竞争风险型数据的统计处理及应用

目的 分析竞争风险场合下Kaplan-Meier等单结局事件估计法的偏倚及适合竞争风险型数据的累积风险等模型.方法 通过对2组实际数据的分析,比较Kaplan-Meier法与累积风险模型等方法结果的区别,并应用部分分布风险模型进行多因素分析.结果 竞争风险场合采用基于单结局事件的估计法将引起较大偏差,而累积风险模型和部分分布风险模型能很好反映实际情况.结论 竞争风险场合应使用累积风险模型及部分分布风险模型进行分析.     

基于竞争风险模型的老年人轻度认知损害转归研究

目的 将竞争风险模型应用于老年人轻度认知损害(MCI)转归研究,探讨MCI向阿尔茨海默病(AD)转归的影响因素并进行转归预测。方法 利用太原市600例社区老年人2010年10月至2013年5月每6个月随访1次的共6次随访数据,以MCI作为暂态,AD与发生AD前死亡分别作为两个吸收态,发生AD前死亡为AD的竞争风险事件,构建竞争风险模型,获得模型参数,分析MCI向AD转归的影响因素,同时根据多状态模型估计3年转移概率。结果 经过多因素竞争风险模型筛选,高年龄(HR=1.56,95%CI:1.01～2.39)、女性(HR=1.72,95%CI:1.02～2.92)、高文化程度(HR=0.64,95%CI:0.41～1.00)、经常读书看报(HR=0.57,95%CI:0.32～0.99)、有高血压(HR=3.43,95%CI:1.08～10.85)和高SBP(HR=1.67,95%CI:1.04～2.66)是MCI转移为AD的影响因素。MCI 3年后转移为AD的概率为10.7%(95%CI:8.6%～13.2%)。结论 年龄、性别、文化程度、高血压,读书看报和SBP对MCI状态向AD状态的转归过程有影响。竞争风险模型对具有多种潜在结局的纵向资料分析有一定的优势。     

山东省抗病毒治疗HIV/AIDS生存状况及影响因素分析

目的 了解山东省抗病毒治疗HIV/AIDS的生存状况及影响因素。方法 运用Kaplan-Meier（K-M）法及累积发生函数（CIF）估算2003-2015年山东省抗病毒治疗HIV/AIDS的艾滋病相关死亡发生率、部分分布比例风险回归模型（F-G模型）分析生存状况及影响因素。结果 竞争风险存在时,K-M法计算艾滋病相关死亡累积发生率高于CIF。CIF估算5 593例治疗HIV/AIDS随访1、3、5、10年艾滋病相关死亡累积发生率分别为3.08%、4.21%、5.37%和7.59%。大专及以上文化程度（HR=0.40,95% CI：0.24~0.65）HIV/AIDS的艾滋病相关死亡发生危险较低,现住址在鲁西地区（HR=1.33,95% CI：1.01~1.89）、医疗机构检测发现（HR=1.39,95% CI：1.06~1.80）、治疗基线方案含NVP（HR=1.36,95% CI：1.03~1.88）、治疗基线临床症状Ⅲ/Ⅳ期（HR=2.61,95% CI：1.94~3.53）、诊断1年后接受随访（HR=2.02,95% CI：1.30~3.15）、诊断基线CD₄⁺T淋巴细胞计数（CD₄）≤ 200个/μl（HR=3.41,95% CI：2.59~4.59）、治疗基线CD₄ ≤ 350个/μl（HR=5.48,95% CI：2.32~12.72）的HIV/AIDS发生艾滋病相关死亡风险高。结论 竞争风险存在时,K-M法高估艾滋病相关死亡累积发生率,优选竞争风险模型进行生存分析;早诊断、及时随访、早治疗可降低HIV/AIDS艾滋病相关死亡。     

上海市和天津市男男性行为人群药物滥用者HIV新发感染队列研究

目的 通过队列研究分析上海市和天津市MSM药物滥用者HIV新发感染状况及影响因素，为在该人群中开展艾滋病预防干预提供基础。方法 采用队列研究设计，2016年6月至2018年6月采用滚雪球抽样方法招募最近6个月内有药物滥用史的MSM为研究对象，并建立MSM开放性队列，在完成基线调查和HIV检测的基础上开展随访调查和检测。调查内容包括人口学特征、艾滋病相关性行为和药物滥用行为情况等信息。以随访过程中"HIV阳转"为结局因变量，同时，将从研究对象进入队列到出现HIV阳转的时间作为时间因变量，应用Cox比例风险回归模型分析HIV新发感染的影响因素。结果 研究对象共455人，HIV新发感染16例，队列随访累积观察时间为586.08人年，HIV新发感染率为2.73/100人年。多因素Cox回归分析结果显示，相比于≥ 30岁年龄组、最近6个月与男性肛交时坚持使用安全套、无药物混合滥用情况，<25岁年龄组（HR=5.01，95%CI：1.09~23.11）、最近6个月与男性肛交时未坚持使用安全套（HR=1.58，95%CI：1.04~2.41）和药物混合滥用者（HR=1.92，95%CI：1.08~3.40）发生HIV感染风险较高。结论 MSM药物滥用者中，HIV新发感染的危险因素包括年龄<25岁、与男性肛交时未坚持使用安全套和药物混合滥用，应持续加强MSM药物滥用者的HIV预防和干预。     

基于常见遗传变异和传统风险因素的中国南方汉族人群结直肠癌风险预测模型研究

目的 基于结直肠癌全基因组关联研究(GWAS)发现的易感位点,联合传统风险因素建立中国南方汉族人群结直肠癌风险预测模型。方法 对1 066例结直肠癌患者和3 880例健康对照的21个GWAS候选位点进行基因分型,分析其与结直肠癌易感性之间的关联。通过遗传风险评分(GRS)和加权遗传风险评分(wGRS)计算显著候选位点的联合效应。以不同方式组合遗传风险评分和传统风险因素,构建结直肠癌风险预测模型,并绘制受试者工作特征曲线评价模型优劣性。结果 7个候选位点与结直肠癌易感性显著相关。随着风险评分的升高,人群患结直肠癌的风险也随之升高(GRS:P=0.002 6,wGRS:P<0.000 1),相比于四分位分组中最低一组,GRS和wGRS最高的一组OR值分别为1.33(95%CI:1.12~1.58,P=0.001 0)和1.76(95%CI:1.45~2.14,P<0.000 1)。联合传统风险因素和wGRS的模型为最优模型,其曲线下面积为0.593(95%CI:0.573~0.613)。结论 结直肠癌易感位点间存在显著的联合作用。相比于传统风险因素模型,传统风险因素结合加权遗传风险评分模型能更好预测结直肠癌的患病风险。     

多结局Cox模型在医学中的应用和Stata实现

目的探讨生存分析中多结局风险比例模型在实际研究中的应用.方法采用多结局的比例风险模型,采用偏似然估计和校正方差估计值,引入新的协变量,同一因素的不同结局的风险函数比(HR)不同.结果通过对实例的分析显示该方法在实际应用中较好地解决了同一因素对应不同结局风险函数比(HR)不同的问题.结论对于不同结局,定义协变量的相应取值,使多结局比例风险模型可估计和推断同一因素对应不同结局的风险函数比.     

术后入外科重症监护室患者再入ICU的竞争风险分析

目的通过竞争风险模型分析,综合考虑死亡事件和再入ICU(intensive care unit,ICU)事件,了解术后入外科重症监护室(surgical intensive care unit,SICU)患者再入ICU的影响因素。方法以MIMIC-Ⅲ(Medical Information Mart for Intensive Care,MIMIC-Ⅲ)数据库中手术后入外科ICU患者为研究对象。将死亡而未再入ICU作为一个竞争风险事件,使用Grey检验进行患者再入ICU的单因素分析,并计算累积发生率,使用竞争风险模型进行多因素分析。结果本研究共纳入8 401例术后入外科重症监护室患者,其中440例(5.7%)患者发生再入ICU,1 086例(12.9%)发生竞争性死亡。再入ICU在患者入院后30 d、50 d和70 d的累积发生率分别为12.5%、16.7%和21.5%。当死亡而未再入ICU被认为是竞争风险时,竞争风险模型多因素分析结果显示,出ICU前48 h内是否机械通气、血管外科手术、elixhauser合并症、血清氯、血红蛋白和血小板是影响患者再入ICU的因素(P0.05)。结论在评估患者再入ICU的风险时,应同时考虑死亡这一竞争风险事件,以更准确地分析影响患者再入ICU的因素。     

生命历程理论模型在早期成长逆境与精神病理症状关联研究中的应用

早期成长逆境与多种精神病理症状风险有关,但关于逆境特征如何影响随后的精神健康结局尚未明确。生命历程理论模型的发展为揭示早期逆境特征与精神病理学风险的因果关联提供了新的视角。本文通过对既往文献进行综述,分别介绍了生命历程理论模型中的敏感期模型、风险累积模型、风险链模型和近因模型在早期逆境与精神病理症状关联研究中的应用,为早期成长逆境的早期干预提供一定依据。     

老年人不健康生活方式与高血压、糖尿病和血脂异常的关联研究

目的 分析老年人不健康生活方式对高血压、糖尿病和血脂异常患病的影响及累积效应,并探索起关键作用的生活方式。方法 基于2021年云南省行为与疾病监测队列的基线数据,选取年龄≥60岁的16 763名老年人作为研究对象。不健康生活方式包括吸烟、饮酒、不健康饮食、低体力活动、BMI异常和腰围异常,并使用每个研究对象暴露的累积数量来计算不健康生活方式得分。采用多因素logistic回归模型以及混合图模型,分析不健康生活方式与高血压、糖尿病和血脂异常之间的关系。结果 老年人的高血压、糖尿病和血脂异常的患病率分别为57.0%、11.5%和37.0%。研究纳入的6种不健康生活方式大多数表现为高血压、糖尿病和血脂异常的危险因素,患病风险随不健康生活方式数量的累积而上升。与无不健康生活方式者相比,同时具有6种不健康生活方式者,患高血压、糖尿病和血脂异常的OR值分别为3.99（95%CI：1.81~8.80）、4.64（95%CI：1.64~13.15）和4.26（95%CI：2.08~8.73）。混合图模型构建的网络中腰围异常（桥强度=0.81）和高血压（桥强度=0.55）为连接不健康生活方式与高血压、糖尿病和血脂异常的关键“桥接节点”。结论 老年人的不健康生活方式得分越高,高血压、糖尿病和血脂异常患病风险越大,腰围异常是不健康生活方式中的关键因素。     

孕期总增重与不良妊娠结局关系的前瞻性研究

目的 探讨孕期总增重与不良妊娠结局之间的关系。方法 选取成都市妇幼医疗机构产前门诊1 220名6~12孕周、单胎健康的妇女为研究对象进行前瞻性研究。通过问卷调查收集孕妇年龄、孕前体重等基本信息,于分娩前测量孕妇体重,计算孕期总增重,参照2009年美国医学研究所发布的孕期增重推荐标准将研究对象分为增重适宜、增重不足和增重过多组。于分娩后通过医院信息系统收集妊娠结局相关信息。采用多因素非条件logistic回归分析探讨孕期总增重与不良妊娠结局关系。结果 共纳入1 045名单胎活产孕妇进行分析。与孕期增重适宜组相比,孕期增重过多组脐带缠绕和大于胎龄儿发生风险升高（分别为OR=1.641,95% CI：1.197~2.252和OR=1.678,95% CI：1.132~2.488）;孕期增重过少组早产发生风险升高（OR=3.189,95% CI：1.604~6.341）。结论 孕期总增重过多和过少均可能导致不良妊娠结局。应重视孕期体重监测,降低不良妊娠结局发生风险。     

Simulating competing risks data in survival analysis

Competing risks analysis considers time‐to‐first‐event (‘survival time’) and the event type (‘cause’), possibly subject to right‐censoring. The cause‐, i.e. event‐specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause‐specific hazard‐driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time‐dependent cause‐specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem‐cell transplanted patients, where results from cause‐specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a time‐averaged effect on the cumulative event probability scale. Copyright © 2009 John Wiley & Sons, Ltd.     

Practical recommendations for reporting Fine‐Gray model analyses for competing risk data

In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause‐specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event‐free. Subdistribution hazard ratios obtained from the Fine‐Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine‐Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients.     

Flexible parametric modelling of the cause‐specific cumulative incidence function

Competing risks arise with time‐to‐event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause‐specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow‐up time, accounting for the fact that some individuals may have a competing event. When modelling the cause‐specific CIF, the most common model is a semi‐parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause‐specific CIF where the effect of follow‐up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause‐specific CIF with the important advantage that the approach is easily extended to model time‐dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time‐dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause‐specific CIF and those obtained from a semi‐parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time‐dependent effects. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling two cause‐specific hazards of competing risks in one cumulative proportional odds model?

Competing risks extend standard survival analysis to considering time‐to‐first‐event and type‐of‐first‐event, where the event types are called competing risks. The competing risks process is completely described by all cause‐specific hazards, ie, the hazard marked by the event type. Separate Cox models for each cause‐specific hazard are the standard approach to regression modelling, but they come with the interpretational challenge that there are as many regression coefficients as there are competing risks. An alternative approach is to directly model the cumulative event probabilities, but again, there will be as many models as there are competing risks. The aim of this paper is to investigate the usefulness of a third alternative. Proportional odds modelling of all cause‐specific hazards summarizes the effect of one covariate on “opposing” competing outcomes in one regression coefficient. For instance, if the competing outcomes are hospital death and alive discharge from hospital, the modelling assumption is that a covariate affects both outcomes in opposing directions, but the effect size is of the same absolute magnitude. We will investigate the interpretational aspects of the approach analysing a data set on intensive care unit patients using parametric methods.     

A full competing risk analysis of hospital-acquired infections can easily be performed by a case-cohort approach

ObjectivesWe provide a case-cohort approach and show that a full competing risk analysis is feasible even in a reduced data set. Competing events for hospital-acquired infections are death or discharge from the hospital because they preclude the observation of such infections.Study Design and SettingUsing surveillance data of 6,568 patient admissions (full cohort) from two Spanish intensive care units, we propose a case-cohort approach which uses only data from a random sample of the full cohort and all infected patients (the cases). We combine established methodology to study following measures: event-specific as well as subdistribution hazard ratios for all three events (infection, death, and discharge), cumulative hazards as well as incidence functions by risk factor, and also for all three events.ResultsCompared with the values from the full cohort, all measures are well approximated with the case-cohort design. For the event of interest (infection), event-specific and subdistribution hazards can be estimated with the full efficiency of the case-cohort design. So, standard errors are only slightly increased, whereas the precision of estimated hazards of the competing events is inflated according to the size of the subcohort.ConclusionThe case-cohort design provides an appropriate sampling design for studying hospital-acquired infections in a reduced data set. Potential effects of risk factors on the competing events (death and discharge) can be evaluated.     

A review of the use of time-varying covariates in the Fine-Gray subdistribution hazard competing risk regression model

In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time-varying covariates difficult or impossible. We conducted a review of the use of time-varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time-varying covariate. Several inappropriately described a time-varying covariate as having an association with the risk of the outcome.     

Use of bivariate survival curves for analyzing mortality of heart failure and sudden death in dilated cardiomiopathy

This paper discusses the use of bivariate survival curves estimators within the competing risk framework. Competing risks models are used for the analysis of medical data with more than one cause of death. The case of dilated cardiomiopathy is explored. Bivariate survival curves plot the conjoint mortality processes. The different graphic representation of bivariate survival analysis is the major contribute of this methodology to the competing risks analysis.     

Model selection in competing risks regression

In the analysis of time‐to‐event data, the problem of competing risks occurs when an individual may experience one, and only one, of m different types of events. The presence of competing risks complicates the analysis of time‐to‐event data, and standard survival analysis techniques such as Kaplan–Meier estimation, log‐rank test and Cox modeling are not always appropriate and should be applied with caution. Fine and Gray developed a method for regression analysis that models the hazard that corresponds to the cumulative incidence function. This model is becoming widely used by clinical researchers and is now available in all the major software environments. Although model selection methods for Cox proportional hazards models have been developed, few methods exist for competing risks data. We have developed stepwise regression procedures, both forward and backward, based on AIC, BIC, and BICcr (a newly proposed criteria that is a modified BIC for competing risks data subject to right censoring) as selection criteria for the Fine and Gray model. We evaluated the performance of these model selection procedures in a large simulation study and found them to perform well. We also applied our procedures to assess the importance of bone mineral density in predicting the absolute risk of hip fracture in the Women's Health Initiative–Observational Study, where mortality was the competing risk. We have implemented our method as a freely available R package called crrstep. Copyright © 2013 John Wiley & Sons, Ltd.     

Competing risk bias in Kaplan–Meier risk estimates can be corrected

BackgroundKaplan–Meier (KM) analyses are frequently used to measure outcome risk over time. These analyses overestimate risk whenever competing events are present. Many published KM analyses are susceptible to such competing risk bias. This study derived and validated a model that predicted true outcome risk based on the biased KM risk.MethodsWe simulated survival data sets having a broad range of 1-year true outcome and competing event risk. Unbiased true outcome risk estimates were calculated using the cumulative incidence function (CIF). Multiple linear regression was used to determine the independent association of CIF-based true outcome risk with the biased KM risk and the proportion of all outcomes that were competing events.ResultsThe final model found that both the biased KM-based risk and the proportion of all outcomes that were competing events were strongly associated with CIF-based risk. In validation populations that used a variety of distinct survival hazard functions, the model accurately predicted the CIF (R² = 1).ConclusionsTrue outcome risk can be accurately predicted from KM estimates susceptible to competing risk bias.     

Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry

In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.