基于参数和半参数回归模型的生存预测及其SAS实现

本文采用SAS8.2中的LIFEREG和PHREG过程和宏程序,阐述生存分析基于参数回归和半参数Cox回归模型的生存预测及其SAS实现方法.利用SAS可输出不同特征个体的中位生存时间、不同时间的预测生存率以及风险函数图,在生存分析中利用SAS还可方便迅速地对具有截尾数据的资料进行生存预测.     

限制平均生存时间回归模型在生存分析中的应用

目的 探讨限制平均生存时间(restricted mean survival time,RMST)回归模型在生存数据分析中的应用。 方法 运用伪值估计方法对医学数据进行限制平均生存时间回归模型实例分析,并与常见生存分析模型进行比较。 结果 RMST回归模型无特定模型假设,适用于不满足比例风险假定的生存数据;实例分析显示,RMST模型构建灵活,可通过设定多个τ值在多个时间段内进行估计;犯第一类错误的概率低于Cox比例风险模型,模型估算结果容易解释,能够提供在临床实践中更为实用的结论。 结论 在不满足比例风险假定且生存曲线有较大交叉的情形下,限制性平均生存时间模型能够提供稳定有效且易于解释的效应估计,在生存分析领域具有优良的适用性,可以作为Cox比例风险模型分析结果的补充。     

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。     

基于分位数回归的睡眠时长对卒中发病风险的影响研究

目的 分析睡眠时长对卒中发病风险的影响以及在不同风险人群中的影响效应.方法 利用中国营养与健康调查2015年数据,通过WLS回归和分位数回归分析睡眠时长对卒中发病风险的影响.结果 共纳入研究对象9566人,WLS回归显示睡眠时长不足和过多会分别增加2.7％和2.4％的卒中发病风险;分位数回归显示随着分位数的提高,睡眠时...     

分位数回归模型在公共卫生领域中的应用及SAS实现

目的 探讨分位数回归模型在公共卫生领域中应用及SAS实现,为该方法推广使用提供参考。方法 在介绍分位数回归模型基本概念基础上,介绍了分位数回归模型应用情景与优势,并以血清克拉拉细胞蛋白水平和第一秒用力呼气容积/用力肺活量(FEV₁/FVC)变化数据为例,与简单线性回归模型相比,探讨分位数回归模型应用及SAS实现过程。结果 分位数回归模型不仅可以分析血清克拉拉细胞蛋白水平对FEV₁/FVC均值的影响,还可以分析血清克拉拉细胞蛋白水平对FEV₁/FVC不同分位数的影响,得到更全面的信息,且通过统计软件SAS可方便实现。结论 分位数回归模型可弥补简单线性回归模型仅关注应变量均值特征而不能分析其完整分布特征的不足,SAS软件提供了相对成熟的分析语句,值得推广。     

区间截尾Cox比例风险模型及其应用

Ｃｏｘ比例风险模型是生存分析中比较常用的一种方法 ,Ｃｏｘ比例风险模型假定说明变量的效果具有参数形式 ,但允许基准生存函数不具有特定的形式 ,所以应用范围非常广泛。但由于资料中常含有截尾数据(ｃｅｎｓｏｒｅｄｄａｔａ) ,使Ｃｏｘ比例风险模型更加复杂。根据个体生存时间与观察时间之间的关系可以分为 3类 :若个体的生存时间长于观察时间 ,称为右截尾数据 ;若个体的生存时间短于观察时间 ,称为左截尾数据 ;若个体的生存时间界于两次观察之间 ,则称为区间截尾数据。目前国内对左截尾和右截尾的Ｃｏｘ比例风险模型研究较多〔1,2〕,有…     

生存分析方法在道路交通事故致颅脑损伤住院时间估计中的应用

目的探讨用生存分析方法对道路交通事故致颅脑损伤住院时间进行估计。方法采用回顾性队列研究设计,从住院病历中收集因道路交通事故致颅脑损伤的病例(1294例)信息,对未死亡病例(1162例)住院时间的分布进行描述,采用KM法和寿命表法对总体住院时间和出院的瞬时速度进行估计,用Cox比例风险模型对多个预测因子的作用大小进行分析,在对Cox模型比例风险假设进行评价的基础上给出了预测平均住院时间的方法。结果住院时间呈正偏态分布,截尾数据比例达61%。平均住院时间的估计值为29天。出院的"快慢"除与病情相关外,还与病人年龄和伤者在事故中的角色密切相关。结论住院时间的分析应该考虑住院时间的分布和截尾现象,采用生存分析的方法比较合理。     

分位数回归在宜昌市带状疱疹就诊费用及其影响因素研究中的应用

目的 结合宜昌市带状疱疹就诊费用及其影响因素的应用实例来介绍分位数回归分析方法.方法 选取2018-2019年宜昌市健康管理大数据中心关于带状疱疹的数据,采用多因素分位数回归,分析不同分位数回归下的偏回归系数.结果 应用实例结果发现,不同分位数下针对带状疱疹就诊费用的影响因素的作用,同时也影响了不同分位数下在控制了其他...     

半参数治愈模型在长期生存者资料分析中的应用

目的 介绍长期生存者资料生存分析模型与方法 .方法 以SARS病人为例阐述半参数治愈模型原理与方法 ,并将长期生存者资料半参数治愈模型与Cox回归模型得到的结果 进行对比分析.结果 Cox比例风险回归模型得到四个协变量有统计学意义;半参数治愈模型比例风险回归部分得到一个有意义的协变量,logistic回归部分得到三个协变量有统计学意义.结论 在对长期生存者存在的资料分析时,半参数治愈模型比传统的Cox比例风险回归模型更具优势,不仅模型形式简明,参数估计解释合理,而且可从多角度提供更多有价值的信息,是一种适用范围更广,实用性更强的统计分析方法 .     

神经网络预后评价在脑胶质瘤患者术后生存质量分析中的应用

目的构建COX比例风险预测模型与人工神经网络预测模型,对脑胶质瘤患者术后生存质量进行评价,为临床医师提供简单、准确的评估方法。方法收集2010年6月至2013年8月山西省肿瘤医院收治的58例脑胶质瘤患者的住院治疗及随访资料的年龄、性别、职业等人口学特征,患者入院时的症状、体征、核磁共振成像(magnetic resonance imaging,MRI)检查、病理诊断分型等,肿瘤切除程度、免疫组化检查及Karnofsky功能状态(Karnofsky performance status,KPS)评分等,筛选有意义因素,建立COX比例风险模型,采用预后指数分层和人工神经网络模型,预测患者术后1年生存质量;并采用ROC分析,对两种方法的预测能力进行评价。结果 COX比例风险模型分析表明,伴有癫痫、术前KPS评分、KI67、病理级别、肿瘤切除程度、血供、肢体活动障碍是影响脑胶质瘤患者术后生存质量的主要影响因素。COX比例风险预测模型的灵敏度为60.0%,特异度为83.3%;人工神经网络预测模型的灵敏度为80.0%,特异度为83.3%。结论人工神经网络模型的预测效果优于COX比例风险模型,人工神经网络可为临床医师评价脑胶质瘤患者术后生存质量提供个体化治疗方法。     

光滑误差项分布的加速失效时间模型及其医学应用

目的介绍光滑误差项分布的加速失效时间模型并探讨其在医学中的应用。方法通过惩罚最大似然的方法对模型中的参数做出估计,以"伪方差估计"的方法做进一步推断,以实例说明模型的实际应用并与Cox模型结果相比较。结果资料不满足Cox模型比例风险假定时,加速失效时间模型的结果优于Cox模型。结论光滑误差项分布的加速失效时间模型不需要知道误差项的基准分布,也不需要满足比例风险假定,是Cox模型很好的替代模型。     

The cost of checking proportional hazards

Confidence intervals (CIs) and the reported predictive ability of statistical models may be misleading if one ignores uncertainty in the model selection procedure. When analyzing time-to-event data using Cox regression, one typically checks the proportional hazards (PH) assumption and subsequently alters the model to address any violations. Such an examination and correction constitute a model selection procedure, and, if not accounted for, could result in misleading CI. With the bootstrap, I study the impact of checking the PH assumption using (1) data to predict AIDS-free survival among HIV-infected patients initiating antiretroviral therapy and (2) simulated data. In the HIV study, due to non-PH, a Cox model was stratified on age quintiles. Interestingly, bootstrap CIs that ignored the PH check (always stratified on age quintiles) were wider than those which accounted for the PH check (on each bootstrap replication PH was tested and corrected through stratification only if violated). Simulations demonstrated that such a phenomenon is not an anomaly, although on average CIs widen when accounting for the PH check. In most simulation scenarios, coverage probabilities adjusting and not adjusting for the PH check were similar. However, when data were generated under a minor PH violation, the 95 per cent bootstrap CI ignoring the PH check had a coverage of 0.77 as opposed to 0.95 for CI accounting for the PH check. The impact of checking the PH assumption is greatest when the p-value of the test for PH is close to the test's chosen Type I error probability.     

370例结直肠癌患者预后影响因素及预后预测分析

目的探讨结直肠癌患者的预后影响因素并建立模型预测预后。方法选择病理诊断的结直肠癌原发病例370例，收集临床病理因素并进行随访。采用Kaplan—Meier法计算生存率，Log—rank检验进行单因素分析，比例风险回归模型（cox模型）进行多因素分析，计算预后指数（PI），cox模型比例风险假定（PH假定）的检验采用对数累积风险函数图法。结果结直肠癌患者1、3、5年生存率分别为90．5％、78．3％和76．5％；淋巴转移、Duckes分期、治疗方式进入结直肠癌预后的cox模型，并满足PH假定，为结直肠癌独立的预后因素；对不同PI值分组，组间生存率存在差异（P〈0．001）；以PI值中位数作为基准，随机选取PI=3．0的个体，该个体相对于基准的预期1、3、5年生存率分别为33．1％、6．8％和3．5％。结论淋巴转移、Duckes分期、治疗方式是影响结直肠癌预后的独立因素；对Cox模型的人选因素有必要进行PH假定的检验；利用预后Cox模型和PI值可有效地预测结直肠癌患者的长期生存状况。     

检验Cox模型成比例危险性假设的探讨

目的：探讨如何对Cox模型成比例危险性假设进行检验,以及协变量与危险函数之间非成比例危险性的解决方法。方法：以Ⅲc期卵巢浆液性囊腺癌数据为例,用图形法对影响Ⅲc期卵巢浆液怀囊腺癌生存时间的预后因素。做了成比例危险性假设的检验。结果：术前一般状态这一 后因素违背了成比例危险性假设。结论：在应用Cox模型时,检验预后因素是否违背成比例危险性假设应当引起重视。     

Parametric models for accelerated and long-term survival: a comment on proportional hazards

The Cox proportional hazards model (CPH) is routinely used in clinical trials, but it may encounter serious difficulties with departures from the proportional hazards assumption, even when the departures are not readily detected by commonly used diagnostics. We consider the Gamel-Boag (GB) model, a log-normal model for accelerated failure in which a proportion of subjects are long-term survivors. When the CPH model is fit to simulated data generated from this model, the results can range from gross overstatement of the effect size, to a situation where increasing follow-up may cause a decline in power. We implement a fitting algorithm for the GB model that permits separate covariate effects on the rapidity of early failure and the fraction of long-term survivors. When effects are detected by both the CPH and GB methods, the attribution of the effect to long-term or short-term survival may change the interpretation of the data. We believe these examples motivate more frequent use of parametric survival models in conjunction with the semi-parametric Cox proportional hazards model.     

Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is relaxed. We describe how this can be carried out in a way that makes the assumptions accessible to all those involved in a research project. Estimation proceeds via multiple imputation, where censored failure times are imputed under user‐specified departures from independent censoring. A novel aspect of our method is the use of bootstrapping to generate proper imputations from the Cox model. We illustrate our approach using data from an HIV‐prevention trial and discuss how it can be readily adapted and applied in other settings. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.     

AN EMPIRICAL COMPARISON OF STATISTICAL TESTS FOR ASSESSING THE PROPORTIONAL HAZARDS ASSUMPTION OF COX'S MODEL

In the analysis of survival data using the Cox proportional hazard (PH) model, it is important to verify that the explanatory variables analysed satisfy the proportional hazard assumption of the model. This paper presents results of a simulation study that compares five test statistics to check the proportional hazard assumption of Cox's model. The test statistics were evaluated under proportional hazards and the following types of departures from the proportional hazard assumption: increasing relative hazards; decreasing relative hazards; crossing hazards; diverging hazards, and non-monotonic hazards. The test statistics compared include those based on partitioning of failure time and those that do not require partitioning of failure time. The simulation results demonstrate that the time-dependent covariate test, the weighted residuals score test and the linear correlation test have equally good power for detection of non-proportionality in the varieties of non-proportional hazards studied. Using illustrative data from the literature, these test statistics performed similarly. © 1997 by John Wiley & Sons, Ltd.     

Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal

Multiple imputation is commonly used to impute missing covariate in Cox semiparametric regression setting. It is to fill each missing data with more plausible values, via a Gibbs sampling procedure, specifying an imputation model for each missing variable. This imputation method is implemented in several softwares that offer imputation models steered by the shape of the variable to be imputed, but all these imputation models make an assumption of linearity on covariates effect. However, this assumption is not often verified in practice as the covariates can have a nonlinear effect. Such a linear assumption can lead to a misleading conclusion because imputation model should be constructed to reflect the true distributional relationship between the missing values and the observed values. To estimate nonlinear effects of continuous time invariant covariates in imputation model, we propose a method based on B‐splines function. To assess the performance of this method, we conducted a simulation study, where we compared the multiple imputation method using Bayesian splines imputation model with multiple imputation using Bayesian linear imputation model in survival analysis setting. We evaluated the proposed method on the motivated data set collected in HIV‐infected patients enrolled in an observational cohort study in Senegal, which contains several incomplete variables. We found that our method performs well to estimate hazard ratio compared with the linear imputation methods, when data are missing completely at random, or missing at random. Copyright © 2013 John Wiley & Sons, Ltd.     

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。