基于人时的相对危险度分布的Monte-Carlo模拟估计

目的应用Monte-Carlo模拟进行基于人时的相对危险度的分布估计。方法结合实例进行相对危险度的模型构建、拉丁超立方抽样和概率分布的拟合及RR可信区间的几种计算方法比较。结果模拟的RR频率分布经拟合符合Pearson5、Lognorm、Gamma和InvGauss4种分布,以Pearson5分布拟合最佳。模拟的RR值95%可信区间结果与统计量函数计算值、Wald法和Score法大致相当,但其上限值和下限值均略小。结论应用Monte-Carlo模拟结合拉丁超立方抽样技术,实现了基于人时的相对危险度的分布估计,该方法可应用于更为复杂的参数分布估计。     

基于队列研究估计暴露因素与罕见结局关联的统计方法选择

比值比(OR)和相对危险度(RR)均是评估暴露因素与研究结局间关联的常用指标, 在罕见结局的队列中, OR值常被用作RR值的近似估计, 但RR值的意义更加清晰易解释。本研究旨在基于罕见结局队列研究, 比较不同多因素回归模型获得RR与OR估计值的差别, 为基于队列研究估计暴露因素与罕见结局间关联关系时选择多因素回归方法, 以及优先报告关联大小估计指标提供参考。本研究基于中国出生队列数据开展实例研究, 以全部病种的出生缺陷为研究结局, 以受孕方式为暴露因素, 纳入孕妇年龄、是否有出生缺陷家族史等有明确证据支持的变量作为协变量, 分别拟合logistic回归、log-binomial回归以及Poisson回归, 并比较OR和RR的点估计值及其95%CI。结果表明, 在罕见结局队列研究中logistic回归估计的OR值与log-binomial回归及Poisson回归估计的RR值近似, 但log-binomial回归及Poisson回归估计的效应值更接近1.00, 且效应值的95%CI分布更窄, 但可能存在不收敛或过离散问题。针对罕见结局的队列研究, 在适用前提下, 推荐优先报告基于log-b...     

不同混杂结构条件下各倾向性评分方法的模拟比较研究

目的通过构建不同混杂结构的处理因素模型和结局模型、不同相关性的协变量,比较多种倾向性评分方法在结局模型为线性回归模型的情况下估计处理效应的优劣。方法采用Monte Carlo模拟方法,通过构建四种由简单到复杂的不同结构的混杂模型,生成相应的数据集,再分别应用倾向性评分匹配、回归调整、加权以及分层的方法估计处理效应并进行比较。评价指标包括点估计、标准误、相对偏倚、均方误差。结果在结局模型为线性回归模型情况下,倾向性评分回归调整法估计的相对偏倚最小,稳定性也最好。匹配法卡钳值取0.02较卡钳值取倾向性评分标准差的0.2倍估计的相对偏倚更小。当处理因素模型中含有非线性效应时,用逆概率加权法估计的偏倚较大,并且加权法估计的标准误也最大。倾向性评分分层法在各种情况下估计的相对偏倚都较大。结论倾向性评分回归调整法能够较好地估计处理效应,并且在各种情况下估计都较为稳健。建议当协变量与处理因素和结局变量的关系无法确定时,这四种方法中可以考虑优先使用回归调整法。     

奶制品摄入与代谢综合征发病风险的Meta分析

目的 研究奶制品摄入与代谢综合征(metabolic syndrome,MetS)发病风险的关系.方法 在Pubmed 中以“dairy or milk”和“metabolic syndrome or insulin resistance syndrome”进行检索,收集研究奶制品与MetS发病风险的前瞻性队列研究,抽取每个研究中奶制品最高与最低摄入量相比较的相对危险度(relative risk,RR)及95％可信区间(confidence interval,CI),利用meta分析计算合并RR值和95％ CI,并进行分层分析、敏感性分析和剂量-反应关系分析.结果 在纳入分析的6篇前瞻性队列研究中,与最低摄入水平相比,最高摄入水平的合并RR为0.79 (0.70～0.90).按照地区、诊断标准、随访时间分层后,RR值无明显变动.剂量-反应关系显示每增加一“份”奶制品摄入可以减少3％的MetS发病风险.结论 奶制品的摄入可以降低MetS发病风险.     

低危险度评价及提高流行病学实践质量的几个问题

对低危险度的评价已经受到愈来愈多的注意，而且它对提高流行病学实践的质量具有重要的意义。本文就这两个问题进行简要的综述。一、低危险度评价：1.联系的强度及RR（OR）的意义：流行病学中应用“危险因素”一词指的是“与疾病的发生有联系，但又不一定是充分病因的因子”[1]。通常用相对危险度（RR）或比数比（OR）及其95％可信区间（95％CI）与P值以评价此危险因素与疾病的联系强度。当95％CI不包括1或P＜0.05时认为此联系有意义，此外被认为没有意义。通常表示危险因素及疾病联系强度的标准是RR（或OR）0.9一1.1认为无联系，O.…     

体重指数、腰围与代谢性健康风险的关系

目的比较体重指数（BMI）、腰围（WC）与代谢性健康风险的大小。方法对苏州市和常熟市两个社区的江苏省多代谢异常和代谢综合征（MS）防治研究中，经济发达地区基线资料的1604例对象按BMI和WC分组，在正常体重（BMI：18～23．9）、超重（BMI：24～27．9）、肥胖（BMI≥28）类别中，计算高血压、高血糖、血脂异常的相对危险度（RR），并对WC类别中腹型肥胖（男≥85cm，女≥80cm）和WC正常个体进行比较。结果无论是男性还是女性，其高血压、糖脂代谢各项指标以及MS的罹患率均与BMI和WC相关，控制混杂因素后，这样的相关性依然存在；但仅BMI超重时，各项表示代谢性健康风险的aRR值基本上都低于BMI超重合并腹型肥胖的aRR值；男、女性BMI正常但有腹型肥胖时，均较BMI超重而WC正常者的健康风险高。结论WC在表示代谢性健康风险方面比BMI更为有效。     

睡眠时间能否预测心血管疾病风险相关前瞻性研究的系统综述与Meta分析

为探讨睡眠时间与冠心病、卒中和心血管疾病发病风险间的关系,英国学者对文献做了Meta分析.研究的入选标准为:随访时间＞3年的成年人前瞻性研究;基线时调查了睡眠时间;结局事件包括冠心病、卒中或心血管疾病.最后共纳入了15项研究(24个队列)的474 684名研究对象(随访时间6.9～25.0年).使用随机效应模型合并了入选研究的相对危险度(RR)及其95％置信区间(CI),作者对效应结果进行了评估.     

流行病学研究中易感性指标的计算

在流行病学研究中,相对危险度(RR)和归因危险度(AR)指标已经应用得很普遍。但是,这些指标并不能用来估计易感于危险因素的人群比例。“易感性”是指足以使某一个体在暴露因素作用之后发生某种疾病的基础因素。本文推荐一种应用RR、人群发病率(I_(?))和人群暴露率(P_(?))来估计人群易感者比例的简单方法,并对它们之间的关系进行讨论。1 计算公式假设在一个无交互作用且具备充分病因的简单模型中,暴露人群某病发生率为I_(?),非暴露人群某病发生率为I_(?),则RR=I_(?)/I_(?)。用S 表示易感者比例。为导出S 与RR、I_(?)、P_(?)的关系式。假定以下两个基本事件A 和B;A 事件是某个体为某暴露因素的易感者,B 事件是某个体具备不包括暴露因素在内的一个充分病因.根据概率的性质,得出事件A 和事件B 的概     

抑郁症患者自杀的相关社会、心理影响因素调查研究

目的：研究抑郁症自杀危险行为的心理社会因素,为预防提供依据。方法：按CCMD-3诊断标准收集120例住院的抑郁症患者,使用社会支持量表,生活事件量表,中文版家庭亲密度与适应性量表第2版,Hamiltion抑郁量表进社会心理评估,采用多因素Logistic回归分析与抑郁症自杀行为有关的心理社会因素。结果：抑郁症自杀未遂60例,自杀与负性生活事件相对危险度（RR=3．943）、主观支持（RR=4．276）、对支持的利用度（RR=2．187）、家庭亲密度（RR=1．493）呈正相关（P均〈0．05）。结论：负性生活事件、主观支持分、对支持的利用度、家庭亲密度是抑郁症患者自杀的危险因素。     

身体测量指标与女性乳腺癌关系的前瞻性队列研究

目的研究上海女性身体测量指标与绝经前后乳腺癌之间的关系.方法采用前瞻性队列研究方法.1997-2000年在上海市区建立一个73 461人年龄40～70岁的女性队列.每2年随访一次,至2004年6月共收集乳腺癌新发病例432例.用Cox回归模型估计身体测量指标与女性乳腺癌发生的相对危险度（RR）和95%可信区间（CI）.结果调整年龄、文化程度、能量摄入、月经、生育等混杂因素后,基线调查时体重、体重指数（BMI）、腰臀围比例（WHR）和20岁后体重增加与绝经后女性乳腺癌危险性呈正相关,与绝经前女性乳腺癌发生无关.身高与乳腺癌危险的显著正相关关系仅在绝经前女性中发现,20岁时身高在161 cm以上者发生乳腺癌的危险是157.1 cm以下者的1.84倍（95%CI：1.30～2.61）.20岁时BMI处于平均水平者绝经前患乳腺癌的危险性显著高于其他两组.BMI和WHR互相调整后,WHR独立于BMI的作用接近显著性水平.调整BMI后,20岁后体重增加会显著增加绝经后乳腺癌危险（RR=1.61,95%CI：1.09～2.37）.结论对于绝经后女性,成年后体重增加和中心性肥胖都是预测乳腺癌危险的指标.因此控制成年后体重、减少腹部脂肪堆积是预防绝经后乳腺癌发生的有效措施.身高可能是影响绝经前女性乳腺癌发生的危险因素.     

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.     

Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression

ObjectiveTo assess alternative statistical methods for estimating relative risks and their confidence intervals from multivariable binary regression when outcomes are common.Study Design and SettingWe performed simulations on two hypothetical groups of patients in a single-center study, either randomized or cohort, and reanalyzed a published observational study. Outcomes of interest were the bias of relative risk estimates, coverage of 95% confidence intervals, and the Akaike information criterion.ResultsAccording to simulations, a commonly used method of computing confidence intervals for relative risk substantially overstates statistical significance in typical applications when outcomes are common. Generalized linear models other than logistic regression sometimes failed to converge, or produced estimated risks that exceeded 1.0. Conditional or marginal standardization using logistic regression and bootstrap resampling estimated risks within the [0,1] bounds and relative risks with appropriate confidence intervals.ConclusionEspecially when outcomes are common, relative risks and confidence intervals are easily computed indirectly from multivariable logistic regression. Log-linear regression models, by contrast, are problematic when outcomes are common.     

A comparison of methods for the construction of confidence interval for relative risk in stratified matched‐pair designs

A stratified matched‐pair study is often designed for adjusting a confounding effect or effect of different trails/centers/ groups in modern medical studies. The relative risk is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this paper, we propose seven confidence interval estimators for the common relative risk and three simultaneous confidence interval estimators for the relative risks in stratified matched‐pair designs. The performance of the proposed methods is evaluated with respect to their type I error rates, powers, coverage probabilities, and expected widths. Our empirical results show that the percentile bootstrap confidence interval and bootstrap‐resampling‐based Bonferroni simultaneous confidence interval behave satisfactorily for small to large sample sizes in the sense that (i) their empirical coverage probabilities can be well controlled around the pre‐specified nominal confidence level with reasonably shorter confidence widths; and (ii) the empirical type I error rates of their associated test statistics are generally closer to the pre‐specified nominal level with larger powers. They are hence recommended. Two real examples from clinical laboratory studies are used to illustrate the proposed methodologies. Copyright © 2009 John Wiley & Sons, Ltd.     

An estimating equation approach for the analysis of case-control studies with exposure measured at several levels

Liang gave an extension of the Mantel-Haenszel estimating procedure for a common odds ratio to logistic regression models. It is applicable to case-control studies with multiple exposure levels, which yield K 2 x J tables. This paper provides variance and covariance estimators, which are consistent in both sparse-data and large-strata, for Liang's estimating functions in the K 2 x J tables case, and proposes an approximate confidence interval method for the common odds ratios.     

A prospective study of folate intake and the risk of pancreatic cancer in men and women

Laboratory and human studies suggest that folate intake may influence the risk of some cancers. However, prospective information about the relation between folate intake and the risk of exocrine pancreatic cancer is limited. The authors examined the relation of dietary folate intake to the risk of pancreatic cancer in two large prospective US cohorts. Folate intake was assessed by food frequency questionnaire in 1984 in women and in 1986 in men. Multivariate relative risks were adjusted for age, energy intake, cigarette smoking, body mass index, diabetes, and height. During 14 years' follow-up in each cohort, 326 incident cases of pancreatic cancer were identified. Compared with participants in the lowest category of folate intake, participants in increasing 100- micro g categories of total energy-adjusted folate intake had pooled multivariate relative risks for pancreatic cancer of 1.08, 1.10, and 1.03 (95% confidence interval: 0.74, 1.43; p(trend) = 0.99). For energy-adjusted folate from food, the pooled relative risks for increasing 100- micro g categories of intake were 0.81, 0.89, and 0.66 (95% confidence interval: 0.42, 1.03; p(trend) = 0.12). There was no statistical interaction between folate intake and methionine, alcohol, fat, or caffeine. The results from these two large prospective cohorts do not support a strong association between energy-adjusted folate intake and the risk of pancreatic cancer.     

Estimating the relative risk in cohort studies and clinical trials of common outcomes

Logistic regression yields an adjusted odds ratio that approximates the adjusted relative risk when disease incidence is rare (<10%), while adjusting for potential confounders. For more common outcomes, the odds ratio always overstates the relative risk, sometimes dramatically. The purpose of this paper is to discuss the incorrect application of a proposed method to estimate an adjusted relative risk from an adjusted odds ratio, which has quickly gained popularity in medical and public health research, and to describe alternative statistical methods for estimating an adjusted relative risk when the outcome is common. Hypothetical data are used to illustrate statistical methods with readily accessible computer software.     

Case-sibling gene-association studies for diseases with variable age at onset

Studies which compare cases to disease-free siblings are useful for assessing association between a genetic locus and a phenotypic trait, as they eliminate the possibility of confounding by population stratification. Many analytic methods for such family-based studies are based on a binary disease model. However, complex diseases have variable age at onset. Consequently, binary-outcome methods can be inefficient or biased. We review methods for analysing censored age-at-onset data from family studies, including stratified Cox regression and genotype-decomposition regression, an unstratified procedure which regresses age-at-onset on between- and within-family genotype components. We also introduce a retrospective likelihood for censored age-at-onset data, which requires an external estimate of the baseline hazard. Stratified Cox regression does not use controls who have not attained the age of their case sibling(s), potentially leading to a loss of efficiency. Both genotype-decomposition regression and the retrospective likelihood use these younger controls. We assess the performance of these methods via simulation studies. Stratified Cox regression and the retrospective likelihood have appropriate type I error rates in almost all situations studied; genotype-decomposition regression is often anti-conservative. Away from the null, confidence intervals for the relative risk derived from stratified Cox regression are anti-conservative when the disease is rare and case-rich families are sampled. The retrospective likelihood is more efficient than stratified Cox regression and its confidence intervals have correct coverage when the disease is rare or the estimate of the baseline hazard is reasonably accurate. These results suggest that when estimating genotype relative risks is the principal analytic goal, stratified Cox regression is appropriate as long as the disease is common; when the disease is rare, the retrospective likelihood may be more appropriate.     

Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies

Some recent articles have discussed biased methods for estimating risk ratios from adjusted odds ratios when the outcome is common, and the problem of setting confidence limits for risk ratios. These articles have overlooked the extensive literature on valid estimation of risks, risk ratios, and risk differences from logistic and other models, including methods that remain valid when the outcome is common, and methods for risk and rate estimation from case-control studies. The present article describes how most of these methods can be subsumed under a general formulation that also encompasses traditional standardization methods and methods for projecting the impact of partially successful interventions. Approximate variance formulas for the resulting estimates allow interval estimation; these intervals can be closely approximated by rapid simulation procedures that require only standard software functions.     

Relative risk of mortality in the physically inactive is underestimated because of real changes in exposure level during follow-up

Relative risk among exposure groups in prospective cohort studies is based on the assumption that all subjects are exposed at the level recorded at baseline throughout the study. Changes in risk behavior during follow-up will dilute the relative risk. This prospective cohort study in Copenhagen, Denmark, between 1964 and 1994 included 30,640 men and women; 19,149 were examined twice, with an interval of 6.7 (standard deviation, 3.4) years. Relative risks calculated from baseline measurements for moderately active and sedentary groups compared with the highly active group were 1.11 (95% confidence interval: 1.05, 1.18) and 1.64 (95% confidence interval: 1.53, 1.75), respectively. The relative risk between the highly active group and the sedentary group decreased with increasing follow-up time. When intraindividual changes in physical activity level during follow-up were taken into account, the relative risk of physical inactivity was 24-59% higher compared with the relative risk estimated from baseline measurements. The risk of a sedentary lifestyle is underestimated when it is calculated from one baseline measurement in prospective studies, because subjects change behavior during follow-up.     

Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error

If several risk factors for disease are considered in the same multiple logistic regression model, and some of these risk factors are measured with error, the point and interval estimates of relative risk corresponding to any of these factors may be biased either toward or away from the null value. A method is provided for correcting point and interval estimates of relative risk obtained from logistic regression for measurement error in one or more continuous variables. The method requires a separate validation study to estimate the coefficients from the multivariate linear regression model relating the surrogate variables to the vector of true risk factors. Similar methods have been suggested by other authors, but none provides a means of correcting the confidence intervals which include a component of variability due to estimation of the measurement error parameters from a validation study. An example is provided from a prospective study of dietary fat, calories, and alcohol in relation to breast cancer, and from a validation study of the questionnaire used to assess these nutrients. Before correcting for measurement error, the age-adjusted relative risk for a 25 g increment in alcohol intake was 1.33 (95% confidence interval (CI) 1.14-1.55); after correcting for measurement error, the relative risk increased to 1.62 (95% CI 1.23-2.12). Similarly, for a 10 g increment in saturated fat intake, the age-adjusted relative risk was 0.94 (95% CI 0.83-1.06); after correcting for measurement error, the relative risk was 0.84 (95% CI 0.59-1.20). These results indicate that the failure to find a substantial positive association between breast cancer risk and saturated fat intake cannot be explained by measurement error in fat, calories, or alcohol.