限制性立方样条在Cox比例风险回归模型中的应用

限制性立方样条Cox比例风险回归模型分析是流行病学多因素生存分析的重要方法。本研究通过对典型Cox比例风险回归模型和限制性立方样条Cox比例风险回归模型比较,阐述了典型Cox比例风险回归模型的局限性,以及限制性立方样条Cox比例风险回归模型基本原理与实现过程。在随访数据不满足典型Cox比例风险回归模型应用条件时,可采用该方法实现连续性暴露与结局之间的关联分析。     

利用三次样条函数考察Cox模型比例风险假定

目的 介绍一种检查Cox模型比例风险假定的假设检验方法。方法 利用时间的三次样条函数评价Cox比例风险回归模型中的时协变量交互作用项。结果 该法灵活有效，并且提供LHRF的点估计和区间估计。结论 三次样条回归作为一种检验方法，可与其他检验方法或图法结合使用，以考察Cox模型比例风险假定。     

鞅残差在Cox回归模型诊断中的应用

目的：探讨Cox模型中某给定连续变量对生存效应的最佳函数形式。方法：不含该变量的Cox模型下的鞅残差对该协变量作图可揭示该变量在全模型中的函数形式。结果：实例分析表明,Loess平滑鞅残差图可用于Cox模型中协变量函数形式的确定。结论：建议分析者把鞅残差图纳入Cox模型分析中,对协变量的生存效应作出正确估计。     

限制性立方样条在非线性回归中的应用研究

目的探讨限制性立方样条在自变量和应变量非线性相关时的应用。方法应用限制性立方样条分析泰坦尼克号乘客生存数据,通过LOWESS平滑曲线和AIC值比较限制性立方样条和分段亚元回归的分析效果。结果限制性立方样条结合logistic回归可以较好地拟合自变量和应变量的非线性关系,并能发现重要的关键点。结论限制性立方样条在非线性影响因素分析,特别是剂量效应分析中有比较好的应用前景。     

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。     

基于三次样条Cox回归的剂量-反应关系分析

目的 将Cox回归应用于剂量-反应关系分析.方法 采用限制/自然三次样条Cox回归考察主效应变量与结局间的剂量-反应关系或二者是否有线性趋势.结果 三次样条Cox回归分析结果可提供线性趋势检验的P值,以及风险比随定量变量变化的曲线图(包括置信域).结论 限制性三次样条Cox回归能利用现行统计软件如SAS拟合,是剂量-反应关系的有效分析方法.     

队列研究资料分析中Cox比例风险回归模型的时间尺度选择和应用

Cox比例风险回归模型（Cox模型）是时间-事件数据分析中常用的多因素分析方法,拟合Cox模型时一个关键问题是如何选择合适的与结局事件发生相关的时间尺度。目前国内开展的队列研究在资料分析中较少关注Cox模型的时间尺度选择问题。本研究对文献报道中常见的几种时间尺度选择策略进行简要介绍和比较;并利用上海女性健康队列资料,以中心性肥胖与肝癌发病风险的关联为例,说明选择不同时间尺度的Cox模型对数据分析结果的影响;在此基础上提出几点Cox模型时间尺度选择上的建议,以期为队列研究资料的分析提供参考。     

区间截尾Cox比例风险模型在健康教育中的应用

目的 介绍区间截尾型Cox比例风险模型及其在健康教育中的应用.方法 采用区间截尾型Cox比例风险模型评价健康教育对降低HIV转阳的作用.结果 接受健康教育对象HIV阳性的危险比未接受的降低了41.6%.结论 区间截尾型Cox比例风险模型在健康教育研究中是可行的.     

Logistic和Cox回归模型在定群研究资料分析中的应用和对比

Ｌｏｇｉｓｔｉｃ和Ｃｏｘ回归模型在定群研究资料分析中的应用和对比项永兵，高玉堂，金凡Ｌｏｇｉｓｔｉｃ回归模型在流行病学研究资料分析中的应用众所周知￣［１，２］。Ｃｏｘ回归模型的应用也日益广泛，尤其在队列随访资料分析方面￣［３～１０］。因而，两者在定群...     

中学生近视发病影响因素Cox回归分析

近视是中学生的常见病和多发病。其患病率居中学生常见病的首位,对于近视的影响因素,国内外学者用多种方法进行了研究．如双生子研究、Logistic回归分析等,但用Cox比例风险模型的研究尚未见报道,该文尝试应用这一方法对近视发病的影响因素进行分析,为近视的防治提供依据。     

On the self-triggering Cox model for recurrent event data

Recurrent event data frequently occur in longitudinal studies when subjects experience more than one event during the observation period. Often, the occurrence of subsequent events is associated with the experience of previous events. Such dependence is commonly ignored in the application of standard recurrent event methodology. In this paper, we utilize a Cox-type regression model with time-varying triggering effect depending on the number and timing of previous events to enhance both model fit and prediction. Parameter estimation and statistical inference is achieved via the partial likelihood. A statistical test procedure is provided to assess the existence of the triggering effects. We demonstrate our approach via comprehensive simulation studies and a real data analysis on chronic pseudomonas infections in young cystic fibrosis patients. Our model provides significantly better predictions than standard recurrent event models.     

Bootstrap investigation of the stability of a Cox regression model

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.     

手术类单病种费用影响因素的Cox回归分析

目的为了控制日益增长的医疗费用,通过Cox回归分析找出影响单病种费用的主要因素,为制定有针对性的费用控制政策提供参考依据。方法对3768例手术类单病种病人费用的影响因素进行Cox回归分析。结果提示手术类单病种发生高费用与病人性别、年龄、手术方式、麻醉方式、麻醉检测时间、药占比、病人付款方式密切相关。结论通过COX回归分析,明确了手术类单病种费用的主要影响因素,提出严格筛选病种、结合合理的临床路径和合理的花费开展费用控制工作。     

实现logistic与Cox回归相乘相加交互作用的临床实践宏程序

病例对照研究常采用条件或非条件logistic分析,生存资料分析常采用Cox比例模型,但多数文献仅纳入主效应模型,然而广义线性模型不同于一般线性模型,其交互作用分为相乘交互与相加交互作用,前者只有统计学意义而后者更符合生物学意义。笔者以SAS 9.4软件编写宏,在计算logistic与Cox相乘交互项同时计算交互对比度、归因比、交互作用指数指标及利用Wald、Delta、PL(profile likelihood) 3种方法的可信区间评价相加交互作用,便于临床流行病学与遗传学大数据分析相乘相加交互作用时参考。     

Cox及其拓展模型在基于队列的依时暴露因素效应估计中的应用

队列研究的特点之一是暴露因素会随时间而改变,如何充分利用暴露因素及其协变量的变化及其相互关系,从而获得更真实的暴露因素与结局关系是目前的研究热点。本研究以开滦队列为例,探讨基于基线暴露状态、随时间变化的暴露信息以及同时控制依时混杂因素时,如何利用Cox比例风险回归及其拓展模型,包括依时Cox回归及边际结构模型,探讨FPG与肝癌的关系,概述并比较了上述拓展模型的基本原理、应用条件、估计结果及结果解释。     

On robustness of marginal regression coefficient estimates and hazard functions in multivariate survival analysis of family data when the frailty distribution is mis-specified

The shared frailty model is an extension of the Cox model to correlated failure times and, essentially, a random effects model for failure time outcomes. In this model, the latent frailty shared by individual members in a cluster acts multiplicatively as a factor on the hazard function and is typically modelled parametrically. One commonly used distribution is gamma, where both shape and scale parameters are set to be the same to allow for unique identification of baseline hazard function. It is popular because it is a conjugate prior, and the posterior distribution possesses the same form as gamma. In addition, the parameter can be interpreted as a time-independent cross-ratio function, a natural extension of odds ratio to failure time outcomes. In this paper, we study the effect of frailty distribution mis-specification on the marginal regression estimates and hazard functions under assumed gamma distribution with an application to family studies. The simulation results show that the biases are generally 10% and lower, even when the true frailty distribution deviates substantially from the assumed gamma distribution. This suggests that the gamma frailty model can be a practical choice in real data analyses if the regression parameters and marginal hazard function are of primary interest and individual cluster members are exchangeable with respect to their dependencies.     

Testing goodness of fit in regression: a general approach for specified alternatives

When fitting generalized linear models or the Cox proportional hazards model, it is important to have tools to test for lack of fit. Because lack of fit comes in all shapes and sizes, distinguishing among different types of lack of fit is of practical importance. We argue that an adequate diagnosis of lack of fit requires a specified alternative model. Such specification identifies the type of lack of fit the test is directed against so that if we reject the null hypothesis, we know the direction of the departure from the model. The goodness‐of‐fit approach of this paper allows to treat different types of lack of fit within a unified general framework and to consider many existing tests as special cases. Connections with penalized likelihood and random effects are discussed, and the application of the proposed approach is illustrated with medical examples. Tailored functions for goodness‐of‐fit testing have been implemented in the R package globaltest . Copyright © 2012 John Wiley & Sons, Ltd.     

Efficiency of the logistic regression and Cox proportional hazards models in longitudinal studies

Both logistic regression and Cox proportional hazards models are used widely in longitudinal epidemiologic studies for analysing the relationship between several risk factors and a time-related dichotomous event. The two models yield similar estimates of regression coefficients in studies with short follow-up and low incidence of event occurrence. Further, with just one dichotomous covariate and identical censoring times for all subjects, the asymptotic relative efficiency of the two models is very close to 1 unless the duration of follow-up is extended. We generalize this result to several qualitative or quantitative covariates. This was motivated by the analysis of mortality data from a study where all subjects are followed up during the same fixed period without loss except by death. Logistic and Cox models were applied to these data. Similar results were obtained for the two models in shorter periods of follow-up of five years or less, but not in longer periods of ten years or more, where the survival rate was lower.     

Reweighting estimators for Cox regression with missing covariate data: analysis of insulin resistance and risk of stroke in the Northern Manhattan Study

Incomplete covariates often obscure analysis results from a Cox regression. In an analysis of the Northern Manhattan Study (NOMAS) to determine the influence of insulin resistance on the incidence of stroke in nondiabetic individuals, insulin level is unknown for 34.1% of the subjects. The available data suggest that the missingness mechanism depends on outcome variables, which may generate biases in estimating the parameters of interest if only using the complete observations. This article aimed to introduce practical strategies to analyze the NOMAS data and present sensitivity analyses by using the reweighting method in standard statistical packages. When the data set structure is in counting process style, the reweighting estimates can be obtained by built-in procedures with variance estimated by the jackknife method. Simulation results indicate that the jackknife variance estimate provides reasonable coverage probability in moderate sample sizes. We subsequently conducted sensitivity analyses for the NOMAS data, showing that the risk estimates are robust to a variety of missingness mechanisms. At the end of this article, we present the core SAS and R programs used in the analysis.