Assessing classification rules

Having constructed a rule for classifying objects into classes, one will need to evaluate the performance of that rule both in absolute terms (is it good enough?) and in relative terms (is it better than an alternative?). In this paper, we discuss such evaluation, focusing primarily on the first question, and covering discriminability (how effective the rule is in classifying new objects to the correct class) and reliability (how accurately it estimates probabilities of class membership). Measures based on percentages correct, measures based on probabilities of being correct and distance based measures are outlined, and attractive and problematic properties are discussed.     

Assessing spinal shape

Idiopathic scoliosis is the most common spinal deformity, affecting perhaps as many as 5% of children. Early recognition of the condition is essential for optimal treatment. A widely used technique for identification is based on a somewhat crude angle measurement from a frontal spinal X-ray. Here, we provide a technique and new summary statistical measures for classifying spinal shape, and present results obtained from clinical X-rays.     

Assessing goodness-of-fit for evaluation of dose-proportionality

For the clinical development of a new drug, the determination of dose-proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non-linear pharmacokinetics and may help to identify sub-populations with divergent clearances. Prior to making any conclusions regarding dose-proportionality, the goodness-of-fit of the model must be assessed to evaluate the model performance. We propose the use of simulation-based visual predictive checks to improve the validity of dose-proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.     

Assessing a vector of clinical observations

The periodic monitoring of drug treatments often involves the collection of biological specimens (e.g. blood, urine, synovial fluid) for the purpose of clinical laboratory assessment. The analysis of a particular specimen yields a vector of measurements from which judgments are made concerning the status of a subject and the effect of the drug. Typically, an observation vector is compared to “normal values” which may be conditioned on covariates such as age, gender, or other relevant characteristics. Under an assumption of multivariate normality of the data available, a method is presented for deciding whether a particular observed vector looks “normal”. The method, based on a predictive approach, is compared to other proposals and is shown to have optimality properties not possessed by standard procedures. Three different approaches are used in the discussion of optimality within the class of invariant methods. The first involves tolerance regions with smallest normalized expected volume, the second involves a decision theoretic comparison of predictive distributions, while the third involves the foundational notions of incoherence (Dutch book) and strong inconsistency.     

Assessing risk for rare events

This paper develops a method for assessing the risk for rare events based on the following scenario. There exists a large population with an unknown percentage p of defects. A sample of size N is drawn from the population and, in the sample, 0 defects are drawn. Given these data, we want to determine the probability that no more than n defects will be found in another random sample of N drawn from the population. Estimates on the range of p and n are calculated from a derived joint distribution which depends on p, n and N. Asymptotic risk results based on an infinite sample are then developed. It is shown that these results are applicable even with relatively small sample spaces.     

Assessing influence in time series

This paper studies influential observations on the spectrum of a stationary stochastic process. We introduce a leave-one-out procedure in spectral density estimation to identify influential points. A simulated envelope is proposed to assess the magnitude of influence when the data follow an autoregressive integrated moving average model. Practical illustrations are discussed in two examples.     

Assessing the fit of finite mixture distributions

Mixture distributions have become a very flexible and common class of distributions, used in many different applications, but hardly any literature can be found on tests for assessing their goodness of fit. We propose two types of smooth tests of goodness of fit for mixture distributions. The first test is a genuine smooth test, and the second test makes explicit use of the mixture structure. In a simulation study the tests are compared to some traditional goodness of fit tests that, however, are not customised for mixture distributions. The first smooth test has overall good power and generally outperforms the other tests. The second smooth test is particularly suitable for assessing the fit of each component distribution separately. The tests are applicable to both continuous and discrete distributions and they are illustrated on three medical data sets.     

中国对外贸易质量剖析

如何科学地评价中国对外贸易质量的状况,是制定有效政策以推进我国对外贸易发展方式转型的前提条件之一.本文利用经济理论判断和统计分析相结合的方法,从世界银行WDI数据库中,选取并构造了1980-2010年反映中国对外贸易状况的6个因变量和9个自变量.通过实证分析,在静态和动态两个维度,描述了改革开放30多年来中国对外贸易质量总体状况的结构变化特征,并提出了相关的政策建议和进一步的研究方向.     

Assessing the impact of ICH E9

The ICH harmonized tripartite guideline 'Statistical Principles for Clinical Trials', more commonly referred to as ICH E9, was adopted by the regulatory bodies of the European Union, Japan and the USA in 1998. This document united related guidance documents on statistical methodology from each of the three ICH regions, and meant that for the first time clear consistent guidance on statistical principles was available to those conducting and reviewing clinical trials. At the 10th anniversary of the guideline's adoption, this paper discusses the influence of ICH E9 by presenting a perspective on how approaches to some aspects of clinical trial design, conduct and analysis have changed in that time in the context of regulatory submissions in the European Union.     

Assessing multivariate normality: a compendium

Various aspects of assessing multivariate normality are discussed. Practical recommendations are given, and areas of further research interest are noted.     

Assessing multiple endpoints with ordered alternatives

Nonparametric approaches to the analysis of multiple endpoints in clinical studies can be of particular value when the endpoints are heterogeneous or distributional assumptions are suspect. We describe a multivariate Terpstra-Jonckheere U-statistic for assessing multiple endpoints with ordered alternatives, and illustrate its use with data arising from a recent clinical study.     

Assessing prior information for designed experiments

We examine, via a Bayesian analysis, in a general experimental situation, whether if is worthwhile to obtain additional prior information before performing an experiment. Examples illustrate the application of the techniques developed.     

Assessing the value of a censored surrogate outcome

Lifetime Data Analysis - Assessing the potential of surrogate markers and surrogate outcomes for replacing a long term outcome is an active area of research. The interest in this topic is partly...     

Assessing robustness of crossover designsto subjects dropping out

In some crossover experiments, particularly in medical applications, subjects may fail to complete their sequences of treatments for reasons unconnected with the treatments received. A method is described of assessing the robustness of a planned crossover design, with more than two periods, to subjects leaving the study prematurely. The method involves computing measures of efficiency for every possible design that can result, and is therefore very computationally intensive. Summaries of these measures are used to choose between competing designs. The computational problem is reduced to a manageable size by a software implementation of Polya theory. The method is applied to comparing designs for crossover studies involving four treatments and four periods. Designs are identified that are more robust to subjects dropping out in the final period than those currently favoured in medical and clinical trials.     

Cumulant Plots for Assessing the Gamma Distribution

This article introduces graphical procedures for assessing the fit of the gamma distribution. The procedures are based on a standardized version of the cumulant generating function. Plots with bands of 95% simultaneous confidence level are developed by utilizing asymptotic and finite-sample results. The plots have linear scales and do not rely on the use of tables or values of special functions. Further, it is found through simulation, that the goodness-of-fit test implied by these plots compares favorably with respect to power to other known tests for the gamma distribution in samples drawn from lognormal and inverse Gaussian distributions.     

Assessing and comparing the accuracy of various bootstrap methods

We evaluate the performance of various bootstrap methods for constructing confidence intervals for mean and median of several common distributions. Using Monte Carlo simulation, we assessed performance by looking at coverage percentages and average confidence interval lengths. Poor performance is characterized by coverage deviating from 0.95 and large confidence interval lengths. Undercoverage is of greater concern than overcoverage. We also assess the performance of bootstrap methods in estimating the parameters of the Cox Proportional Hazard model and Accelerated Failure Time model.