A new program to compute and evaluate continuously monitored stopping boundaries for clinical trials

We present code for the calculation and evaluation of continuously monitored stopping boundaries for use in one-arm and two-arm clinical trials. These designs were first developed for one-arm trials by Thall, Simon and Estey (TSE) (P.F. Thall, R. Simon, E.H. Estey, Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes, Stat. Med. 14 (1995) 357-379). Our code corrects some problems in the original TSE algorithms and extends these algorithms for use in a two-arm trial setting. It is written in S-Plus to improve interactivity for the statistically adept user, and employs external routines, dynamically loaded into S-Plus, to improve calculation efficiency. Efficient versions of our code require both a C compiler and the S-Plus program. Our code has been tested in UNIX and Microsoft Windows environments, and compiled code is available from our website. A numerical integration routine for the convolution of beta distributions is included.     

Computation of the Uniform Minimum Variance Unbiased Estimator of a Normal Mean Following a Group Sequential Trial

The sampling distribution of data collected in a group sequential trial is such that the usual fixed-sample estimates of treatment effect are biased. Improved estimates can be obtained by taking the group sequential stopping rule into account. In particular, in the case of inference about the mean of a normal distribution, the sample mean is no longer the uniform minimum variance unbiased estimator (UMVUE). In this, I present a way in which the UMVUE for a normal mean can be calculated using software capable of determining the operating characteristics of a group-sequential test.     

Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition

This paper considers the problem of stopping rules, in the context of sequential Bayesian classification. In particular a new criterion, based on the probability of reversal of the obtained classification, is introduced and compared to more commonly used strategies. The results show good behavior of the proposed technique, with both simulated and real data drawn from biomedical application. In fact it appears that this stopping rule reduces the misallocation error rate with the same mean number of used features, or conversely, with an equal level of misallocation error rate, it reduces the mean number of features necessary to attain it.     

Deriving Stopping Rules for the Probabilistic Hough Transform by Sequential Analysis

It is known that Hough transform computation can be significantly accelerated by polling instead of voting. A small part of the data set is selected at random and used as input to the algorithm. The performance of these probabilistic Hough transforms depends on the poll size. Most probabilistic Hough algorithms use a fixed poll size, which is far from optimal since conservative design requires the fixed poll size to be much larger than necessary under average conditions. It has recently been experimentally demonstrated that adaptive termination of voting can lead to improved performance in terms of the error rate versus average poll size tradeoff. However, the lack of a solid theoretical foundation made general performance evaluation and optimal design of adaptive stopping rules nearly impossible. In this paper it is shown that the statistical theory of sequential hypotheses testing can provide a useful theoretical framework for the analysis and development of adaptive stopping rules for the probabilistic Hough transform. The algorithm is restated in statistical terms and two novel rules for adaptive termination of the polling are developed. The performance of the suggested stopping rules is verified using synthetic data as well as real images. It is shown that the extension suggested in this paper to A. Wald's one-sided alternative sequential test (Sequential Analysis,Wiley, New York, 1947) performs better than previously available adaptive (or fixed) stopping rules.     

CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

As dot-com bubble burst in 2002, an uncountable number of small-sized online shopping malls have emerged every day due to many good characteristics of online marketplace, including significantly reduced search costs and menu cost for products or services and easily accessing products or services in the world. However, all the online shopping malls have not continuously flourished. Many of them even vanished because of the lack of customer relationship management (CRM) strategies that fit them. The objective of this paper is to propose CRM strategies for small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customers in terms of recency, frequency and monetary (RFM) value. Then, we developed a model which classifies customers into VIP or non-VIP, using various data mining techniques such as decision tree, artificial neural network, logistic regression and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.     

np1: a computer program for dose escalation strategies in phase I clinical trials

The continual reassessment method is a recommended dose escalation design for including patients in phase I clinical trials designed to estimate the maximum tolerated dose. However, for a particular trial, the implementation of these methods requires extensive computer programming. Standard 3+3 designs do not require this, but have shown to possess poor statistical properties. np1 is a user friendly computer program which has implemented two continual reassessment methods for simulating and conducting a phase I clinical trial. Several options allow the user to investigate operating characteristics under various scenarios.     

基于贝叶斯网的分类器及其在CRM中的应用

基于贝叶斯网的分类器因其对不确定性问题有较强的处理能力，因此在CRM客户建模中有其独特的优势。在对朴素贝叶斯分类器、通用贝叶斯网分类器优缺点分析的基础上，引入增强型BN分类器和贝叶斯多网分类器，详细介绍了后者的算法，并将其应用到实际电信CRM客户．建模中，取得较好的效果。     

Bayesian sample-size determination for two independent Poisson rates

Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist sample-size determination methods, the Bayesian paradigm offers a wide variety of sample-size determination methodologies. Bayesian sample-size determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences. Recently, Bayesian approaches have been used to determine the sample size of a single Poisson rate parameter in a clinical trial setting. In this paper, we extend these results to the comparison of two Poisson rates and develop methods for sample-size determination for hypothesis testing in a Bayesian context. We have created functions in R to determine the parameters for the conjugate gamma prior and calculate the sample size for the average length criterion and average power methods. We also provide two examples that implement our sample-size determination methods using clinical data.     

A two-phased approach to setting due-dates in single machine job shops

This paper explores the characteristics of job flow time distributions resulting from the use of different priority rules via computer simulation and statistical analysis. Statistical analysis of results from phase I experimentation modeling traditional due-date setting practices are used to develop job flow time prediction models. In phase II simulation experiments, the regression models developed in phase I are used to establish due-date allowances to jobs based on their processing times. Forms of the prediction equations reveal the differences in resultant job flow distributions, and explain the underlying nature of interactions between due-date setting methods and shop performance characteristics of dispatching rules as reported in previous studies. Application of the prediction equations in phase II experimentation show the viability of improving shop floor performance without resorting to looser due-dates.     

Team training in the skies: does crew resource management (CRM) training work?

The aviation community has invested great amounts of money and effort into crew resource management (CRM) training. Using D. L. Kirkpatrick's (1976) framework for evaluating training, we reviewed 58 published accounts of CRM training to determine its effectiveness within aviation. Results indicated that CRM training generally produced positive reactions, enhanced learning, and promoted desired behavioral changes. However, we cannot ascertain whether CRM has an effect on an organization's bottom line (i.e., safety). We discuss the state of the literature with regard to evaluation of CRM training programs and, as a result, call for the need to conduct systematic, multilevel evaluation efforts that will show the true effectiveness of CRM training. As many evaluations do not collect data across levels (as suggested by D. L. Kirkpatrick, 1976, and by G. M. Alliger, S. I. Tannenbaum, W. Bennett, Jr., & H. Traver, 1997), the impact of CRM cannot be truly determined; thus more and better evaluations are needed and should be demanded.     

Autonomous rule induction from data with tolerances in customer relationship management

In this paper, application of the rough set theory (RST) to feature selection in customer relationship management (CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. Automated decision support for CRM has been proposed in recent years. However, little work has been devoted to the development of computer-based systems to support CRM in rule induction. This paper presents a novel rough set based algorithm for automated decision support for CRM. Particularly, the approach is capable to handle real numbers instead of integer numbers through introduction of converted numbers involving tolerances. Being unique and useful in solving CRM problems, an alternative rule extraction algorithm (AREA) is presented for discovering preference-based rules according to the reducts which contain the maximum of strength index (SI) in the same case, where the data with tolerance. The empirical data set associated with CRM has proven the validity and reliability of these approaches. This research thus contributes to developing and validating a useful approach to automated decision support for CRM. This paper forms the basis for solving many other similar problems that occur in the service industry.     

由Markov网到Bayesian网

Markov网（马尔可夫网）是类似于Bayesian网（贝叶斯网）的另一种进行不确定性揄的有力工具，Markov网是一个无向图，而Bayesian网是一个有向无环图，发现Markov网不需要发现边的方向，因此要比发现Bayesian网容易得多，提出了一种通过发现Markov网得到等价的Bayesian网的方法，首先利用信息论中验证信息独立的一个重要结论，提出了一个基于依赖分析的边删除算法发现Markov网，该算法需O（n^2)次CI（条件独立）测试，CI测试的时间复杂度取决于由样本数据得到的联合概率函数表的大小，经证明，假如由样本数据得到的联合概率函数严格为正，则该算法发现的Markov网一定是样本的最小L图，由发现Markov网，根据表示的联合概率函数相等，得到与其等价的Bayesian网。     

Some conservative stopping rules for the operational testing ofsafety critical software

Operational testing, which aims to generate sequences of test cases with the same statistical properties as those that would be experienced in real operational use, can be used to obtain quantitative measures of the reliability of software. In the case of safety critical software it is common to demand that all known faults are removed. This means that if there is a failure during the operational testing, the offending fault must be identified and removed. Thus an operational test for safety critical software takes the form of a specified number of test cases (or a specified period of working) that must be executed failure-free. This paper addresses the problem of specifying the numbers of test cases (or time periods) required for a test, when the previous test has terminated as a result of a failure. It has been proposed that, after the obligatory fix of the offending fault, the software should be treated as if it were completely novel, and be required to pass exactly the same test as originally specified. The reasoning here claims to be conservative, in as much as no credit is given for any previous failure-free operation prior to the failure that terminated the test. We show that, in fact, this is not a conservative approach in all cases, and propose instead some new Bayesian stopping rules. We show that the degree of conservatism in stopping rules depends upon the precise way in which the reliability requirement is expressed. We define a particular form of conservatism that seems desirable on intuitive grounds, and show that the stopping rules that exhibit this conservatism are also precisely the ones that seem preferable on other grounds     

Group sequential testing of homogeneity in genetic linkage analysis

Human genetic linkage studies have the objective of testing whether disease genes are linked to genetic markers based on family genetic data. Sometimes, these studies require many years of recruiting informative families and large amount of funds. One way to reduce the required sample size for such studies is to use sequential testing procedures. In this paper, we investigate two group sequential tests for homogeneity in binomial mixture models that are commonly used in genetic linkage analysis. We conduct Monte Carlo simulations to examine the performance of the group sequential procedures. The results show that the proposed group sequential procedures can save, on average, substantial sample size and detect linkage with almost the same power as their nonsequential counterparts.     

聚类分析在电信行业客户关系管理中的应用

客户关系管理在电信行业被广泛应用,它已经成为电信运营商增加收入和利润,提高客户满意度、忠诚度的有效工具。聚类分析是数据挖掘的一种重要方法,将它应用在客户关系管理中,可以改善客户关系,并对将来的趋势和行为进行预测。本文采用k-means聚类算法对电信客户数据进行聚类分析,得到具有不同特征的客户群组,对不同群组采取不同的经营策略,帮助管理者提供了合理的决策支持。     

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.     

Visual and textual fusion for semantically supervised region-based retrieval

This paper presents a unified annotation and retrieval framework, which integrates region annotation with image retrieval for performance reinforcement. To integrate semantic annotation with region-based image retrieval, visual and textual fusion is proposed for both soft matching and Bayesian probabilistic formulations. To address sample insufficiency and sample asymmetry in the annotation classifier training phase, we present a region-level multi-label image annotation scheme based on pair-wise coupling support vector machine (SVM) learning. In the retrieval phase, to achieve semantic-level region matching we present a novel retrieval scheme which differs from former work: the query example uploaded by users is automatically annotated online, and the user can judge its annotation quality. Based on the user’s judgment, two novel schemes are deployed for semantic retrieval: (1) if the user judges the photo to be well annotated, Semantically supervised Integrated Region Matching is adopted, which is a keyword-integrated soft region matching method; (2) If the user judges the photo to be poorly annotated, Keyword Integrated Bayesian Reasoning is adopted, which is a natural integration of a Visual Dictionary in online content-based search. In the relevance feedback phase, we conduct both visual and textual learning to capture the user’s retrieval target. Better annotation and retrieval performance than current methods were reported on both COREL 10,000 and Flickr web image database (25,000 images), which demonstrated the effectiveness of our proposed framework.     

Mining top-<Emphasis Type="Italic">K</Emphasis> frequent itemsets through progressive sampling

We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very frequent (resp., very infrequent) itemsets, together with an estimate of these itemsets’ frequencies with a bounded error. Our first result is an upper bound on the sample size which guarantees that the top-K frequent itemsets mined from a random sample of that size approximate the actual top-K frequent itemsets, with probability larger than a specified value. We show that the upper bound is asymptotically tight when w is constant. Our main algorithmic contribution is a progressive sampling approach, combined with suitable stopping conditions, which on appropriate inputs is able to extract approximate top-K frequent itemsets from samples whose sizes are smaller than the general upper bound. In order to test the stopping conditions, this approach maintains the frequency of all itemsets encountered, which is practical only for small w. However, we show how this problem can be mitigated by using a variation of Bloom filters. A number of experiments conducted on both synthetic and real benchmark datasets show that using samples substantially smaller than the original dataset (i.e., of size defined by the upper bound or reached through the progressive sampling approach) enable to approximate the actual top-K frequent itemsets with accuracy much higher than what analytically proved.