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.     

Stress-strength reliability of exponentiated Fréchet distributions based on Type-II censored data

In this paper, we consider inference of the stress-strength parameter, R, based on two independent Type-II censored samples from exponentiated Fréchet populations with different index parameters. The maximum likelihood and uniformly minimum variance unbiased estimators, exact and asymptotic confidence intervals and hypotheses testing for R are obtained. We conduct a Monte Carlo simulation study to evaluate the performance of these estimators and confidence intervals. Finally, two real data sets are analysed for illustrative purposes.     

New evidence about effects of reproductive variables on child mortality in sub-Saharan Africa

There is still considerable uncertainty about how reproductive factors affect child mortality. This study, based on Demographic and Health Survey data from 28 countries in sub-Saharan Africa, shows that mortality is highest for firstborn children with very young mothers. Other children with young mothers, or of high birth order, also experience high mortality. Net of maternal age and birth order, a short preceding birth interval is associated with above average mortality. These patterns change, however, if time-invariant unobserved mother-level characteristics of importance for both mortality and fertility are controlled for in a multilevel–multiprocess model. Most importantly, there are smaller advantages associated with longer birth intervals and being older at first birth. The implications of alternative reproductive ‘strategies’ are discussed, taking into account that if the mother is older at birth, the child will also be born in a later calendar year, when mortality may be lower.     

一种简单有效的二项分布比例参数似然比置信区间的求法

对二项分布比例参数p的似然比置信区间,提出一种简便求解方法。在平均覆盖率、平均区间长度及区间长度的95%置信区间准则下与WScore、Plus4、Jeffreys置信区间进行模拟比较。试验表明,在二项分布b(n,p)的参数n≥20且p∈(0.1,0.9)时,该方法获取的似然比置信区间性能优良。当点估计p值不是接近于0或1且n≥20时,推荐使用本方法获取p的置信区间。     

Enabling robust assessment of QTc prolongation in early phase clinical trials

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a “thorough QTc” (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration‐QTc (conc‐QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an “E14 Q&As (R3)” document supporting the use of conc‐QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc‐QTc assessment methods for typical first‐in‐human crossover‐like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc‐QTc assessment will further enable highly reliable go/no‐go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.     

On construction of asymptotically correct confidence intervals

In this paper we discuss constructing confidence intervals based on asymptotic generalized pivotal quantities (AGPQs). An AGPQ associates a distribution with the corresponding parameter, and then an asymptotically correct confidence interval can be derived directly from this distribution like Bayesian or fiducial interval estimates. We provide two general procedures for constructing AGPQs. We also present several examples to show that AGPQs can yield new confidence intervals with better finite-sample behaviors than traditional methods.     

Exact average coverage probabilities and confidence coefficients of confidence intervals for discrete distributions

For a confidence interval (L(X),U(X)) of a parameter θ in one-parameter discrete distributions, the coverage probability is a variable function of θ. The confidence coefficient is the infimum of the coverage probabilities, inf  _θ P _θ (θ∈(L(X),U(X))). Since we do not know which point in the parameter space the infimum coverage probability occurs at, the exact confidence coefficients are unknown. Beside confidence coefficients, evaluation of a confidence intervals can be based on the average coverage probability. Usually, the exact average probability is also unknown and it was approximated by taking the mean of the coverage probabilities at some randomly chosen points in the parameter space. In this article, methodologies for computing the exact average coverage probabilities as well as the exact confidence coefficients of confidence intervals for one-parameter discrete distributions are proposed. With these methodologies, both exact values can be derived.     

论青年女性流动人口初育间隔与人口控制——来自厦门流动人口家庭问卷调查的启示

青年女性流动人口实际生育行为的控制对于人口规模与结构的控制,起着至关重要的作用。而生育时间的选择作为影响流动人口生育实践的一个重要因素,体现在对各孩次生育间隔的控制上。本文在描述分析的基础上,应用Cox比例风险模型,主要对厦门市青年女性流动人口初育间隔的影响因素进行估计和检验,并在总结分析的基础上对流动人口控制问题提出几点对策思考。     

Can Likert Scales be Treated as Interval Scales?—A Simulation Study

The Likert scale is widely used in social work research, and is commonly constructed with four to seven points. It is usually treated as an interval scale, but strictly speaking it is an ordinal scale, where arithmetic operations cannot be conducted. There are pros and cons in using the Likert scale as an interval scale, but the controversy can be handled by increasing the number of points. Several researchers have suggested bringing the number up to eleven, on the basis of empirical data. In this article the authors explore this rational and share the same view, but simulate artificial data from both symmetrical normal and skewed distributions where the underlying metric is known in advance. Results show that more Likert scale points will result in a closer approach to the underlying distribution, and hence normality and interval scales. To increase generalizability social work practitioners are encouraged to use 11-point Likert scales from 0 to 10, a natural and easily comprehensible range.