Estimating the population proportion in modified ranked set sampling methods

In this paper, proportion estimators and associated variance estimators are proposed for a binary variable with a concomitant variable based on modified ranked set sampling methods, which are extreme ranked set sampling (ERSS), median ranked set sampling (MRSS), percentile ranked set sampling (Per-RSS) and L ranked set sampling (LRSS) methods. The Monte Carlo simulation study is performed to compare the performance of the estimators based on bias, mean squared error, and relative efficiency for different levels of correlation coefficient, set and cycle sizes under normal and log-normal distributions. Moreover, the study is supported with real data application.     

Optimal design of repetitive group sampling plans for Weibull and gamma distributions with applications and comparison to the Birnbaum–Saunders distribution

In this paper, we propose a multiple deferred state repetitive group sampling plan which is a new sampling plan developed by incorporating the features of both multiple deferred state sampling plan and repetitive group sampling plan, for assuring Weibull or gamma distributed mean life of the products. The quality of the product is represented by the ratio of true mean life and specified mean life of the products. Two points on the operating characteristic curve approach is used to determine the optimal parameters of the proposed plan. The plan parameters are determined by formulating an optimization problem for various combinations of producer's risk and consumer's risk for both distributions. The sensitivity analysis of the proposed plan is discussed. The implementation of the proposed plan is explained using real-life data and simulated data. The proposed plan under Weibull distribution is compared with the existing sampling plans. The average sample number (ASN) of the proposed plan and failure probability of the product are obtained under Weibull, gamma and Birnbaum–Saunders distributions for a specified value of shape parameter and compared with each other. In addition, a comparative study is made between the ASN of the proposed plan under Weibull and gamma distributions.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

Financial liberalization and long-run stability of money demand in Nigeria

A stable money demand function is essential when using monetary aggregate as a monetary policy. Thus, there is need to examine the stability of the money demand function in Nigeria after the deregulation of the financial sector. To achieve this, the study employed CUSUM (cumulative sum) and CUSUMSQ (CUSUM of square) tests after using autoregressive distributive lag bounds test to determine the existence of a long run relationship between monetary aggregates and their determinants. Results of the study show that a long-run relationship holds and that the demand for money is stable in Nigeria. In addition, the inflation rate is found to be a better proxy for an opportunity variable when compared to interest rate. The main implication of the study is that interest rate is ineffective as a monetary policy instrument in Nigeria.     

Likelihood ratio tests for multivariate normality

This paper presents some powerful omnibus tests for multivariate normality based on the likelihood ratio and the characterizations of the multivariate normal distribution. The power of the proposed tests is studied against various alternatives via Monte Carlo simulations. Simulation studies show our tests compare well with other powerful tests including multivariate versions of the Shapiro–Wilk test and the Anderson–Darling test.     

基于修正UTAUT模型的农信社手机银行使用行为研究——以福建省农户调查数据为例

基于UTAUT模型,引入感知风险、替代软件等2个变量进行修正,构建农信社手机银行使用行为影响因素研究模型,并运用结构方程模型、Bootstrap中介检验、多群组分析等方法对福建省农户调查数据进行实证分析。研究发现:(1)感知易用性显著正向影响农信社手机银行使用意愿;感知风险和替代软件显著负向影响农信社手机银行使用意愿;社会影响对农信社手机银行使用意愿影响不显著。(2)便利条件和使用意愿显著正向影响农信社手机银行使用行为;农信社手机银行使用行为影响因素由大到小依次为便利条件、替代软件、感知风险、感知易用性。(3)教育在感知易用性对农信社手机银行使用意愿影响中具有调节作用。据此提出,应进一步增强操作的便利性以优化业务办理效率,增强信息的安全性以降低软件使用风险,增强软件的特色性以降低软件可替代性,增强软件的易用性以优化便捷使用体验,从而优化农村金融服务供给,助推农村经济发展。     

An update on ‘a comparative study of tests for homogeneity of variance’

Tests for equality of variances using independent samples are widely used in data analysis. Conover et al. [A comparative study of tests for homogeneity of variance, with applications to the outer continental shelf bidding data. Technometrics. 1981;23:351–361], won the Youden Prize by comparing 56 variations of popular tests for variance on the basis of robustness and power in 60 different scenarios. None of the tests they compared were robust and powerful for the skewed distributions they considered. This study looks at 12 variations they did not consider, and shows that 10 are robust for the skewed distributions they considered plus the lognormal distribution, which they did not study. Three of these 12 have clearly superior power for skewed distributions, and are competitive in terms of robustness and power for all of the distributions considered. They are recommended for general use based on robustness, power, and ease of application.     

Statistical considerations in clinical trial design with event-free survival as the primary efficacy endpoint

In late-phase confirmatory clinical trials in the oncology field, time-to-event (TTE) endpoints are commonly used as primary endpoints for establishing the efficacy of investigational therapies. Among these TTE endpoints, overall survival (OS) is always considered as the gold standard. However, OS data can take years to mature, and its use for measurement of efficacy can be confounded by the use of post-treatment rescue therapies or supportive care. Therefore, to accelerate the development process and better characterize the treatment effect of new investigational therapies, other TTE endpoints such as progression-free survival and event-free survival (EFS) are applied as primary efficacy endpoints in some confirmatory trials, either as a surrogate for OS or as a direct measure of clinical benefits. For evaluating novel treatments for acute myeloid leukemia, EFS has been gradually recognized as a direct measure of clinical benefits. However, the application of an EFS endpoint is still controversial mainly due to the debate surrounding definition of treatment failure (TF) events. In this article, we investigate the EFS endpoint with the most conservative definition for the timing of TF, which is Day 1 since randomization. Specifically, the corresponding non-proportional hazard pattern of the EFS endpoint is investigated with both analytical and numerical approaches.     

Multivariate Approach for Comparative Evaluations of Customer Satisfaction with Application to Transport Services

In problems related to evaluations of products or services (e.g. in customer satisfaction analysis) the main difficulties concern the synthesis of the information, which is necessary for the presence of several evaluators and many response variables (aspects under evaluation). In this article, the problem of determining and comparing the satisfaction of different groups of customers, in the presence of multivariate response variables and using the results of pairwise comparisons is addressed. Within the framework of group ranking methods and multi criteria decision making theory, a new approach, based on nonparametric techniques, for evaluating group satisfaction in a multivariate framework is proposed and the concept of Multivariate Relative Satisfaction is defined. An application to the evaluation of public transport services, like the railway service and the urban bus service, by students of the University of Ferrara (Italy) is also discussed.