When is n large enough? Looking for the right sample size to estimate proportions

The central limit theorem indicates that when the sample size goes to infinite, the sampling distribution of means tends to follow a normal distribution; it is the basis for the most usual confidence interval and sample size formulas. This study analyzes what sample size is large enough to assume that the distribution of the estimator of a proportion follows a Normal distribution. Also, we propose the use of a correction factor in sample size formulas to ensure a confidence level even when the central limit theorem does not apply for these distributions.     

Effects of Firm-specific and Country-specific Advantages on Relative Acquisition Size in Service Sector Cross-Border Acquisitions: An Empirical Examination

Using a sample of 348 service sector cross-border acquisitions by U.S. firms in 44 countries during 1990–2006, our study seeks to identify factors that influence relative acquisition size (acquisition transaction value as a percentage of acquiring firm's asset value). Our findings indicate that firm-specific advantages (FSAs) in the form of available financial slack and target industry knowledge were positively associated with relative acquisition size. However, contrary to expectations, we observed a negative relationship between cross-border acquisition experience and relative acquisition size. In addition, our results suggest that country-specific advantages (CSAs) associated with higher market potential, lower political risk, and greater cultural similarity contributed to increased relative acquisition size in service industry cross-border acquisitions. Finally, our analysis reveals that the relationship between available financial slack and relative acquisition size is contingent on cultural similarity with the relationship being more pronounced when cultural similarity is high.     

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.     

Blinded continuous monitoring in clinical trials with recurrent event endpoints

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.     

Modelling period fertility: Schooling and intervals following a birth in Eastern Africa

We describe a regression-based approach to the modelling of age-, order-, and duration-specific period fertility, using retrospective survey data. The approach produces results that are free of selection biases and can be used to study differential fertility. It is applied to Demographic and Health Survey data for Ethiopia, Kenya, Tanzania, and Zimbabwe to investigate differential trends in fertility by education. Parity progression fell and the intervals following each birth lengthened between the 1970s and 2000s in all four countries. Fertility fell most among women with secondary education. In contrast to other world regions, postponement of successive births for extended periods accounted for much of the initial drop in fertility in these African countries. However, family size limitation by women with secondary education in Ethiopia and Kenya and longer birth spacing in Zimbabwe also played significant roles. Thus, birth control is being adopted in Eastern Africa in response to diverse changes in fertility preferences.     

Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.     

The middling mobile: finding place in the liquid city

The middling mobile, differentiated by their modest and unsure mobility, are a vast of bulk of people who intend or expect to move on. As they travel, they tread differing intensities of rhythm and social embeddedness felt to be chaotic, constraining, liberating, or comforting. Owing to their aspirations to ‘get out’ of a rhythmic life, or to move somewhere familiar, the middling mobile use movement and rootedness to navigate and manage everyday life in the liquid city. My analysis offers a holistic exposition of a life that flows between mobility/immobility and rhythmic/arrhythmic forms, and the transitions between them.     

自驾偏好、居民异质与居住选址——基于单中心城市模型的空间均衡分析

基于线性单中心城市模型，研究了自驾偏好和居民拥有车辆异质性对双方式交通走廊沿线居民出行方式选择和居住区位选址均衡的影响.将有车居民对自驾出行的偏好集成到直接效用函数中，建立了空间均衡模型.分析发现，当城市居民全部自驾出行时，租金-距离可能呈现先提高后降低的趋势.考虑只有部分居民拥有自驾车辆的一般情形，推导出了有车和无车居民在城市走廊沿线居住分布的不同均衡模式.通过理论解析，进一步考察了小汽车保有量和城市居民数量的变化对城市空间结构和居民效用水平的影响.     

Bayesian sample‐size determination methods considering both worthwhileness and unpromisingness for exploratory two‐arm randomized clinical trials with binary endpoints

A randomized exploratory clinical trial comparing an experimental treatment with a control treatment on a binary endpoint is often conducted to make a go or no‐go decision. Such an exploratory trial needs to have an adequate sample size such that it will provide convincing evidence that the experimental treatment is either worthwhile or unpromising relative to the control treatment. In this paper, we propose three new sample‐size determination methods for an exploratory trial, which utilize the posterior probabilities calculated from predefined efficacy and inefficacy criteria leading to a declaration of the worthwhileness or unpromisingness of the experimental treatment. Simulation studies, including numerical investigation, showed that all three methods could declare the experimental treatment as worthwhile or unpromising with a high probability when the true response probability of the experimental treatment group is higher or lower, respectively, than that of the control treatment group.