Analysis of Survival Data Having Time-Dependent Covariates

Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the optimum number of hidden units and testing the goodness-of-fit. The proposed methods are illustrated using data from a long-term study of patients with primary biliary cirrhosis (PBC).      

Black-Box Correctness Tests for Basic Parallel Data Structures

Operations on basic data structures such as queues, priority queues, stacks, and counters can dominate the execution time of a parallel program due to both their frequency and their coordination and contention overheads. There are considerable performance payoffs in developing highly optimized, asynchronous, distributed, cache-conscious, parallel implementations of such data structures. Such implementations may employ a variety of tricks to reduce latencies and avoid serial bottlenecks, as long as the semantics of the data structure are preserved. The complexity of the implementation and the difficulty in reasoning about asynchronous systems increases concerns regarding possible bugs in the implementation. In this paper we consider postmortem, black-box procedures for testing whether a parallel data structure behaved correctly. We present the first systematic study of algorithms and hardness results for such testing procedures, focusing on queues, priority queues, stacks, and counters, under various important scenarios. Our results demonstrate the importance of selecting test data such that distinct values are inserted into the data structure (as appropriate). In such cases we present an O(n) time algorithm for testing linearizable queues, an O(n log n) time algorithm for testing linearizable priority queues, and an O( np ² ) time algorithm for testing sequentially consistent queues, where n is the number of data structure operations and p is the number of processors. In contrast, we show that testing such data structures for executions with arbitrary input values is NP-complete. Our results also help clarify the thresholds between scenarios that admit polynomial time solutions and those that are NP-complete. Our algorithms are the first nontrivial algorithms for these problems.     

Identifying Error-Prone Software—An Empirical Study

A major portion of the effort expended in developing commercial software today is associated with program testing. Schedule and/ or resource constraints frequently require that testing be conducted so as to uncover the greatest number of errors possible in the time allowed. In this paper we describe a study undertaken to assess the potential usefulness of various product-and process-related measures in identifying error-prone software. Our goal was to establish an empirical basis for the efficient utilization of limited testing resources using objective, measurable criteria. Through a detailed analysis of three software products and their error discovery histories, we have found simple metrics related to the amount of data and the structural complexity of programs to be of value for this purpose.     

Computing P-values for a class of permutation tests of equal survival functions

Dallas and Rao (Biometrics 56 (2000) 154) proposed a class of permutation tests for testing the equality of two survival distributions based on randomly right censored survival time data consisting of both paired and unpaired observations. Data sets of this type can occur frequently in medical settings. Two members of this class were advocated for use due to their generally high power for detecting scale and location shifts in the exponential and log-logistic distributions for the survival times, and improved power over paired data test procedures that disregard unpaired observations. Because the computations for the tests become quite laborious as the sample sizes increase, computing routines are required for practical implementation of these tests. This paper provides computing routines to execute the tests.     

Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data

According to the American Cancer Society report (1999), cancer surpasses heart disease as the leading cause of death in the United States of America (USA) for people of age less than 85. Thus, medical research in cancer is an important public health interest. Understanding how medical improvements are affecting cancer incidence, mortality and survival is critical for effective cancer control. In this paper, we study the cancer survival trend on the population level cancer data. In particular, we develop a parametric Bayesian joinpoint regression model based on a Poisson distribution for the relative survival. To avoid identifying the cause of death, we only conduct analysis based on the relative survival. The method is further extended to the semiparametric Bayesian joinpoint regression models wherein the parametric distributional assumptions of the joinpoint regression models are relaxed by modeling the distribution of regression slopes using Dirichlet process mixtures. We also consider the effect of adding covariates of interest in the joinpoint model. Three model selection criteria, namely, the conditional predictive ordinate (CPO), the expected predictive deviance (EPD), and the deviance information criteria (DIC), are used to select the number of joinpoints. We analyze the grouped survival data for distant testicular cancer from the Surveillance, Epidemiology, and End Results (SEER) Program using these Bayesian models.     

监督分类法在城郊景观类型判定中的应用

基于TM影像,采用最小距离法对北京昌平沙河镇附近城郊景观进行初步分类。在分类过程中涉及图像中不同景观类型的选择和数据预处理等工作：通过实地调查与了解,配合人工目视判读的景观类别,对遥感图像上某些抽样单元影像地物的类别属性获得了先验知识,计算机便按照这些已知类别的特征去;训读;判决函数,以此完成对整个图像的分类。该方法的分类结果与该区域土地利用图各景观类型的面积进行对比后比较接近,总体分类精度达到80.3%,说明该分类方法基本完成了利用遥感监督分类方法快速判定城郊景观类型的技术要求。     

Computer analysis of reliability testing with exponential distribution

Several methods of testing for reliability of engineering systems and equipment are currently available, together with applicable theoretical background. One of the more common methods is sequential testing, where tests are continued until a decision is reached as to product acceptability. Assuming an exponential survival curve outline the theory and procedures involved in carrying out these tests.The present paper gives a method for simulating reliability tests on digital computers whereby additional information such as the probability of completing the tests as a function of time can be obtained. The described Monte Carlo simulation uses exponential product survival characteristics and utilizes mathematical methods for saving computation time. The outlined computer method gives the user the flexibility of devising his own reliability testing procedures. The paper includes a numerical example and compares the results obtained from the computer program with those computed from theoretical formulas.     

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.     

Selecting the median and two quartiles in a set of numbers

The median, the 0.25-percentile and the 0.75-percentile are three of the most relevant ranks in data analysis. MED2Q is a new in situ algorithm to solve this problem. It asymptotically performs an average of 2 5/8 n + o(n) comparisons when n numbers are given as input thus becoming the asymptotically fastest algorithm for this problem reported to date. The performance of MED2Q is compared with those of FIND¹ and ALGORITHM 489², two well-known selection algorithms adapted to this specific problem. From this performance comparison, MED2Q is best when input sets consist of more than 50,000 numbers.     

Estimation for mixed exponential distributions under type-II progressively hybrid censored samples

The type-II progressively hybrid censoring scheme can be deemed as a mixture of type-II progressive and hybrid censoring schemes, which has been utilized to analyze lifetime data in the literature for exponential distribution and Weibull distribution and so on, where the experiment terminates at a pre-specified time. However, little attention has been paid to parametric estimation under this censoring scheme for the mixed exponential distribution (MED) model, which is an important model in life data analysis. Based on type-II progressively hybrid censored samples, the estimation problem of the MED is addressed. The closed form of maximum likelihood estimators (MLEs) of unknown parameters using the EM algorithm are obtained. Some Monte Carlo simulations are implemented and a real data set is analyzed to illustrate the performance of the proposed method.     

Analyzing the relationships between inspections and testing to provide a software testing focus

ContextQuality assurance effort, especially testing effort, is frequently a major cost factor during software development. Consequently, one major goal is often to reduce testing effort. One promising way to improve the effectiveness and efficiency of software quality assurance is the use of data from early defect detection activities to provide a software testing focus. Studies indicate that using a combination of early defect data and other product data to focus testing activities outperforms the use of other product data only. One of the key challenges is that the use of data from early defect detection activities (such as inspections) to focus testing requires a thorough understanding of the relationships between these early defect detection activities and testing. An aggravating factor is that these relationships are highly context-specific and need to be evaluated for concrete environments.ObjectiveThe underlying goal of this paper is to help companies get a better understanding of these relationships for their own environment, and to provide them with a methodology for finding relationships in their own environments.MethodThis article compares three different strategies for evaluating assumed relationships between inspections and testing. We compare a confidence counter, different quality classes, and the F-measure including precision and recall.ResultsOne result of this case-study-based comparison is that evaluations based on the aggregated F-measures are more suitable for industry environments than evaluations based on a confidence counter. Moreover, they provide more detailed insights about the validity of the relationships.ConclusionWe have confirmed that inspection results are suitable data for controlling testing activities. Evaluated knowledge about relationships between inspections and testing can be used in the integrated inspection and testing approach In²Test to focus testing activities. Product data can be used in addition. However, the assumptions have to be evaluated in each new context.     

Homogeneity Testing for Unlabeled Data: A Performance Evaluation

In this paper, we address the problem of testing homogeneity for unlabeled pixels observed in a subimage. Homogeneity testing is an essential component in split-and-merge segmentation algorithm. Two types of homogeneity tests are involved: tests for labeled data when deciding on merges between regions and tests for unlabeled data when deciding whether to split a region. In our study, we focus on images that are modeled as a mosaic of uniform regions corrupted by additive Gaussian noise. Using this model, we present a statistical analysis on the performance of two commonly used approaches for testing homogeneity of unlabeled data based on the region/subregion similarity and the data dispersion, respectively. We also propose and evaluate a new hierarchical homogeneity testing scheme for unlabeled data. The most important finding of this study is that the tests based on region/subregion similarity have a low power on average of detecting inhomogeneity in unlabeled data.     

Testing the equality of proportions for correlated otolaryngologic data

In otolaryngologic (or ophthalmologic) studies, each subject usually contributes information for each of two ears (or eyes), and the values from the two ears (or eyes) are generally highly correlated. Statistical procedures that fail to take into account the correlation between responses from two ears could lead to incorrect results. On the other hand, asymptotic procedures that overlook small sample designs, sparse data structures, or the discrete nature of data could yield unacceptably high type I error rates even when the intraclass correlation is taken into consideration. In this article, we investigate eight procedures for testing the equality of proportions in such correlated data. These test procedures will be implemented via the asymptotic and approximate unconditional methods. Our empirical results show that tests based on the approximate unconditional method usually produce empirical type I error rates closer to the pre-chosen nominal level than their asymptotic tests. Amongst these, the approximate unconditional score test performs satisfactorily in general situations and is hence recommended. A data set from an otolaryngologic study is used to illustrate our proposed methods.     

机车信号自动仿真测试系统

基于上海铁路局的要求,为解决机车信号在线自动测试和记录等问题,以某新型机车信号识别系统为测试对象,设计并实现了一种"机车信号自动仿真测试系统".文中详细介绍了该系统的构成及其在嵌入式系统上的实现过程,采用了一种基于微控制器管理的波形合成技术,仿真出测试规定要求的标准18信息机车信号,合成符合沪杭铁路要求的18信息移频信号和符合标准测试要求的跳变序列,重现了该机车信号自动识别系统在线运行全过程,并将测试结果分类保存以建立起专家级数据库,以供检修人员参考.整个系统除达到上海铁路局所规定的要求外,还实现了对机车信号识别系统进行检测的高可靠性,高可操作性,高灵活性和信息切换时输出信号相位无跳变的特点.该系统的研制成功,提高了机车出入库检验的便利性和可靠性,彻底杜绝人工测试的差错,对进一步提升铁路系统的自动化运行具有重要的现实意义.     

Selecting and using data for integration testing

The extension of dataflow testing to interprocedural testing is described. This was done by developing both an analysis technique that computes the required interprocedural definition-use information, for both direct and indirect dependencies and a testing technique that uses this information in selecting and executing the subpaths across procedure boundaries. A testing tool that implements this technique is presented. For the interprocedural dataflow analysis, the technique summarizes the individual procedures' definition and use information at call sites and then propagates this information throughout the interacting procedures. By efficiently computing the interprocedural data dependencies before testing, the approach lets the testing tool use existing path-selection techniques based on dataflow for interprocedural testing. To track the execution path, the technique recognizes the calls to and returns from procedures and handles the association of various names with a definition as the execution path is being inspected. The technique handles recursive procedures and supports separate compilation of procedures     

Can you Trust a Single Data Source Exploratory Software Engineering Case Study?

As the demand for empirical evidence for claims of improvements in software development and evolution has increased, the use of empirical methods such as case studies has grown. In case study methodology various types of triangulation is a commonly recommended technique for increasing validity. This study investigates a multiple data source case study with the objective of identifying whether more findings, trustworthier findings and other findings are made using multiple data source triangulation, than had a single data source been used. The case study investigated analyses key lead-time success factors for a software evolution project in a large organization developing eBusiness systems with high-availability high throughput transaction characteristics. By tracing each finding in that study to the individual evidences motivating the finding, it is suggested that a multiple data source explorative case study can have a higher validity than a single data source study. It is concluded that a careful case study design with multiple sources of evidence can result in not only better justified findings than a single data source study, but also other findings. Thus this study provides empirically derived evidence that a multiple data source case study is more trustworthy than a comparable single data source case study.     

How Internet Security Breaches Harm Market Value

Decision-tree induction is an effective technique for examining the factors influencing abnormal stock market returns when security breaches are announced in the public media. In this article, the authors extend a previous study, specifically identifying new relationships between abnormal returns and firm and attack characteristics and subject them to traditional statistical testing. They relate their results to the confidential, integrity, and availability dimensions of information security and discuss the findings' technical and managerial implications. The Web extra for this article presents detailed event data.