336例创伤数据对中国人创伤评分权重系数的评价

目的为了评价国内对ＴＲＩＳＳ评分权重系数的修订，对３３６例创伤患者分别用国外权重和国内权重评定伤情后进行比较。方法３３６例采用ＴＲＩＳＳ法计算生存概率Ｐｓ，所需权重分别采用新近报道的国内权重和国外ＭＴＯＳ权重。Ｐｓ≥０．５预测生存，Ｐｓ＜０．５预测死亡，将预测生死与实际生死结果进行比较。结果中国人权重在敏感性和区别度方面高于国外权重，误判率低于国外权重。结论中国人权重对中国人的创伤程度评估有价值，为进一步精确修订权重，建议建立大型中国人创伤数据库     

加权关联度评价计划免疫工作质量

目的 :探求客观定量、准确可行、简便易行、能反映计划免疫工作质量全貌的综合评价方法。方法 :采用加权关联度分析对达州市 2 0 0 2年计划免疫监测结果进行评价。结果 :求得达州市 7个县市计划免疫监测结果的加权关联度值 ,列出其频数分布 ,估计回归方程 r=- 0 .3175 0 .185 7y,r=0 .995 3,差异有显著性意义 (P<0 .0 5 ) ,经合理分档 ,得出计划免疫工作质量优秀的县市 3个 ,中等的 3个 ,较差的 1个 ,符合实际情况。结论 :加权关联度分析能直观、定量、简明、准确地反映计划免疫工作质量 ,适用于计划免疫工作质量的综合评价。     

Trauma centre patient volume and inpatient mortality risk reconsidered

BackgroundSeveral studies have examined the relationship between injury volumes and trauma centre outcomes, with varying results attributable to differences in the measurement of volume's effect on mortality and differences in how characteristics are addressed as potential confounders.MethodsThis analysis includes all trauma cases reported to the NTDB 2012. The effect of trauma centre volume on patient mortality risk was measured in three different contexts: as a linear function of trauma centre volume, as a dichotomous function comparing patients in trauma centres with and without 1200 or more cases, and as a non-linear function of trauma centre volume. Multivariable weighted Hierarchical Generalized Linear Models were used to account for the combined effects of facility level and patient level covariates. Patient level mortality risk was assessed using the ACS Trauma Quality Improvement Programme methodology.ResultsTrauma centre volume was not a statistically significant predictor (at the α = 0.01 level) of patient mortality risk, in any of the three models. Comprehensive adjustments for patient level risk were obtained, with excellent discrimination between survivor and decedent cases. The addition of trauma volume to baseline patient mortality risk yielded no improvement in the accuracy of any model. These results were not sensitive to the inclusion of Level II trauma centres. Equivalent results were obtained by repeating the analysis for the Level I subpopulation only.ConclusionsCase volume may be a reasonable standard for determining whether adequate numbers of injured patients are available to support training needs and experience requirements of a Level I trauma centre. However, case volume is not a useful predictor of patient mortality in individual facilities. Trauma centre volume has no independent effect, after accounting for the patient level characteristics that predominantly influence mortality.     

Evaluation of alternative statistical models for crossover studies to demonstrate caffeine adjuvancy in the treatment of tension headache

This paper discusses alternative statistical models for the analysis of six crossover studies to determine whether better relief of tension headache occurs from treatment with an analgesic plus caffeine (C) than with the analgesic alone (A) or with placebo (P). Each patient in these crossover studies randomly received a pair of distinct medications in such a way as to treat the first two of four headaches with the initial medication in the pair and to treat the third and fourth headaches with the last medication in the pair. In order to have greater power for the C versus A comparison, three times as many patients were randomly assigned to the A:C and C:A sequence groups as to the A:P, C:P, P:A, and P:C sequence groups.

An issue of statistical interest for these crossover studies is the extent to which the possibility of unequal carryover effects of the three medications influences the roles of alternative models for data analysis and the interpretation of results. When carryover effects for all three medications are equal, univariate analysis of variance for the difference scores between the average response for the first two headaches and the average response for the third and fourth headaches for each patient provides nearly the same power for pairwise treatment comparisons as more comprehensive multivariate methods for all four headaches. However, for comparisons concerning carryover effects and for treatment comparisons with adjustment for carryover effects, multivariate methods encompassing all four headaches jointly can provide greater power than univariate analysis for difference scores, particularly when there is low intraclass correlation for responses within the same patient. Another noteworthy role for multivariate methods in situations with potentially unequal carryover effects is their capacity to clarify whether multiple types of carryover effects occur across the second, third, and fourth headaches in the respective sequence groups.

Multivariate models with alternative specifications of carryover effects are fit to the data from the six crossover studies to compare C, A, and P by weighted least squares. The role of potential variation among centers is addressed in these analyses by the use of stratified proportional means over centers, means of center means, and means ignoring centers. The primary focus of attention in the respective analyses is the evaluation of treatment comparisons with and without adjustment for potential differences among carryover effects of the treatments. Comparisons among carryover effects are assessed as well, but they mainly serve a background purpose since the principal issue is the extent to which findings for treatment comparisons are similar across alternative ways of accounting for potential carryover effects.

For all models, the average predicted response across all headaches treated with C was significantly better than that for A or P. For models that adjusted treatment effects for carryover effects in a statistically efficient way, the adjusted direct treatment effect of C was significantly better than that of A or P. Thus, the superiority of C over A found robust support from models both with and without adjustment for potential differences among carryover effects of the treatments.     

A novel active contour model for serial image segmentation

A novel method for the segmentation of serial images is proposed. In the presented framework, the driving force acts as the attracting term to propel the evolving curve towards the object boundaries, and the adaptive term changes the sign of driving force accordingly. Therefore, the evolving curves can arrive at the desired direction without a requirement for the initial curve to be strictly inside or outside the object. A weighted length term is used to keep the smoothness of curve and penalize the formulation of discontinuities. To prevent the level set function deviating from a signed distance function, a distance rectifying flow is also added to the model; therefore the time-consuming re-initialization procedure is completely avoided. Experiments on both synthetic image and CT serial images demonstrate the feasibility and efficiency of the method.     

基于加权基因共表达网络分析运动性精子结构域包含蛋白2在类风湿关节炎中的表达及与疾病活动度的相关性

目的利用加权基因共表达网络分析(WGCNA)及实验验证寻找RA相关的关键基因。方法从GEO数据库下载了RA患者基因芯片数据,构建基因网络,利用WGCNA将基因划分为不同的模块,将与RA临床症状相关的模块中的关键基因进行了基因本体论分析。随后使用GEO不同的数据集用受试者工作特征曲线(ROC)评价关键基因对RA诊断的准确性。此外,还通过实时荧光定量反转录PCR(RT-PCR)及蛋白质印迹法验证关键基因在RA中的表达,分析其与DAS28的关系。采用配对样本t检验和Pearson相关性分析对结果进行分析。结果共筛选出5413个基因构建了加权基因共表达网络,将基因分为23个模块。其中,黑色模块与RA临床症状密切,包含346个基因。富集分析及京都基因与基因组百科全书(KEGG)信号通路分析显示其要富集于对IL-6的正调控、IL-1β分泌、破骨细胞分化、NOD样受体信号通路、辅助性T细胞(Th)17细胞分化等多个与RA密切相关的通路。其中运动性精子结构域包含蛋白2(MOSPD2)与临床症状具有明显相关性,在血液单核细胞、骨髓单核细胞中高表达(t=2.238,P=0.032;t=3.153,P=0.006),在RA关节滑膜液中与血液中表达呈正相关(r=0.683,P=0.03)。ROC曲线分析表明,MOSPD2能区分RA和对照组(曲线下面积分别为0.855和0.726)。RT-PCR及蛋白质印迹法结果显示,MOSPD2在RA患者中表达上调(t=-3.96,P=0.02)。MOSPD2在血液中的表达水平与RA患者的DAS28呈正相关(r=0.8846,P=0.0462)。结论MOSDP2与RA患者临床症状密切相关,可能是诊断及治疗RA的靶点之一。     

Information estimators for weighted observations

The Shannon information content is a valuable numerical characteristic of probability distributions. The problem of estimating the information content from an observed dataset is very important in the fields of statistics, information theory, and machine learning. The contribution of the present paper is in proposing information estimators, and showing some of their applications. When the given data are associated with weights, each datum contributes differently to the empirical average of statistics. The proposed estimators can deal with this kind of weighted data. Similar to other conventional methods, the proposed information estimator contains a parameter to be tuned, and is computationally expensive. To overcome these problems, the proposed estimator is further modified so that it is more computationally efficient and has no tuning parameter. The proposed methods are also extended so as to estimate the cross-entropy, entropy, and Kullback–Leibler divergence. Simple numerical experiments show that the information estimators work properly. Then, the estimators are applied to two specific problems, distribution-preserving data compression, and weight optimization for ensemble regression.     

Dynamic class imbalance learning for incremental LPSVM

Linear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online classification of data streams with imbalanced class distribution, we propose a dynamic class imbalance learning (DCIL) approach to incremental LPSVM (IncLPSVM) modeling. In doing so, we simplify a computationally non-renewable weighted LPSVM to several core matrices multiplying two simple weight coefficients. When data addition and/or retirement occurs, the proposed DCIL-IncLPSVM¹ accommodates newly presented class imbalance by a simple matrix and coefficient updating, meanwhile ensures no discriminative information lost throughout the learning process. Experiments on benchmark datasets indicate that the proposed DCIL-IncLPSVM outperforms classic IncSVM and IncLPSVM in terms of $F$-measure and $G$-mean metrics. Moreover, our application to online face membership authentication shows that the proposed DCIL-IncLPSVM remains effective in the presence of highly dynamic class imbalance, which usually poses serious problems to previous approaches.