new method for carrying out such interim power evaluations for observations from normal distributions without unblinding the treatment assignments or discernably affecting the Type 1 error rate. Simulation studies confirm the expected performance of the method.     

State,Commune and Gender Inequality Among Teachers in Rural and Small-Town China, 1957–1979

Studies of Maoist China have suggested that workplace gender inequality was greater in rural areas than cities. One possible explanation is that this was a result of the different institutional arrangements that governed rural and urban spaces. Another alternative is that different ideologies were associated with different types of work irrespective of institutional context. This article examines this through an analysis of teachers in Guangdong and Jiangsu. Some teachers were employed by the state in the same way that urban workers were, while others were employed by village-level administrations within communes. Levels of gender inequality were not majorly different between the two groups of teachers. In both groups there was a high degree of pay equality in comparison to other types of work. Other types of gender inequalities existed in teaching, but their effects were mitigated by the unintended consequences of structural changes in education during the Cultural Revolution.     

Emergence of the wrapped Cauchy distribution in mixed directional data

AStA Advances in Statistical Analysis - Inferring the most appropriate distribution (or distributions) to describe observed directional data is important in many applications of circular...     

Adaptation and Strategic Retirement of Secondary School Teachers

Public Organization Review - This study explains strategic retirement amongst Uganda’s secondary school teachers using work adjustment and development theories. Data relating to psychological...     

Asymptotic separability in sensitivity analysis

In an observational study in which each treated subject is matched to several untreated controls by using observed pretreatment covariates, a sensitivity analysis asks how hidden biases due to unobserved covariates might alter the conclusions. The bounds required for a sensitivity analysis are the solution to an optimization problem. In general, this optimization problem is not separable, in the sense that one cannot find the needed optimum by performing a separate optimization in each matched set and combining the results. We show, however, that this optimization problem is asymptotically separable, so that when there are many matched sets a separate optimization may be performed in each matched set and the results combined to yield the correct optimum with negligible error. This is true when the Wilcoxon rank sum test or the Hodges-Lehmann aligned rank test is applied in matching with multiple controls. Numerical calculations show that the asymptotic approximation performs well with as few as 10 matched sets. In the case of the rank sum test, a table is given containing the separable solution. With this table, only simple arithmetic is required to conduct the sensitivity analysis. The method also supplies estimates, such as the Hodges-Lehmann estimate, and confidence intervals associated with rank tests. The method is illustrated in a study of dropping out of US high schools and the effects on cognitive test scores.     

A computationally efficient method for bootstrapping systems of demand equations: A comparison to traditional techniques

The solution to a Liapunov matrix equation (LME) has been proposed to estimate the parameters of the demand equations derived from the Translog, the Almost Ideal Demand System and the Rotterdam demand models. When compared to traditional scemingly unrelated regression (SUR) methods the LME approach saves both computer time and space, and it provides parameter estimates that are less likely to suffer from round-off error. However, the LME method is difficult to implement without the use of specially written computer programs and, unlike traditional SUR methods, it does not automatically provide an estimate of the covariance of the parameters. This paper solves these two problems, the first by providing a simplified solution to the Liapunov matrix equation which can be written in a few lines of code in computer languages such as SAS PROC MATRIX/IML^TM or GAUSS^TM; the second, by bootstrapping the parameter covariance matrix.     

Bayesian variable selection for the Cox regression model with missing covariates

In this paper, we develop Bayesian methodology and computational algorithms for variable subset selection in Cox proportional hazards models with missing covariate data. A new joint semi-conjugate prior for the piecewise exponential model is proposed in the presence of missing covariates and its properties are examined. The covariates are assumed to be missing at random (MAR). Under this new prior, a version of the Deviance Information Criterion (DIC) is proposed for Bayesian variable subset selection in the presence of missing covariates. Monte Carlo methods are developed for computing the DICs for all possible subset models in the model space. A Bone Marrow Transplant (BMT) dataset is used to illustrate the proposed methodology.     

A uniform central limit theorem for neural network-based autoregressive processes with applications to change-point analysis

We consider an autoregressive process with a nonlinear regression function that is modelled by a feedforward neural network. First, we derive a uniform central limit theorem which is useful in the context of change-point analysis. Then, we propose a test for a change in the autoregression function which – by the uniform central limit theorem – has asymptotic power one for a large class of alternatives including local alternatives not restricted to the correctly specified model.     

Teaching Intraverbal Behavior to Children with Autism: A Comparison of Textual and Echoic Prompts

Although echoic prompts may be effective for teaching intraverbal behavior to children with autism, the performance of some children may become dependent on such prompts (i.e., the prompts cannot be eliminated). Recent research suggests that visual rather than echoic prompts may be used to teach children with autism a variety of skills and may facilitate independent performance. In the present study, an adapted alternating treatments design was used to compare the effects of using visual (textual) and echoic prompts on acquisition of intraverbal responses (answering questions) by 2 children with autism. The results indicated that the textual prompts were more effective than the echoic prompts. Implications for the use of visual prompts during instruction with children with autism are discussed.