A Comparison Between Recurrence Risks and Conditional Recurrence Risks Based on the Semi-Symmetric Intraclass Contingency Table

Recurrence risks are compared to conditional recurrence risks under certain association models corresponding to the underlying semi-symmetric intraclass contingency table. Ratios of mean squared errors are used to make the comparisons. A study of the comparisons corroborates the theory of KHAMIS and HINKELMANN (1984), and gives new insight into the disease-genotype association problem.     

Log-Linear Model Analysis of the Disease-Genotype Association Problem: Two-Loci Case

The log-linear model is modified in order to adapt it to the disease-genotype association problem, where two loci are involved. By so doing, the structure of associations among the genetic variables can be determined, and sources of distortion can be identified in certain test statistics and coefficients of association.     

Selection of Log-Linear Models with Orthonormal Basis for Contingency Tables

A model selection criterion for log-linear models with orthonormal basis for contingency tables is developed using the Gauss discrepancy between the logarithms of the frequencies. The contribution of each parameter to the criterion may be determined separately. A test for the hypothesis that the use of a certain parameter increases the expected discrepancy is given.     

房颤射频消融术后复发风险预测评分系统的建立及评价

目的:探讨心房颤动(房颤)患者射频消融术后复发的风险因素,并依此构建个性化的风险评分系统。方法:选取2017年1~8月行射频消融术的房颤患者154例作为研究对象,依据术后3个月的随访结果将患者分为复发组及未复发组,采用单因素分析和Logistic回归分析对各风险因素进行分析,构建其评分系统,采用Hosmer-Lemeshow拟合优度检验和ROC曲线下面积评价评分系统的准确度及区分度。结果:术后随访3个月的结果显示共37例(24.03%)房颤患者出现复发,房颤类型、病程、体质量指数(BMI)、左房前后径(LAD)、左房容积(LAV)及超敏C反应蛋白(hs-CRP)水平均是房颤复发的独立风险因素(P<0.05)。构建的风险评分系统得分为0~26分,Hosmer-Lemeshow拟合优度检验:x^2=7.520,P=0.482;ROC曲线下面积为0.864(95%CI:0.837~0.891),预测评分值为15分时,约登指数最大(0.605),此时的敏感度和特异度分别为77.3%和83.2%。结论:房颤患者射频消融术后的复发率较高,依据风险因素构建的风险评分系统具有较高的预测效率和区分能力,可作为房颤患者射频消融术后复发风险评估的参考工具。     

425 例结节性甲状腺肿患者术后复发影响因素的分析

目的:探讨结节性甲状腺肿复发的影响因素。方法:分别对本院425例结节性甲状腺肿患者随访1年至8年;采用SPSS软件对复发相关因素进行单因素和多因素Logistic回归分析。结果:随访患者中有39例复发,复发率为9.18%。分析结果显示,两侧甲状腺结节数量越多,结节性甲状腺肿复发率越高(OR=2.631),双侧结节性甲状腺肿患者比单侧患者复发率高(OR=2.758),与接受正规替代治疗者相比,未接受替代治疗及接受非正规替代治疗者更易复发(OR=7.577/4.151),与单纯结节切除术相比,一侧加峡部全切、对侧部分/次全切除术术后复发率低(OR=0.209)。结论:结节性甲状腺肿患者术后复发率较高,针对上述各种影响因素,在临床上相应的治疗措施可有效预防结节性甲状腺肿的复发。     

Ecological Risks Associated with Oyster Restoration Options for Chesapeake Bay

Ecological risk assessments were conducted as part of the Programmatic Environmental Impact Statement designed to evaluate costs and benefits of alternative approaches to oyster restoration in Chesapeake Bay, USA, including the intentional introduction of a non-native Asian oyster species. A relative risk model was used to evaluate alternatives involving both the native Eastern oyster and the non-native species. Effects of options were examined for a diverse set of ecological resources and conditions for the Bay including oysters, submerged aquatic vegetation, phytoplankton, zooplankton, benthic invertebrates, blue crabs, fish, wildlife receptors, dissolved oxygen, and total suspended solids. A weight-of-evidence method based on available scientific evidence was also used to answer a set of questions developed by a science advisory panel. There was low risk that the Asian oyster would not provide ecosystem services similar to those afforded by the Eastern oyster; however, there is moderate to high risk that the Asian oyster would interact with and compete with the Eastern oyster. The potential for introduction and spread of diseases from the Asian oyster to other species in the Bay is considered negligible, but there is high risk that the Asian oyster would disperse outside of the Bay.     

广义Logistic模型的捕获优化问题

以王寿松所提出的广义Ｌｏｇｉｓｔｉｃ模型为基础,讨论单种群生物资源的捕获优化问题,分析了被开发生物种群的动力学性质。在单位捕获努力量假定下,以最大可持续捕获量为管理目标,确定了线性捕获下的最优捕获策略,得到了最优捕获努力量,最大可持续收获及相应的最优种群水平的显式表达式,包括著名的Ｓｃｈａｅｆｅｒ模型作为特例,推广了相应的结果。     

On a probability Model for Classifying the Unknown Causes of Death Due to Two Competing Risks into Assignable Causes

A probability model to classify the otherwise unclassified causes of death due to two competing risks R₁ and R₂ has been evolved in this paper. A simple model based on Poisson inputs has been illustrated by numerical illustrations. Further generalization of the model with more than two competing risks is straightforward for the given model.     

A Generalized Logistic Model for Quantal Response Bioassay

In bioassay, where different levels of the stimulus may represent different doses of a drug, the binary response is the death or survival of an individual receiving a specified dose. In such applications, it is common to model the probability of a positive response P at the stimulus level x by P = F(x′β), where F is a cumulative distribution function and β is a vector of unknown parameters which characterize the response function. The two most popular models used for modelling binary response bioassay involve the probit model [BLISS (1935), FINNEY (1978)], and the logistic model [BERKSON (1944), BROWN (1982)]. However, these models have some limitations. The use of the probit model involves the inverse of the standard normal distribution function, making it rather intractable. The logistic model has a simple form and a closed expression for the inverse distribution function, however, neither the logistic nor the probit can provide a good fit to response functions which are not symmetric or are symmetric but have a steeper or gentler incline in the central probability region. In this paper we introduce a more realistic model for the analysis of quantal response bioassay. The proposed model, which we refer to it as the generalized logistic model, is a family of response curves indexed by shape parameters m₁ and m₂. This family is rich enough to include the probit and logistic models as well as many others as special cases or limiting distributions. In particular, we consider the generalized logistic three parameter model where we assume that m₁ = m, m is a positive real number, and m₂ = 1. We apply this model to various sets of data, comparing the fit results to those obtained previously by other dose-response curves such as the logistic and probit, and showing that the fit can be improved by using the generalized logistic.     

进展期胃癌根治术后早期复发的影响因素分析

目的:分析影响进展期胃癌根治术后早期复发的相关因素,为临床干预工作提供依据。方法:选取2009年6月至2012年7月本院收治的195例进展期胃癌患者作为研究对象,所有患者均接受胃癌根治术治疗,根据患者术后1年内复发与否将上述患者分为早期复发组(n=103)与对照组(n=92)。先后采用x2检验、非条件Logistic回归分析确定影响进展期胃癌根治术后早期复发的独立相关因素。结果:单因素分析发现,两组患者的肿瘤直径、Borrmann分型、Lauren分型、T分期、N分期、TNM分期、新辅助化疗、术后化疗等指标相比差异有统计学意义(P0.05),两组患者的性别、年龄、体质指数、肿瘤位置、分化程度、手术方式、腹腔镜手术等指标相比差异无统计学意义(P0.05)。非条件Logistic回归发现,N分期、TNM分期是影响进展期胃癌根治术后早期复发的独立危险因素,而新辅助化疗是独立保护因素。结论:进展期胃癌的N分期、TNM分期是其术后早期复发的独立危险因素,采取而新辅助化疗可降低进展期胃癌根治术后早期复发率。     

Frailty‐Based Competing Risks Model for Multivariate Survival Data

Summary In this work, we provide a new class of frailty‐based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34 , 541–554) to incorporate frailty variates, with the use of cause‐specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within‐subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.     

Dose-Response Model for Lassa Virus

This article develops dose-response models for Lassa fever virus using data sets found in the open literature. Dose-response data were drawn from two studies in which guinea pigs were given subcutaneous and aerosol exposure to Lassa virus. In one study, six groups of inbred guinea pigs were inoculated subcutaneously with doses of Lassa virus and five groups of out-bred guinea pigs were similarly treated. We found that the out-bred subcutaneously exposed guinea pig did not exhibit a dose-dependent trend in response. The inbred guinea pigs data were best fit by an exponential dose-response model. In a second study, four groups of out-bred guinea pigs were exposed to doses of Lassa virus via the aerosol route. In that study, aerosol diameter was less than 4.5 μ m and both mortality and morbidity were used as endpoints. The log-probit dose-response model provided a somewhat better fit than the Beta-Poisson model for data with mortality as the endpoint, but the Beta-Poisson is considered the best fit model because it can be derived using biological considerations. Morbidity data were best fit with an exponential dose-response model.     

广义Kolmogorov模型的Lyapunov函数构造新算法及其应用

本文对广义Kolmogorov模型,给出构造Lyapunov函数的新算法,在文献1中只对其中某些特殊类型给出几种特殊的构造方法,而本文给出的是这类模型的一般新算法,应用较广泛。     

基于血清CEA、GPX3、CXCL9构建非小细胞肺癌患者根治术后早期复发转移的预测模型

摘要 目的：基于血清癌胚抗原（CEA）、谷胱甘肽过氧化物酶3（GPX3）、CXC趋化因子配体9（CXCL9）构建非小细胞肺癌（NSCLC）患者根治术后早期复发转移的预测模型。方法：选取2018年7月-2019年12月于湖南中医药大学第二附属医院行肺癌根治术的95例NSCLC患者。采用酶联免疫吸附法检测血清CEA、GPX3、CXCL9水平。术后行3年随访,根据随访期间患者有无发生复发转移分为未复发转移组与复发转移组。Logistic回归模型分析NSCLC患者术后复发转移的影响因素,并建立NSCLC患者根治术后早期复发转移预测模型。采用受试者工作特征（ROC）曲线分析预测模型的预测效能。结果：术后随访3年,95例患者失访2例,复发转移36例。复发转移组血清CEA、CXCL9水平高于未复发转移组,GPX3水平低于未复发转移组（P＜0.05）。复发转移组与未复发转移组在肿瘤类型、TNM分期、淋巴结清扫数方面,差异比较具有统计学意义（P＜0.05）。Logistic回归模型结果显示,TNM分期、淋巴结清扫数及血清CEA、GPX3、CXCL9水平为NSCLC患者术后复发转移的影响因素（P＜0.05）。ROC分析结果显示,Log P 预测模型的ROC-AUC（0.95CI）为0.862（0.772～0.943）。结论：血清CEA、CXCL9水平上升,GPX3水平下降为NSCLC肺癌根治术后患者复发转移的影响因素。血清CEA、CXCL9、GPX3联合构建的预测模型可辅助预测NSCLC术后复发转移风险。     

术前预后营养指数对肺鳞状细胞癌患者术后复发及死亡的预测价值分析

摘要 目的：探讨术前预后营养指数（PNI）与肺鳞状细胞癌患者预后的关系及对术后复发、死亡的预测效能。方法：纳入2017年1月-2019年1月在我院接受治疗的78例肺鳞状细胞癌患者,所有患者均具有完整的临床资料及病理信息,对其进行门诊复查随访3年,除去失访病例共纳入76例患者资料,期间共有43例患者复发、37例患者死亡;按照复发及死亡情况将该76例患者分别分为复发组（n=43）及未复发组（n=33）,死亡组（n=37）及存活组（n=39）,分别使用单因素和多因素Logistic回归分析影响肺鳞状细胞癌患者复发及死亡的独立危险因素;采用受试者工作特征（ROC）曲线分别分析PNI在肺鳞状细胞癌患者术后复发及死亡的预测效能及最佳截断值。结果：单因素分析显示,TNM分期、吸烟年限、糖尿病、家族史、PNI是影响肺鳞状细胞癌患者术后复发的相关因素（P＜0.05）;性别、年龄、TNM分期、BMI、吸烟史、吸烟年限及PNI是影响肺鳞状细胞癌患者术后死亡的相关因素（P＜0.05）。多因素Logistic回归模型分析显示,TNM分期为Ⅲ期、吸烟年限较长、家族史是引发肺鳞状细胞癌患者术后复发的独立危险因素,PNI为保护因素（P＜0.05）;另外男性、年龄较大、TNM分期为Ⅲ期、吸烟年限较长是引发肺鳞状细胞癌患者术后死亡的独立危险因素,PNI为保护因素（P＜0.05）;ROC分析显示PNI在预测肺鳞状细胞癌患者术后复发的曲线下面积为0.726,敏感度为0.814,特异度为0.667,最佳截断值为48;PNI在预测肺鳞状细胞癌患者术后存活的曲线下面积为0.787,敏感度为0.838,特异度为0.718,最佳截断值为50。结论：PNI对肺鳞状细胞癌患者术后复发及生存均具有较高的预测效能,提高PNI水平对改善肺鳞状细胞癌患者的预后具有积极作用。     

Model choice and influential cases for survival studies

Deletion diagnostics are developed for identifying observations that influence the estimates of regression parameters and the mixture parameter in the families of relative risk functions for failure time data. The diagnostic for the regression parameters is a generalization of Cain and Lange's (1984, Biometrics 40, 493-499) measure of individual influence. The generalizations of martingale residuals, Schoenfeld's partial residuals (1982, Biometrika 69, 239-241), and score residuals by Therneau, Grambsch, and Fleming (1990, Biometrika 77, 147-160) are also obtained. The influence of some observations on regression parameters can be drastically modified as the mixture parameter changes, even for a very small change. In addition, adding or deleting some observations might result in choosing different models. The diagnostics are applied to a family proposed by Guerrero and Johnson (1982, Biometrika 69, 309-314). One illustrative example is presented.