Revalidation of a prognostic score model based on complete blood count for nasopharyngeal carcinoma through a prospective study

Objective： In our previous work, we incorporated complete blood count （CBC） into TNM stage to develop a new prognostic score model, which was validated to improve prediction efficiency of TNM stage for nasopharyngeal carcinoma （NPC）. The purpose of this study was to revalidate the accuracy of the model, and its superiority to TNM stage, through data from a prospective study.Methods： CBC of 249 eligible patients from the 863 Program No. 2006AA02Z4B4 was evaluated. Prognostic index （PI） of each patient was calculated according to the score model. Then they were divided by the PI into three categories： the low-, intermediate-and high-risk patients. The 5-year disease-specific survival （DSS） of the three categories was compared by a log-rank test. The model and TNM stage （Tth edition） were compared on efficiency for predicting the 5-year DSS, through comparison of the area under curve （AUC） of their receiver-operating characteristic curves.Results： The 5-year DSS of the low-, intermediate- and high-risk patients were 96.0%, 79.1% and 62.2%, respectively. The low- and intermediate-risk patients had better DSS than the high-risk patients （P〈0.001 and P〈0.005, respectively）. And there was a trend of better DSS in the low-risk patients, compared with the intermediate-risk patients （P=0.049）. The AUC of the model was larger than that of TNM stage （0.726 vs. 0.661, P：0.023）. Conclusions： A CBC-based prognostic score model was revalidated to be accurate and superior to TNM stage on predicting 5-year DSS of NPC.

The Need for Structural Adjustment: Was It Essential for African Countries over the Decade of the 80’s? An Econometric Analysis Using Count Data Models

Several economists agree to say that the need for adjustment was essential for African countries over the decade of the 80’s. The econometric analysis of a sample of 28 sub-Saharan African countries, from variables regarded as “representatives” for the adjustment objectives, proves that this assertion cannot be completely rejected.

Modelling fertility:an application of count regression models

Often the lifecycle data occur as count of the vital events and are recorded as integers.The purpose of this article is to model the fertility behavior based on religious,educational,economic,and occupational characteristics.The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model(PRM) based on the National Family Health Survey-3 dataset.The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data.Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model(MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation.Choice of the explanatory factors is limited to the availability of data.Trends and patterns of preference for birth counts suggest that religion,caste,wealth,female education,and occupation are the dominant factors shaping the observed birth process.Empirical analysis suggests that both the models used in the study perform similarly on the sample data.However,fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values.Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.

Monte Carlo study of single-barrier structure based on exclusion model full counting statistics

Different from the usual full counting statistics theoretical work that focuses on the higher order cumulants computation by using cumulant generating function in electrical structures, Monte Carlo simulation of single-barrier structure is performed to obtain time series for two types of widely applicable exclusion models, counter-flows model, and tunnel model. With high-order spectrum analysis of Matlab, the validation of Monte Carlo methods is shown through the extracted first four cumulants from the time series, which are in agreement with those from cumulant generating function. After the comparison between the counter-flows model and the tunnel model in a single barrier structure, it is found that the essential difference between them consists in the strictly holding of Pauli principle in the former and in the statistical consideration of Pauli principle in the latter.

Bayesian Joint Modelling of Survival Time and Longitudinal CD4 Cell Counts Using Accelerated Failure Time and Generalized Error Distributions

Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.

Some Additional Moment Conditions for a Dynamic Count Panel Data Model with Predetermined Explanatory Variables

This paper proposes some additional moment conditions for the linear feedback model with explanatory variables being predetermined, which is proposed by [1] for the purpose of dealing with count panel data. The newly proposed moment conditions include those associated with the equidispersion, the Negbin I-type model and the stationarity. The GMM estimators are constructed incorporating the additional moment conditions. Some Monte Carlo experiments indicate that the GMM estimators incorporating the additional moment conditions perform well, compared to that using only the conventional moment conditions proposed by [2,3].

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

Modelling and predicting low count child asthma hospital readmissions using General Additive Models

Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time inVictoria,Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department ofHealth,Victoria,Australiain 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p

Statistical models for predicting number of involved nodes in breast cancer patients

Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (negative nodes), which can lead to over-dispersion. In this situation, alternative models may better account for over-dispersion due to excess zeros. This study examines data from 1152 patients who underwent axillary dissections in a tertiary hospital in India during January 1993-January 2005. We fit and compare various count models to test model abilities to predict the number of involved nodes. We also argue for using zero inflated models in such populations where all the excess zeros come from those who have at some risk of the outcome of interest. The negative binomial regression model fits the data better than the Poisson, zero hurdle/inflated Poisson regression models. However, zero hurdle/inflated negative binomial regression models predicted the number of involved nodes much more accurately than the negative binomial model. This suggests that the number of involved nodes displays excess variability not only due to unobserved heterogeneity but also due to excess negative nodes in the data set. In this analysis, only skin changes and primary site were associated with negative nodes whereas parity, skin changes, primary site and size of tumor were associated with a greater number of involved nodes. In case of near equal performances, the zero inflated negative binomial model should be preferred over the hurdle model in describing the nodal frequency because it provides an estimate of negative nodes that are at “high-risk” of nodal involvement.

Precise large deviations for sums of random vectors in a multidimensional size-dependent renewal risk model

Consider a multidimensional renewal risk model, in which the claim sizes {Xk, k ≥1} form a sequence of independent and identically distributed random vectors with nonnegative components that are allowed to be dependent on each other. The univariate marginal distributions of these vectors have consistently varying tails and finite means. Suppose that the claim sizes and inter-arrival times correspondingly form a sequence of independent and identically distributed random pairs, with each pair obeying a dependence structure. A precise large deviation for the multidimensional renewal risk model is obtained.

Stochastic Model for Multiple Classes and Subclasses Simple Documents Processing

The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian ML×K/ML×K/1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. In fine, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.

A theoretical extraction scheme of transport information based on exclusion models

In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows model and tunnel model. The first four cumulants of these two exclusion models are computed in a single barrier structure, and their characteristics are obtained. A scheme with the help of the first three cumulants is devised to check a transport process to follow the counter-flows model, the tunnel model or neither of them. Time series generated by Monte Carlo techniques is adopted to validate the abstraction procedure, and the result is reasonable.

A Recursive Binary Tree Model for the Analysis of the Response to Antiretroviral Therapy of HIV Infected Adults in Burkina Faso

In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen.

基于广义线性混合效应模型的森林树木死亡研究

基于计数模型方法,同时考虑样地的随机效应,构建林分水平死亡模型,探究影响树木死亡的因素,以期为森林资源的监测与管理提供参考依据。以美国德州东部森林连续清查的样地数据为数据源,按4∶1的比例将其进行随机抽样,划分为训练集和验证集数据,将立地因子、林分因子和气候因子作为模型的自变量,林木死亡株数则作为模型的因变量,运用计数模型和混合效应模型方法进行模型的构建,并分析影响林木死亡株数的因子。使用赤池信息准则(AIC)、贝叶斯信息准则(BIC)和-2倍对数似然函数值(-2logL)3种模型评价指标评估各模型间的拟合效果;采用平均绝对误差(MAE)和均方根误差(RMSE)2种评价指标评估其预测效果,以便筛选出最佳的林分水平死亡模型。结果表明:立地因子方面,林木死亡株数与海拔(P<0.01)呈显著的负效应,与坡度(P<0.05)呈显著的正效应,说明林木死亡株数随海拔的升高而减少,随坡度的增加而增多;林分因子方面,林木死亡株数与林分年龄(P<0.001)和树木基面积(P<0.001)呈显著的正效应,与林分平方平均胸径(P<0.001)和林分密度(P<0.05)呈显著的负效应,说明林木死亡株数随林分年龄的增加和树木基面积的增大而增加,随林分平方平均胸径和林分密度的增大而减少;气候因子方面,林木死亡株数与SPEI(P<0.05)、干旱长度(P<0.001)、年平均温度(P<0.001)和夏季平均降雨量(P<0.05)均呈显著的负效应,与夏季平均温度(P<0.001)呈显著的正效应,说明林木死亡株数随干旱强度和夏季平均温度的增加而增多,随干旱长度、年平均温度和夏季平均降雨量的增加而减少。在基础计数模型中,零膨胀负二项(ZINB)模型的拟合效果最好。而加入样地随机效应后,混合效应模型的拟合精度明显有所提高。基于所有模型模拟结果的比较,得出德州东部森林的林分水平死亡模型以ZINB-mixed模型为最优模型。

Mixture Models for Estimating the Number of Drug Users in Thailand 2005-2007

It is difficult to measure the sizes of illegal drug user populations directly by using the survey method because of many “hidden drug addicts” and the difficulty of receiving a true response. Systematic and routine information on treatment episodes of drug users is adopted to estimate the population size in this study. Mixture models of zero-truncated Poisson distributions using the nonparametric maximum likelihood estimators (NPMLE) by means of capture-recapture repeated count data were used to project the number of drug users. The method was applied to surveillance data of drug users identified by treatment episodes in over 1140 health treatment centers in Thailand from the Bureau of Health Service System Development, Ministry of Public Health. We presented how this mixture model could be utilized to construct the unobserved frequency of drug users with no treatment episode and further estimated the total population size of drug users in the country from 2005 to 2007. The result of simulation was confirmed that mixture model is suitable when population is large. By means of mixture models, the estimations for the number of drug users were fitted with excellent goodness-of-fit values and we were also compared to the conventional Chao estimates. The NPMLE for the total number of drug users in Thailand 2005, 2006, and 2007 were 184,045 (95% CI: 181,297-86,793), 230,665 (95% CI: 226,611-234,719), 299,670 (95% CI: 294,217-305,123), respectively, also 125,265 (95% CI: 123,092-127,142), 166,287 (95% CI: 163,222-169,352), 228,898 (95% CI: 224,766 - 233,030) for the number of methamphetamine (Yaba) users, and 11,559 (95% CI: 10,234-12,884), 11,333 (95% CI: 9276-13,390), 8953 (95% CI: 7878-10,028) for the number of heroin users, respectively. The numbers of marijuana, kratom-plant, opium, and inhalant users were underestimated because their symptoms were mild and not severe enough to remedy in health treatment centers which led to the smaller size of the total number of drug users. The well-estimated sizes of heroin and methamphetamine addicts are high reliable because they are based on clearly evident count with a severe addiction problem to health treatment centers. The estimation by means of mixture models can be recommended to monitor drug demand trend and drug health service routinely;it is easy to calculate via the available programs MIXTP based on request.

乙肝相关慢加急性肝衰竭患者6个月预后影响因素分析及模型构建

目的分析乙肝相关慢加急性肝衰竭(Hepatitis B virus associated chronic acute liver failure,HBV-ACLF)患者6个月预后影响因素及预后模型的建立。方法以2017年5月-2022年5月新疆医科大学第一附属医院感染中心收治的131例HBV-ACLF患者为研究对象。随访6个月,根据患者预后结果分为死亡组(n=60)和存活组(n=71)。收集患者血常规、肝功能、肾功能及血气指标,采用Logistics回归分析筛选影响HBV-ACLF患者6个月预后的因素,并将筛选的因素使用R 4.3.0软件进行绘制列线图预测模型,绘制校准曲线和受试者工作特征曲线(Receiver operating characteristic,ROC)拟合度及预测效能评估,根据决策曲线评估预测模型的临床适用性。结果二元Logistics回归分析发现,血小板计数、白蛋白及凝血酶原时间是影响HBVACLF患者6个月预后的重要因素,建立的预后模型区分度和校准能力较好。自发性腹膜炎在存活组和死亡组HBV-ACLF患者并发症中占比均最高。结论通过白蛋白,血小板计数和凝血酶原时间建立的预测模型对HBV-ACLF患者6个月预后有较好的预测价值。

CD41单克隆抗体建立慢性免疫性血小板减少症小鼠模型失败的初步分析

目的:通过向Balb/c小鼠腹腔注射不同次数和不同剂量CD41单克隆抗体(MWReg30),探究该方法能否构建出一种稳定的慢性免疫性血小板减少症(ITP)模型,并在造模结束后采用党参提取液干预此模型小鼠,以明确其对造模小鼠异常血小板计数的调整作用。方法:首先,使用MWReg30的常用剂量对Balb/c小鼠建立慢性ITP模型,并在抗体注射结束后探究党参提取液对造模小鼠的影响;随后,分别对不同组别的小鼠进行MWReg30的2次注射和2倍剂量注射,明确注射次数和剂量的增加是否有助于建立慢性ITP模型。结果:向小鼠体内注射MWReg30的造模方式不能使Balb/c小鼠血小板数量长期维持低水平,并且通过增加抗体注射次数和剂量的方法也难以实现此目的。但是,MWReg30的2次注射和2倍剂量注射分别使小鼠血小板计数在抗体注射结束7 d内和14 d内反弹升高至较高水平,且低剂量党参提取液干预造模小鼠可使其异常升高的血小板数量呈现下降趋势。结论:对小鼠腹腔注射MWReg30的造模方法无法复制出稳定的慢性ITP模型,此方法能否成为一种理想的慢性ITP模型建立方式仍有待进一步证实。而增加MWReg30的注射剂量能够延长模型小鼠血小板计数低水平的维持时间;且低剂量的党参提取液可能会对造模小鼠异常升高的血小板数量起纠正作用。

基于频域分析的机载液压驱动装置疲劳寿命预测方法研究

目的 采用时域雨流法和4种频域振动疲劳分析方法对机载液压驱动装置的危险部位进行寿命预测及频域方法适用性研究。方法 首先通过有限元随机振动分析得到该结构耳片危险区域的应力PSD,计算的谱宽系数都集中在0.3~0.35,是窄带和宽带分界区域。然后运用4种典型的载荷谱估计模型,即三区间法、基于Dirlik雨流幅值经验模型、用于窄带过程的Rayleigh分布模型、Weibull分布模型,得到危险位置的疲劳寿命安全系数。接着将应力PSD映射为应力-时间序列,并基于雨流计数法得到疲劳寿命安全系数。最后以时域疲劳预测结果基准,对4种频域方法的适用性进行讨论。结果 预测结果显示,Dirlik方法预测的9个安全系数最为可靠,Rayleigh法结果一般,三区间法和Weibull法最差。结论 在窄带和宽带分界区域的谱宽系数,建议采用Dirlik方法,不建议采用三区间法和Weibull法。

基于ResNet34模型的大菱鲆鱼苗识别计数方法

鱼苗数量的精准统计对提升苗种品质评价、养殖密度估算、鱼苗销售等环节的智能化水平具有重要作用。针对大菱鲆鱼苗个体小、透明度高以及体型不规则等影响计数精准度的问题,提出了一种基于ResNet34的大菱鲆鱼苗识别计数方法。首先,设计了一套适用于微小目标计数需要的图像采样装置,采用图像预处理方法实现鱼苗前景分割和初步定位。为了有效统一样本空间和待识别目标空间,利用最小外接矩规则化初步定位前景图像,构建图像样本集。大菱鲆鱼苗识别阶段,利用相同预处理方法获取待识别目标区域,并引入ResNet34模型作为识别模型实现待识别目标区域苗种识别;最后,通过统计所有待识别目标识别数量结果实现大菱鲆苗种计数。结果显示:本方法在微小鱼苗识别计数方面取得了较好的精度,利用ResNet34模型的大菱鲆鱼苗的识别平均准确率达到94.27%,比基于SVM方法(识别精度85.8%)和AlexNet(识别精度87.04%)方法识别精度分别提高7.4个百分点和8.64个百分点,优于ResNet18(识别精度93.21%)和ResNet50(识别精度93.83%)等相似结构的识别效果。本模型鱼苗计数的平均准确率达到96.28%。研究表明,提出的样本集构建和识别方法能够满足微小目标计数需求,可为鱼类苗种计数提供了技术借鉴。

矸石基混凝土轴心受压声发射特性与损伤本构模型

为研究不同水泥取代率、不同水胶比下矸石基混凝土轴心受压时的力学性能,及推广应用矸石基混凝土这一绿色建筑材料,通过轴心受压试验和声发射试验,研究了轴心受压时不同煤矸石粉掺量、不同水胶比下矸石基混凝土的应力-应变曲线、声发射振铃计数和声发射累积能量随煤矸石粉掺量和水胶比的变化规律,提出了矸石基混凝土轴心受压时的本构关系。结果表明:相比普通混凝土,矸石基混凝土的力学性能有一定程度的改善,主要表现为其峰值应力和峰值应变增加;矸石基混凝土轴心受压应力-应变全曲线与普通混凝土基本一致,煤矸石粉掺量为20%时峰值应力达到最大值;声发射振铃计数可以有效描述矸石基混凝土的损伤演化规律,随着煤矸石粉取代率的增大,振铃计数先减少后增加。考虑煤矸石粉和水胶比对矸石基混凝土轴心受压时的影响,提出了具有较高精度的矸石基混凝土轴心受压本构关系,这为矸石基混凝土力学性能的进一步研究提供了参考,也对推广应用的结构计算有一定参考价值。