病例队列研究的设计及分析

在流行病学研究中常常会遇到这样的情况:在一个大样本队列中,随访一段时间后只能得到少量病人,其他大多数对象只能得到截尾(censored)观察结果,这时如果要获得所有对象的协变量资料作统计分析,则需花费大量的资源.为此,Prentice RL在1986年提出了一种新的设计方法--病例队列研究(case-cohort study),该设计仅收集全部研究对象(全队列)中的一个随机样本(子队列,subcohort)和所有发病者(不论是否在子队列内)的协变量资料进行分析,因此极具研究效率.该方法吸取了病例对照研究与队列研究的许多优点,目前被广泛应用于医学研究中,下面对其设计和分析作一简单介绍. 1 病例队列研究的设计原理 病例队列研究是将队列设计和病例对照研究设计相互交叉,融合两者的优点后而形成的一种设计方法,其设计原理为:首先确定某个人群作为所研究的队列(全队列),然后在该队列中用随机的方法抽取一个样本(即子队列 )作为对照组,再收集全队列中所有的欲研究疾病的病例作为病例组,最后用一定的统计方法比较分析两组资料,以探索影响疾病发生、疾病生存时间、预后等的因素( 见图1).     

矽肺对肺癌及总死亡影响的回顾性队列研究

目的 利用香港矽肺患者队列的资料进行分析,探讨矽尘、矽肺与肺癌的关系.方法 选择1981年1月1日至1998年12月31日期间在香港尘肺诊所登记的2789例男性矽肺病例为研究对象,取用同时期一般男性人群作为对照.用人年的方法估计各死因的标化死亡比(SMR),用Axelson's法间接调整吸烟的混杂影响.矽尘与肺癌的剂量-效应关系采用多因素p-spline平滑法模型来拟合最佳风险模型.结果 该组研究队列人数为2789,共观察24 992.6人年,失访率仅为2.9%.该队列主要工种为建筑工人(5 1.09%)和地下沉箱操作工人(37.54%).队列总死亡人数为853人,平均死亡年龄为(63.8±10.27)岁,整个队列中86例死于肺癌.全死因及全癌的SMR均明显上升,首位死因是呼吸道疾病,肺癌的5MR明显增加(SMR:1.69,95%CI:1.35～2.09).去除年龄、时期和吸烟的混杂因素的影响,矽肺对肺癌的相对危险度下降到1.12(95%CI:0.89～1.38).间接调整吸烟的混杂影响后建筑工人及地下沉箱工人肺癌的相对危险度分别为1.09(95%CI:0.82～1.42)和1.56(0.98～2.36).多因素p-spline平滑法风险模型分析显示,肺癌与累积呼吸性矽尘总量或平均矽尘浓度的关系无剂量-效应关系.结论 队列研究未发现接触矽尘或矽肺能增加肺癌死亡的危险,平滑法模型拟合的风险模型并不支持矽尘与肺癌死亡之间存在剂量-效应关系.     

队列研究随访之我见

队列研究提供的病因证据在各种观察性研究设计中具有最高的质量，最重要的原因之一是其先"因"后"果"的设计原理。因此，随访是队列研究的核心要素之一，有效的随访方法是队列研究质量的重要保障。本文简要介绍了队列研究中关于随访期的考虑，随访队列的两种主要方法及其各自的优缺点。     

京津冀自然人群队列研究的理念与实践

队列研究是探索病因和疾病防治知识发现的最有效工具之一。队列研究在我国已有60余年的工作基础,尤其自2016年科技部首次对队列研究专门立项以来,我国队列研究的种类和数量急剧增加。现代队列研究需要在经典的队列研究设计原则基础上,充分应用现代多学科资源和技术方法,以使暴露与结局关联分析和病因推断更加系统和精准。本文介绍了京津冀自然人群队列研究设计的理念、研究进展和挑战,以及在实施中应对的关键问题,可为国内队列研究及其随访机制建设提供参考。     

基于倾向性评分匹配和亚群治疗效果模式图队列研究暴露与结局关联的异质性分析

介绍亚群治疗效果模式图（STEPP）的基本原理、应用场景与主要步骤,梳理将其与倾向性评分匹配（PSM）联合应用在队列研究数据中的理论基础,旨在将STEPP应用到暴露与结局风险关联的异质性分析中。通过模拟案例“不同输液导管类型的肿瘤患者发生静脉血栓栓塞（VTE）风险的队列研究”展示如何将PSM与STEPP在队列研究数据中联合应用,得出亚群中经外周置入中心静脉导管组和中心静脉导管组发生VTE的绝对风险值和相对风险值,以及亚群间的异质性分析结果,并对PSM与STEPP联合应用的局限性和研究前景进行讨论,为实际应用提供参考。     

酒精摄入与女性乳腺癌发病风险的Meta分析

目的 探讨酒精摄入与女性乳腺癌发病风险的关联性。方法 通过Stata 12.0软件对国内外发表的有关酒精摄入与女性乳腺癌发病风险的队列研究和病例对照研究进行Meta分析。结果 共纳入16项病例对照研究和15项队列研究,包括50 519例患者和973 216例对照者。病例对照研究Meta分析提示,酒精暴露可明显增加女性个体发生乳腺癌的风险,合并OR值为1.18,95％置信区间为1.05～1.32;以绝经状态为亚组的Meta分析提示,酒精暴露可增加绝经后女性个体发生乳腺癌的风险,合并OR值为1.26,95％置信区间为1.08～1.46。队列研究Meta分析提示,有酒精暴露者女性乳腺癌的发生率是非饮酒者的1.1倍,合并RR值为1.10,95％置信区间为1.06～1.15;以绝经状态为亚组的Meta分析提示,有酒精暴露的绝经后女性乳腺癌的发生率是非饮酒者的1.1倍,合并RR值为1.14,95％置信区间为1.10～1.19。结论 酒精摄入可显著提高女性尤其绝经后乳腺癌的发病风险。     

中国国家出生队列研究质量控制策略与措施

出生队列是研究孕前和孕期各种环境暴露因素与胎儿、婴幼儿、青少年健康之间关系的有效方法。出生队列建设周期长、环节众多,研究质量可能受到多种因素的影响。本文对中国国家出生队列建设过程中的各项质量保证和质量控制措施进行梳理,归纳总结建设经验。以期为相关队列研究提供经验,减少相关因素对队列研究的影响,提升队列研究质量。中国国家出生队列在质量保证的顶层设计方面采取一系列措施保障研究质量,包括研究中心筛选、成员管理系统开发、标准化操作流程制定及工作人员规范化培训;在质量控制方面,包括针对队列数据产生过程的实时、及时、定时质控,针对生物样本采集、处理及保存的全周期质控,以及针对参与工作人员的培训、督查和量化考核的全面立体质控。     

N-亚硝胺与消化道恶性肿瘤发生风险的关系:基于队列研究的Meta分析

目的 分析N-亚硝胺与消化道恶性肿瘤发生/死亡风险的关系。方法 通过系统检索中国生物医学文献数据库、中文期刊全文数据库、万方电子期刊、PubMed、EBSCO等文献数据库,纳入N-亚硝胺与消化道恶性肿瘤发生/死亡风险队列研究文献,采用RevMan 5.1软件进行Meta分析。结果 共检索到相关文献13篇,纳入其中7篇含有消化道恶性肿瘤的文献进行Meta分析;N-亚硝胺显著增加消化道癌症的发生风险(RR=1.12,95%CI:1.03~1.21);与食管癌发生风险无显著性关联(RR=1.18,95%CI:0.98~1.41),但显著增加食管鳞状细胞癌发生风险(RR=1.72,95%CI:1.01~2.96),而与食管腺癌无显著性相关(RR=0.88,95%CI:0.57~1.37);N-亚硝胺能显著提高胃癌的发生风险(RR=1.08,95%CI:1.00~1.18),但对贲门癌与胃腺癌发生风险的影响无显著性。结论 现有人群队列研究证据显示,N-亚硝胺会显著增加消化道恶性肿瘤的发生风险,但是对不同亚型食管癌和胃癌的影响不同,由于队列研究数量较少且在研究设计、人群选择、暴露测量等方面存在差异,仍需进一步积累相关研究证据。     

大型队列数据资源及其研究方法综述

本文总结了大型队列数据资源的内容与主要研究方法。简要介绍了国际较为著名的8个大型队列数据资源所包含的数据内容、主要功能和获取权限;总结了队列数据应有的处理规范,介绍了现有国际大型队列数据包含的主要数据类型和主要分析方法。分析了中国大型队列数据资源的发展现状,阐述了队列数据在生物医学研究领域的巨大作用以及其未来的发展方向,有助于完善我国自己的多层次精准医学知识库体系,建设安全、稳定、可操作性强的生物医学大数据平台。     

橡胶职业暴露与肺癌关系的前瞻性研究——Ⅱ巢式病例对照和病例队列设计

目的研究橡胶职业暴露、生活方式习惯、家庭环境和经济状况与肺癌之间以及因素相互之间的关系,并对本文应用的两种分析方法进行比较和评价。方法对队列在随访期间死亡的５１例病例及其对照用巢式病例－对照和病例－队列两种方法进行多因素分析。结果在控制非职业性危险因素条件下,亚硝胺暴露的危险度有显著增高（ＲＲ＝２．７１,９５％ＣＩ：１．３２～５．５７）,并发现亚硝胺暴露与肺癌间有明确的剂量－反应关系存在。结论（１）本文数据提示病例－队列分析因标准误较小、抽样更简便而优于巢式病例－对照研究方法;（２）亚硝胺暴露与肺癌之间有较确定的关系,但受到吸烟和经济状况等因素作用的修饰,需进一步研究     

Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death

To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding.     

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.     

Piecewise exponential survival times and analysis of case-cohort data

Case-cohort designs select a random sample of a cohort to be used as control with cases arising from the follow-up of the cohort. Analyses of case-cohort studies with time-varying exposures that use Cox partial likelihood methods can be computer intensive. We propose a piecewise-exponential approach where Poisson regression model parameters are estimated from a pseudolikelihood and the corresponding variances are derived by applying Taylor linearization methods that are used in survey research. The proposed approach is evaluated using Monte Carlo simulations. An illustration is provided using data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study of male smokers in Finland, where a case-cohort study of serum glucose level and pancreatic cancer was analyzed.     

Risk ratio and rate ratio estimation in case-cohort designs: Hypertension and cardiovascular mortality

Multivariate analysis in case-base designs depends on approximate methods. In the present study, new pseudo-likelihood methods are developed for this design. With these methods, the case-cohort risk ratio and rate ratio as well as their standard errors are easily estimated using logistic regression and Poisson regression, respectively. This is illustrated by the association between hypertension and cardiovascular mortality in a cohort, estimated by case-cohort analysis, using samples of several sizes. The estimates are compared with those obtained in full-cohort and nested case-control designs. The results indicate that these methods, which require nothing but widely available computer software, are valid. The case-cohort design, therefore, is a good, sometimes even advantageous alternative to the nested case-control design, in studying a disease that is not very rare. Application of the risk ratio method to the full cohort, using a ‘sample’ of 100 per cent follows logically; whenever the true risk ratio is desired instead of the odds ratio, a multivariate model for its estimation is therefore available.     

Brain tumors and occupational exposures in a cohort of female textile workers in Shanghai, China

OBJECTIVES: This study assessed the associations between brain tumors and specific processes and exposures among female textile workers in Shanghai, China. METHODS: A case-cohort study was conducted that was nested in a cohort of textile workers originally included in a randomized trial of breast self-examination. Incident brain tumor cases (N=114) were identified from 1989 to 1998 from a tumor and death registry operated by the Shanghai Textile Industry Bureau. A subcohort (N=3188), representing an age-stratified random sample of the entire cohort, was selected as a comparison group. Job-exposure matrices were created to assess historical exposures to specific agents, including quantitative assessments for cotton dust and endotoxin exposure. Cox proportional hazards modeling, modified according to a case-cohort design, was used to analyze associations between jobs and exposures and the risk of brain tumors. RESULTS: Employment in maintenance workshops was associated with an increased brain tumor incidence (ever-never exposed hazard ratio 2.36, 95% confidence interval 1.12-4.97), with increasing hazard ratios by duration of employment in maintenance jobs. Handling or processing wool fibers was associated with an increased risk of brain tumors, as was specific exposure to wool fibers; however, estimates did not increase with duration of employment. CONCLUSIONS: These results provide some preliminary suggestion that employment in textile industry maintenance jobs and exposure to wool products may be associated with an increased risk of brain tumors.     

Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes

ObjectiveCox proportional hazards regression models are frequently used to determine the association between exposure and time-to-event outcomes in both randomized controlled trials and in observational cohort studies. The resultant hazard ratio is a relative measure of effect that provides limited clinical information.Study Design and SettingA method is described for deriving absolute reductions in the risk of an event occurring within a given duration of follow-up time from a Cox regression model. The associated number needed to treat can be derived from this quantity. The method involves determining the probability of the outcome occurring within the specified duration of follow-up if each subject in the cohort was treated and if each subject was untreated, based on the covariates in the regression model. These probabilities are then averaged across the study population to determine the average probability of the occurrence of an event within a specific duration of follow-up in the population if all subjects were treated and if all subjects were untreated.ResultsRisk differences and numbers needed to treat.ConclusionsAbsolute measures of treatment effect can be derived in prospective studies when Cox regression is used to adjust for possible imbalance in prognostically important baseline covariates.     

福氏志贺菌O-抗原修饰及血清型转换机制研究进展

该项研究结果说明采用血清型转换噬菌体位点特异性整合系统修饰O-抗原血清型的方法, 可以有效介导外源基因进入细菌染色体的特异性位点, 而不会干扰宿主菌中任何重要基因的表达, 对疫苗的研究具有重要意义。     

样条Cox回归在随访资料分析中的应用

介绍借助R软件应用样条Cox回归分析不满足Cox比例风险模型两个基本假定条件的随访资料的方法,可同时估计非线性效应和时协效应.结果表明文中实例涉及的连续型协变量多不符合线性假定,3个变量不符合比例风险假定,应用样条Cox回归控制多个协变量后,踝臂指数每降低0.1,全因死亡的风险比(HR)为1.071.随访资料在不满足比例风险Cox回归模型的应用条件时,可选择应用样条Cox回归进行分析.     

队列研究资料相加交互作用可信区间的Bootstrap法估计

交互作用评估是流行病学数据分析的重要环节,病因学研究中得到广泛应用的指数模型如logistic回归或Cox比例风险模型,常将危险因素的乘积项纳入模型,其乘积项系数反映了因素间的相乘交互作用,而在公共卫生方面交互作用分析应基于加法模型才更合适.文中根据Rothman提出的评估相加交互作用的指标,通过一个队列研究实例拟合Cox比例风险模型,应用RR值计算两因素的相加交互作用指标,并利用内置Bootstrap功能的S-Plus软件,较为方便地得到Bootstrap法估计的可信区间,避免队列研究资料应用OR值计算导致的估值偏差,且有更高的估计精度.相加和相乘交互作用分析的组合模式相当复杂,当两者冲突时宜选择加法模型.     

Regression coefficient–based scoring system should be used to assign weights to the risk index

ObjectiveSome previously developed risk scores contained a mathematical error in their construction: risk ratios were added to derive weights to construct a summary risk score. This study demonstrates the mathematical error and derived different versions of the Charlson comorbidity score (CCS) using regression coefficient–based and risk ratio–based scoring systems to further demonstrate the effects of incorrect weighting on performance in predicting mortality.Study Design and SettingThis retrospective cohort study included elderly people from the Clinical Practice Research Datalink. Cox proportional hazards regression models were constructed for time to 1-year mortality. Weights were assigned to 17 comorbidities using regression coefficient–based and risk ratio–based scoring systems. Different versions of CCS were compared using Akaike information criteria (AIC), McFadden's adjusted R², and net reclassification improvement (NRI).ResultsRegression coefficient–based models (Beta, Beta10/integer, Beta/Schneeweiss, Beta/Sullivan) had lower AIC and higher R² compared to risk ratio–based models (HR/Charlson, HR/Johnson). Regression coefficient–based CCS reclassified more number of people into the correct strata (NRI range, 9.02–10.04) compared to risk ratio–based CCS (NRI range, 8.14–8.22).ConclusionPreviously developed risk scores contained an error in their construction adding ratios instead of multiplying them. Furthermore, as demonstrated here, adding ratios fail to even work adequately from a practical standpoint. CCS derived using regression coefficients performed slightly better than in fitting the data compared to risk ratio–based scoring systems. Researchers should use a regression coefficient–based scoring system to develop a risk index, which is theoretically correct.