Optimal allocation of resources over health care programmes: dealing with decreasing marginal utility and uncertainty

This paper addresses the problem of how to value health care programmes with different ratios of costs to effects, specifically when taking into account that these costs and effects are uncertain. First, the traditional framework of maximising health effects with a given health care budget is extended to a flexible budget using a value function over money and health effects. Second, uncertainty surrounding costs and effects is included in the model using expected utility. Other approaches to uncertainty that do not specify a utility function are discussed and it is argued that these also include implicit notions about risk attitude.     

Cost-effectiveness analysis and health care resource allocation: decision rules under variable returns to scale

We argue that health programs are administered in settings that often violate the frequently stated assumption of constant returns to scale in the provision of health services. Three types of returns to scale are identified from the general economic literature: returns to scale with respect to population, effectiveness, and quality. We show that decision rules based on incremental cost-effectiveness ratios or cost-benefit tests are not optimal if returns to scale are not constant. We derive the optimal decision rules under variable returns to scale using optimization techniques, and employ several examples to illustrate the concepts and methods.     

Methods for detecting functional classifications in neuroimaging data

Data-driven statistical methods are useful for examining the spatial organization of human brain function. Cluster analysis is one approach that aims to identify spatial classifications of temporal brain activity profiles. Numerous clustering algorithms are available, and no one method is optimal for all areas of application because an algorithm's performance depends on specific characteristics of the data. K-means and fuzzy clustering are popular for neuroimaging analyses, and select hierarchical procedures also appear in the literature. It is unclear which clustering methods perform best for neuroimaging data. We conduct a simulation study, based on PET neuroimaging data, to evaluate the performances of several clustering algorithms, including a new procedure that builds on the kth nearest neighbor method. We also examine three stopping rules that assist in determining the optimal number of clusters. Five hierarchical clustering algorithms perform best in our study, some of which are new to neuroimaging analyses, with Ward's and the beta-flexible methods exhibiting the strongest performances. Furthermore, Ward's and the beta-flexible methods yield the best performances for noisy data, and the popular K-means and fuzzy clustering procedures also perform reasonably well. The stopping rules also exhibit good performances for the top five clustering algorithms, and the pseudo-T2 and pseudo-F stopping rules are superior for noisy data. Based on our simulations for both noisy and unscaled PET neuroimaging data, we recommend the combined use of the pseudo-F or pseudo-T2 stopping rule along with either Ward's or the beta-flexible clustering algorithm.     

Quantile estimation following non-parametric phase I clinical trials with ordinal response

A non-parametric multi-dimensional isotonic regression estimator is developed for use in estimating a set of target quantiles from an ordinal toxicity scale. We compare this estimator to the standard parametric maximum likelihood estimator from a proportional odds model for extremely small data sets. A motivating example is from phase I oncology clinical trials, where various non-parametric designs have been proposed that lead to very small data sets, often with ordinal toxicity response data. Our comparison of estimators is performed in conjunction with three of these non-parametric sequential designs for ordinal response data, two from the literature and a new design based on a random walk rule. We also compare with a non-parametric design for binary response trials, by keeping track of ordinal data for estimation purposes, but dichotomizing the data in the design phase. We find that a multidimensional isotonic regression-based estimator far exceeds the others in terms of accuracy and efficiency. A rule by Simon et al. (J. Natl. Cancer Inst. 1997; 89:1138-1147) yields particularly efficient estimators, more so than the random walk rule, but has higher numbers of dose-limiting toxicity. A small data set from a leukemia clinical trial is analysed using our multidimensional isotonic regression-based estimator.     

Analyzing the eye movement of dentists during their reading of CT images

In order to evaluate the eye movements of dentists when they were interpreting radiographs, ten normal computed tomography (CT) images and ten images with pathologic lesions were shown to eight dentists, and the pattern of their eye movement was qualitatively analyzed. Six fixation point parameters were calculated, including the time required to discriminate between normal and pathologic images (X₁), the total fixation point count (X₂), the total travel distance between fixation points (X₃), the average time spent on each fixation point (X₄), the total gaze fixation time (X₅), and the maximum gaze fixation time spent on each image (X₆). When the subjects were interpreting pathologic images, X₂ and X₄ were shorter; however, when they were viewing normal images, time was spent on observing multiple fixation points before the completion of the interpretive process. While pathologic images were recognized through top-down processing, there was a tendency for normal images to be recognized through bottom-up processing. The results of discriminant analysis, using a linear discriminant function, indicated that the independent variables X₂ and X₄ and the dependent variable X₅ were the only variables that contributed significantly to differentiating between normal and pathologic images. The linear discriminant function was Z = 9.0 × 10⁻² × X₂ + 3.0 × X₄ − 2.1 (discriminant score: Z < 0, pathologic image; Z ≧ 0, normal image). When the mean value of each individual's gaze fixation data was substituted into the discriminant formula, the hit rate for normal and pathologic images was discriminated at 94% (15 of 16). Received: September 27, 2000 / Accepted: July 26, 2001     

Classification of breast tissue by electrical impedance spectroscopy

Electrical impedance spectroscopy is a minimally invasive technique that has clear advantages for living tissue characterisation owing to its low cost and ease of use. The present paper describes how this technique can be applied to breast tissue classification and breast cancer detection. Statistical analysis is used to derive a set of rules based on features extracted from the graphical representation of electrical impedance spectra. These rules are used hierarchically to discriminate several classes of breast tissue. Results of statistical classification obtained from a data set of 106 cases representing six classes of excised breast tissue show an overall classification efficiency of ∼92% with carcinoma discrimination >86%.     

生活垃圾管理法治化的内涵

探讨了生活垃圾管理法治化的基本原则,强调了依法管理垃圾所必须坚持的主要原则和制度,并指出目前实现生活垃圾管理法治化急需加强执法队伍建设.     

A quantifier-based fuzzy classification system for breast cancer patients

ObjectivesRecent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups.Materials and methodsIn this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called ‘fuzzy quantification subsethood-based algorithm’) and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.ResultsA rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm.ConclusionThe fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.     

星座图判别法用于CT诊断脑占位性病变

本文介绍了一种新的判别方法—星座图判别法,并用它对脑部三种占位性疾病进行了判别分类,总符合率为85%。本文还对内部构成不均匀的样本在判别分析时的处理进行了讨论,且将星座图判别与逐步判别作了比较,认为前者更适合于文中资料的判别分析,应用也更方便。