基于R软件的逐步Cox回归模型拟合及Nomogram预测图绘制

目的建立基于R软件的逐步Cox回归模型拟合及列线图（Nomogram）绘制方法。方法以R软件的Survival包分别对R自带的Lung数据集228例晚期肺癌病人数据和美国监测、流行病、终点结局（surveillance, epidemiology and end results,SEER）数据库获取的6 341例胰腺癌病人数据拟合逐步Cox回归模型,采用rms包绘制列线图,以校准曲线对模型效果进行验证评价。结果Cox回归模型显示性别、ph.ecog评分是晚期肺癌病人生存的独立影响因素,年龄、肿瘤部位、肿瘤分化程度、TNM分期、肿瘤大小与淋巴结阳性率为胰腺癌病人生存的独立影响因素。基于以上影响因素绘制的2个列线图可较准确地评估肺癌和胰腺癌病人1年、2年生存概率,校准曲线显示1年、2年实际生存概率与预测概率大致接近。结论R绘制列线图方便,可直观地预测个体生存概率。     

生存分析中的回归模型

生存分析是肿瘤及其他慢性病预后分析的主要方法,自20世纪70年代中期以来,得到迅速的发展,无论在理论或应用方面都受到了人们的重视.常用的回归分析方法可分为三种类型：参数回归、半参数回归以及非参数回归.     

采用Cox回归描述多变量生存期和预测多变量生存率

目的：采用Cox回归描述多变量生存期和预测多变量生存率。方法：介绍生存函数和结合线性内插法的基本原理,用一个实例和SPSS8．0和Cox regression过程说明该计算方法。结果：列出该法和Kaplan-Meier法的比较表和预测某个体的多变量生存率,结论：涉及多普量影响的生存分析时,使用该法进行描述和预测更客观和直观。     

Cox回归模型与对数线性回归模型在生存分析中应用的比较

运用Ｃｏｘ回归模型和对数线性回归模型对１６８９例肝癌病人生存时间的分析，发现Ｃｏｘ回归模型能够提供主要的预后影响因素，其结果与特定的参数回归模型相接近，使临床上能够快速地获得预后的影响因素。     

有监督的主成分分析和偏Cox回归模型在基因数据生存预测中的应用

目的探讨有监督的主成分分析及偏Cox回归模型在基因数据生存预测中的应用。方法针对基因数据的协变量个数大于样本例数,以及变量间存在相关性等特点进行模拟研究,并对国际上公开的三个基因数据集进行分析,考察两种模型的预测性能。结果模拟研究显示随着影响生存的基因块的方差的增大以及组内相关系数的增高,两种方法的预测性能变好;随着删失比例的增加,两种方法的预测性能变差。实例分析提示不同的数据集最适方法不同。结论 SuperPC和偏Cox回归都适用于基因数据的生存分析。在模拟中SuperPC比偏Cox回归的表现好,但偏Cox回归计算速度较快。     

半参数回归模型介绍

半参数回归模型介绍＊第三军医大学（６３００３８）易东王文昌张蔚许汝福回归分析技术是揭示医学和生物学现象中各个因素内在联系的有力工具。半参数回归模型是介于参数回归和非参数回归的一种新的回归分析技术，其研究起源于８０年代，并在近几年引起了许多统计学者的关...     

随机生存森林和Cox回归对ICU内缺血性卒中患者死亡风险的预测性能比较

目的 以重症监护病房(intensive care unit,ICU)内缺血性卒中患者为研究对象,分别构建随机生存森林和Cox回归并对两者预测效果进行比较。方法 以是否发生院内死亡及在ICU内接受治疗的时间为结局变量,以人口学特征、实验室指标、合并症等为预测变量,将研究对象按7:3的比例随机划分成训练集和测试集,在训练集提取重要变量并构建模型,在测试集进行模型评价。用受试者工作特征(receiver operation characteristic,ROC)曲线下面积(area under curve,AUC)评价区分度,以Brier score评价校准度,并综合灵敏度(sensitivity,TP)、特异度(specificity,TN)、阳性预测值(positive predictive value,PPV)和阴性预测值(negative predictive value,NPV)对模型比较。结果 共纳入1 505例缺血性卒中患者,其中217例发生院内死亡。500次Bootstrap自助抽样结果表明,在测试集中,随机生存森林和Cox回归AUC_7day分别为0....     

生存分析中的最小二乘回归模型

回归模型的建立是生存分析的基本问题之一。指数分布假定下的线性模型因其对生存分布的特殊要求,不便于实际应用。Cox模型的提出及发展把生存分析的理     

半参数回归模型及模拟实例分析

目的 放宽经典线性模型中的解释变量的线性假定和探讨半参数回归分析模型。方法 利用最小惩罚二乘原理构造加权惩罚平方和,通过广义交互有效得分函数自动选择光滑参数值,用直接法求解方程组。结果 用SAS程序实现了半参数回归分析。得到了回归系数向量和样条函数的最小惩罚二乘估计,模拟实例表明,半参数回归模型较传统的线性模型有较强的适应性。结论 半参数回归模型是经典线性模型和非参数回归模型的一个混合体。可作为回归分析的一种新技术得到广泛应用。     

压缩预测在Cox模型预测改善中的应用

目的改善Cox回归模型预测.方法引入压缩预测技术.结果通过在确认样本上计算得到压缩因子和校正预后指数,可改善预测.结论压缩预测在一定程度上校正模型过度拟合,有效改善预测.     

SEGMENTED REGRESSION WITH ERRORS IN PREDICTORS: SEMI-PARAMETRIC AND PARAMETRIC METHODS

We consider the estimation of parameters in a particular segmented generalized linear model with additive measurement error in predictors, with a focus on linear and logistic regression. In epidemiologic studies segmented regression models often occur as threshold models, where it is assumed that the exposure has no influence on the response up to a possibly unknown threshold. Furthermore, in occupational and environmental studies the exposure typically cannot be measured exactly. Ignoring this measurement error leads to asymptotically biased estimators of the threshold. It is shown that this asymptotic bias is different from that observed for estimating standard generalized linear model parameters in the presence of measurement error, being both larger and in different directions than expected. In most cases considered the threshold is asymptotically underestimated. Two standard general methods for correcting for this bias are considered; regression calibration and simulation extrapolation (simex). In ordinary logistic and linear regression these procedures behave similarly, but in the threshold segmented regression model they operate quite differently. The regression calibration estimator usually has more bias but less variance than the simex estimator. Regression calibration and simex are typically thought of as functional methods, also known as semi-parametric methods, because they make no assumptions about the distribution of the unobservable covariate X. The contrasting structural, parametric maximum likelihood estimate assumes a parametric distributional form for X. In ordinary linear regression there is typically little difference between structural and functional methods. One of the major, surprising findings of our study is that in threshold regression, the functional and structural methods differ substantially in their performance. In one of our simulations, approximately consistent functional estimates can be as much as 25 times more variable than the maximum likelihood estimate for a properly specified parametric model. Structural (parametric) modelling ought not be a neglected tool in measurement error models. An example involving dust concentration and bronchitis in a mechanical engineering plant in Munich is used to illustrate the results. © 1997 by John Wiley & Sons, Ltd.     

ANALYSIS OF SEMI-PARAMETRIC REGRESSION MODELS WITH NON-IGNORABLE NON-RESPONSE

In this article we consider the problem of making inferences about the parameter β₀ indexing the conditional mean of an outcome given a vector of regressors when a subset of the variables (outcome or covariates) are missing for some study subjects and the probability of non-response depends upon both observed and unobserved data values, that is, non-response is non-ignorable. We propose a new class of inverse probability of censoring weighted estimators that are consistent and asymptotically normal (CAN) for estimating β₀ when the non-response probabilities can be parametrically modelled and a CAN estimator exists. The proposed estimators do not require full specification of the likelihood and their computation does not require numerical integration. We show that the asymptotic variance of the optimal estimator in our class attains the semi-parametric variance bound for the model. In some models, no CAN estimator of β₀ exists. We provide a general algorithm for determining when CAN estimators of β₀ exist. Our results follow after specializing a general representation described in the article for the efficient score and the influence function of regular, asymptotically linear estimators in an arbitrary semi-parametric model with non-ignorable non-response in which the probability of observing complete data is bounded away from zero and the non-response probabilities can be parametrically modelled. © 1997 by John Wiley & Sons, Ltd.     

REGRESSION MODELS FOR RECURRENT EVENT DATA: PARAMETRIC RANDOM EFFECTS MODELS WITH MEASUREMENT ERROR

Statistical methodology is presented for the statistical analysis of non-linear measurement error models. Our approach is to provide adjustments for the usual maximum likelihood estimators, their standard errors and associated significance tests in order to account for the presence of measurement error in some of the covariates. We illustrate the technique with a mixed effects Poisson regression model for recurrent event data applied to a randomized clinical trial for the prevention of skin tumours. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 853–864 (1997).     

基于降秩回归的膳食模式与代谢综合征相关性研究

目的利用降秩回归(RRR)分析法探讨膳食模式与代谢综合征(MetS)的相关性。方法选取参加2006年中国健康与营养调查的4493名成年居民(男性2035人,女性2458人),采用RRR方法,用个人连续3 d 24 h膳食回顾和家庭食物称重法数据建立膳食模式,2009年测量结局变量,运行Logistic回归以发现RRR方法得出的不同性别居民各种膳食模式得分与MetS及其组份关联强度差异。结果不同性别居民RRR模式膳食得分数均与面类及其制品摄入量呈正相关,而与米类及其制品、深色蔬菜、动物油类摄入量呈负相关。与膳食模式得分最低四分位数相比,"男性RRR模式"(面类及其制品、植物油、鱼和海鲜、蛋类干豆类、零食小吃及坚果类)、"女性RRR模式"(面类及其制品、杂粮、酱油类及盐)、最高四分位数MetS发病率调整优势比(OR)分别为1.84(95%CI 1.28,2.65)、1.32(95%CI0.90,1.94)。结论本研究表明,RRR方法能较好地构建与MetS及其组份发生风险密切相关的膳食模式,采用RRR方法有利于研究膳食模式与MetS相关性。     

A BIVARIATE PARAMETRIC MODEL FOR SURVIVAL AND INTERMEDIATE EVENT TIMES

In diseases such as acquired immune deficiency syndrome (AIDS), there is great interest in describing the frequency of secondary diagnoses that occur during the course of the disease and their effect on survival. Casting this situation in a more general framework, one distinguishes a terminal event (TE) and an intermediate event (IE) that may or may not occur. In epidemiologic applications the TE is usually death. Earlier studies of IE and TE times have used the latter to censor the IE time for individuals who do not present it. For such cases, we argue that more appropriately the TE removes the individual from the risk set for the IE. With this view, one distinguishes observations of four types, each with a different formula for its likelihood contribution. We propose an approach that uses separate parametric models for the marginal distribution of the survival time D and for the conditional distribution of the time R to the IE given D = d and R⩽D. A central quantity is the probability of presenting the IE given the occurrence of the TE at time d. This function of d can reveal important connections between the two events. We suggest a model derived from Weibull distributions where the parameters control the shape of this function. One can obtain inferences about other probabilities of interest such as the proportion of individuals who present the IE, P(R⩽D), the marginal distribution of R among the IE cases, P(R>r∣R⩽D) and the residual survival after the IE occurs, P(D−R>v∣R⩽D, R = r). We apply the model to the analysis of time to secondary Kaposi's sarcoma (KS) diagnosis and time to death in a large cohort study of homosexual men infected with the human immunodeficiency virus type 1 (HIV) and who had an initial non-KS AIDS diagnosis.     

LIMITATIONS OF TELEPHONE BASED SELECTION AND INTERVIEW PROCEDURES

The use of telephone based sampling and interview techniques has become more popular in epidemiological research in recent years. Previous studies of these designs have shown good response rates and reliability of information which is comparable to that obtained from the use of mail and personal interview. This paper reports on the results of a pilot study, designed to examine the possible limitations of the use of telephone interviews, in population based studies in Australia. The findings show that as many as 31 per cent of people listed on the Federal electoral rolls in the Hunter Valley region of New South Wales may be inaccessible to interview by telephone, and that these people differ from those who are accessible on a number of important socioeconomic and health indicators. The potential biases introduced by telephone surveys should be considered and explored by those wishing to use them.     

REGRESSION MODELS FOR INTERVAL CENSORED SURVIVAL DATA: APPLICATION TO HIV INFECTION IN DANISH HOMOSEXUAL MEN

This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men.     

ON THE RELATIONSHIP BETWEEN RESPONSE TO TREATMENT AND SURVIVAL TIME

In this paper we review several approaches which have been used to investigate the relationship between survival time and response to treatment. We show that the approaches based on summary data are subjected to various types of biases (publication bias, confounding by prognostic features, ecologic bias) and are therefore of doubtful value. We also discuss several approaches based on individual patient data. Comparisons of survival by response are generally subject to length-biased sampling, and are therefore inadequate. The landmark method is adequate when responses occur soon after starting treatment, but not when responses may appear later in the course of the disease. For responses which can occur over extended periods of time, response must be considered as a time-dependent covariate. Using data from randomized trials in advanced colorectal cancer, we show that response is a potent and independent prognostic factor for survival in this disease. Analyses using the landmark method yield results essentially equivalent to those in which response is considered as a time-dependent covariate. The hazard rate of responders is about half that of non-responders, after taking the patient's performance status into account. The issue of response as a surrogate marker for survival is taken up further in a separate paper.     

Poisson和Cox回归模型在队列随访资料分析中的应用和对比

Ｐｏｉｓｓｏｎ与Ｃｏｘ回归模型是流行病学队列随访资料分析中常用的两类多变量分析方法。本文对有关这两类多变量回归模型的统计方法等问题进行了系统的回顾（相乘模型），并用一个实例的结果来说明两者的应用。从本文的结果和讨论来看，Ｐｏｉｓｓｏｎ和Ｃｏｘ回归模型均适合于队列随访资料的分析，但两者各有一些优势和不足。最后，笔者就目前两者的应用情况和相互比较提出了一些看法。此外，还讨论了其它形式的回归模型（相加模型）及在回归模型中如何引入外部对照率等。     

基于LMS回归的一步M估计与加权最小二乘估计

探讨在GM假设条件不满足情况下对线性回归模型的稳健估计方法。方法：介绍了估计的稳健性尺度和有效性的概念,采用基于LMS估计的M估计及加权最小二乘估计做稳健回归。结果：通过对一个模拟数据的分析,说明这两种稳健估计结果均优于经典的M估计及LS估计。结论：本文介绍的两种估计方法同时具有较高的稳健性及有效性,并可作近似的稳健推断。