存在时依性混杂时的G方法

  目的  介绍处理时依性混杂的G方法,并对不同G方法进行探讨和比较。  方法  通过4个情境的模拟试验验证不同G方法在不同情境下对时依性混杂的处理效果,并应用英国生物样本库(UK Biobank)的数据集进行实例分析。  结果  模拟试验和实例分析结果均显示G方法能有效处理时依性混杂。模拟试验显示3种方法效果类似,G-computation易受G-null paradox的影响。随着时依性混杂因素数量增加,相比于G-computation和G-estimation,逆概率加权法(inverse probability of treatment weighting, IPTW)的效果波动较大。  结论  不同G方法都能适当地处理时依性混杂,降低统计分析过程中的偏倚大小。     

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。     

观察性研究中基于贝叶斯加性回归树的平均处理效应估计

目的 探讨观察性研究中贝叶斯加性回归树估计平均处理效应的统计学性能及适用条件。方法 通过模拟试验和实例分析比较贝叶斯加性回归树与多变量回归、倾向性得分匹配、逆概率加权的估计结果差异。结果 模拟试验表明,在线性假设下,贝叶斯加性回归树的估计表现与常用方法接近。当数据中变量之间关系复杂,存在非线性关系时,贝叶斯加性回归树的估计结果明显优于其他方法。当不满足可忽略性假设时,未观测到的混杂因素导致四种估计方法的结果都会存在偏倚,但是贝叶斯加性回归树的估计偏倚明显小于其他三种方法,结果相对稳健。并在实例中使用该方法估计了戒烟对于体重变化的平均处理效应。结论 在绝大多数观察性研究中,研究结局受到多个因素的影响,研究者很难正确指定变量间的关系。从模型拟合和结果准确性来说,贝叶斯加性回归树是值得推荐的方法。     

不同绝经状态下体质量指数对高尿酸与代谢综合征关系中介效应分析

目的 研究不同绝经状态下高尿酸与代谢综合征（metabolic syndrome,MS）患病风险的关联性,并探讨体质量指数（bodymassindex, BMI）在不同绝经状态下对高尿酸与MS关系的中介效应。方法 采用多阶段、分层整群抽样方法,抽取苗族女性作为研究对象。采用logistic回归模型分析高尿酸与MS的关联,计算MS患病风险。利用中介效应模型分析BMI在高尿酸与MS关系中的中介效应值。结果符合条件的研究对象共3 194人,绝经人群高尿酸和MS的检出率分别为58.26%、62.24%,大于未绝经人群高尿酸和MS的检出率（41.74%和37.76%）。多因素logistic回归显示,在未绝经及绝经人群中,高尿酸均与较高的MS风险相关,且绝经后的高尿酸与MS关联风险[OR (95%CI):2.58 (2.01～3.32)]强于绝经前[OR (95%CI):2.33 (1.72～3.14)]。中介效应分析显示,BMI介导高尿酸与MS的关联,中介效应值及其95%CI在未绝经人群、绝经人群中分别为0.03 (0.02～0.04)、0.05 (0.03～0.07),中介效应占比分别为18...     

疾病预后研究的中介分析方法评价

目的对用于癌症预后研究的五种中介分析方法(VanderWeele法、Baron-Kenny法、Imai法、Sobel法和InverseWeight法)进行评价,为实例分析的方法选择提供依据。方法基于模拟试验,产生不同参数设置下的模拟数据,并评价五种方法的第一类错误、检验效能和分析时间。结果除InverseWeight法在相关系数较大时第一类错误有所膨胀外,其余四种方法的第一类错误在不同参数情况下均在0.05附近。五种方法的检验效能趋势一致,均随着样本量、中介比、总效应的增大而增大,随着删失比的增大而减小。在样本量较小(N=100)且中介比不大于30%的情况下,InverseWeight法的检验效能低于另四种方法。InverseWeight法、Baron-Kenny法和Imai法的分析效率远低于VanderWeele法和Sobel法。结论综合考虑一类错误控制、检验效能及分析效率,推荐VanderWeele法进行预后研究的中介分析。     

获取信息能力在江西省居民传染病防治素养和自我防护行为间的中介效应

目的 了解江西省居民传染病防治素养及其影响因素,并分析获取新冠肺炎疫情相关信息能力在传染病防治素养和自我防护行为间的中介效应,为提升居民自我防护能力提供参考。方法 采用分层多阶段随机抽样方法,对江西省12个地区2 882位居民进行调查,采用《全国居民健康素养监测问卷》和新冠肺炎认知水平与防护行为调查问卷进行问卷调查; 运用描述性分析、t/F检验和多重线性回归分析居民传染病防治素养水平及其影响因素,采用Goodman-Kruskal Gamma法对传染病防治素养与自我防护行为进行关联性检验,中介效应采用Process 3.4软件进行检验。结果 2021年江西省居民传染病防治素养水平为19.40%,传染病防治素养平均得分为3.80±1.82; 多重线性回归结果显示年龄、文化程度和获取信息能力为江西省居民传染病防治素养的影响因素(P＜0.05); 传染病防治素养与自我防护行为之间呈相关关系,关联系数G=0.414(P＜0.05); 传染病防治素养对自我防护行为的总效应为0.5512,通过获取信息能力的中介效应为0.1472,相对中介效应为26.71%。结论 江西省居民传染病防治素养水平尚需提升,获取信息能力在传染病防治素养与自我防护行为之间起到了部分中介作用,政府及卫生健康部门在提高居民个人防护行为时应重视其获取信息的能力。     

拒烟自我效能在吸烟危害认知对戒烟成功影响的中介效应分析

目的 分析拒烟自我效能在吸烟危害认知对戒烟成功影响的中介效应。方法 采用以社区人群为基础的病例对照研究设计,以642例男性自发性戒烟成功者为病例组,700例男性自发性戒烟失败者为对照组。吸烟危害认知水平由12个评分项目的总分评估,拒烟自我效能由拒烟自我效能问卷评估。总效应被分解为直接效应和间接效应,通过基于KHB法的logistic回归分析探讨各效应。结果 调整潜在混杂因素(包括年龄、开始吸烟年龄、职业、教育水平、婚姻状况)后,拒烟自我效能的中介效应仅占吸烟危害认知对戒烟成功影响总效应的6.03%,吸烟危害认知对戒烟成功影响的直接效应占总效应的93.97%;在3种拒烟自我效能情境(包括积极、消极、习惯情境)下的中介效应占吸烟危害认知对戒烟成功总效应的比例分别为6.80%、3.08%和2.32%。结论 拒烟自我效能在吸烟危害认知对戒烟成功的影响具有部分中介效应。提高吸烟者的危害认知水平可直接促使其戒烟成功,并可通过增大拒烟自我效能促使其成功戒烟。     

城镇化对农村居民医疗保健消费的传导机制研究

目的:研究城镇化对农村居民医疗保健消费的影响及传导机制。方法:基于2006—2017年我国30个省份(不包括西藏地区)的面板数据,运用中介效应模型实证分析城镇化与农村居民医疗保健消费之间的关系以及传导机制。结果:城镇化有利于我国农村居民医疗保健消费增加;收入效应、老龄化效应和示范效应均在城镇化作用于农村居民医疗保健消费的过程中表现为正向部分中介效应;在并行中介效应中,收入效应和老龄化效应的中介作用均显著,而示范效应不显著;城镇化通过影响收入效应,进而作用于老龄化效应,最终对农村居民医疗保健消费产生积极影响的传导路径是存在的。结论:城镇化在直接促进农村医疗保健消费增加的同时,还能通过收入效应、老龄化效应和示范效应间接对农村居民医疗保健消费产生积极作用。因此,政府应大力推进城镇化进程,释放城镇化红利,以满足农村居民日益增长的医疗保健消费需求。     

因果中介效应分析及SAS实现

目的 通过模拟数据，研究父母提供的鼓励环境是否会对儿童的认知发展产生影响，探索父母鼓励是否增强了孩子的学习动机，介绍因果中介效应分析方法的原理及SAS实现。方法 在未控制混杂因素和控制混杂因素两种情况下，运用因果中介效应分析方法对鼓励和认知得分之间的因果路径进行分解，确定中介变量动机在因果关系中的作用程度。结果 学习动机在父母鼓励与儿童认知发展之间起中介作用，中介效应占总效应的比例为47%(不控制混杂因素)、37%(控制混杂因素)。结论 学习动机是中介变量，父母鼓励可以通过增强孩子的学习动机来提高儿童的认知发展。在满足相关前提和假设下，CAUSALMED过程可以实现因果中介效应分析，探索因果关系的内在影响机制。     

生存结局的多中介变量的中介分析方法比较

目的本研究以生存结局为切入点,探讨含两个中介变量时的中介生存分析模型(Aalen相加风险模型、Cox比例风险模型、加速失效时间AFT模型),为预后的多中介变量分析方法的选择提供应用建议。方法通过统计模拟试验,设定不同的相关系数、效应比、删失率等,从第一类错误及检验效能等方面对上述三种方法进行统计学性质评价。结果中介变量与暴露的相关系数越大,越容易发现中介变量的中介效应;删失率与效应比对Aalen模型的影响较大,对其他两种模型的影响较小;随着删失率的降低,Aalen模型的第一类错误反而膨胀,故Aalen模型不适用于多中介变量的分析;样本量越大,三种模型的检验效能差别减小且趋于稳定。不同参数设定下,AFT模型的检验效能最大,其次为Cox模型,最后为Aalen模型。结论 AFT模型优于其他两种方法,推荐用于生存结局的多中介变量的中介分析;进行中介分析时需要足够的样本量。     

Doubly robust generalized estimating equations for longitudinal data

A popular method for analysing repeated‐measures data is generalized estimating equations (GEE). When response data are missing at random (MAR), two modifications of GEE use inverse‐probability weighting and imputation. The weighted GEE (WGEE) method involves weighting observations by their inverse probability of being observed, according to some assumed missingness model. Imputation methods involve filling in missing observations with values predicted by an assumed imputation model. WGEE are consistent when the data are MAR and the dropout model is correctly specified. Imputation methods are consistent when the data are MAR and the imputation model is correctly specified. Recently, doubly robust (DR) methods have been developed. These involve both a model for probability of missingness and an imputation model for the expectation of each missing observation, and are consistent when either is correct. We describe DR GEE, and illustrate their use on simulated data. We also analyse the INITIO randomized clinical trial of HIV therapy allowing for MAR dropout. Copyright © 2009 John Wiley & Sons, Ltd.     

Missing data in a multi-item instrument were best handled by multiple imputation at the item score level

ObjectivesRegardless of the proportion of missing values, complete-case analysis is most frequently applied, although advanced techniques such as multiple imputation (MI) are available. The objective of this study was to explore the performance of simple and more advanced methods for handling missing data in cases when some, many, or all item scores are missing in a multi-item instrument.Study Design and SettingReal-life missing data situations were simulated in a multi-item variable used as a covariate in a linear regression model. Various missing data mechanisms were simulated with an increasing percentage of missing data. Subsequently, several techniques to handle missing data were applied to decide on the most optimal technique for each scenario. Fitted regression coefficients were compared using the bias and coverage as performance parameters.ResultsMean imputation caused biased estimates in every missing data scenario when data are missing for more than 10% of the subjects. Furthermore, when a large percentage of subjects had missing items (>25%), MI methods applied to the items outperformed methods applied to the total score.ConclusionWe recommend applying MI to the item scores to get the most accurate regression model estimates. Moreover, we advise not to use any form of mean imputation to handle missing data.     

Missing covariate data in medical research: To impute is better than to ignore

ObjectiveWe compared popular methods to handle missing data with multiple imputation (a more sophisticated method that preserves data).Study Design and SettingWe used data of 804 patients with a suspicion of deep venous thrombosis (DVT). We studied three covariates to predict the presence of DVT: d-dimer level, difference in calf circumference, and history of leg trauma. We introduced missing values (missing at random) ranging from 10% to 90%. The risk of DVT was modeled with logistic regression for the three methods, that is, complete case analysis, exclusion of d-dimer level from the model, and multiple imputation.ResultsMultiple imputation showed less bias in the regression coefficients of the three variables and more accurate coverage of the corresponding 90% confidence intervals than complete case analysis and dropping d-dimer level from the analysis. Multiple imputation showed unbiased estimates of the area under the receiver operating characteristic curve (0.88) compared with complete case analysis (0.77) and when the variable with missing values was dropped (0.65).ConclusionAs this study shows that simple methods to deal with missing data can lead to seriously misleading results, we advise to consider multiple imputation. The purpose of multiple imputation is not to create data, but to prevent the exclusion of observed data.     

Meta‐analysis with missing study‐level sample variance data

We consider a study‐level meta‐analysis with a normally distributed outcome variable and possibly unequal study‐level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing‐completely‐at‐random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta‐regression to impute the missing sample variances. Our method takes advantage of study‐level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross‐over studies. Copyright © 2016 John Wiley & Sons, Ltd.     

Methods to Account for Attrition in Longitudinal Data: Do They Work? A Simulation Study

Attrition threatens the internal validity of cohort studies. Epidemiologists use various imputation and weighting methods to limit bias due to attrition. However, the ability of these methods to correct for attrition bias has not been tested. We simulated a cohort of 300 subjects using 500 computer replications to determine whether regression imputation, individual weighting, or multiple imputation is useful to reduce attrition bias. We compared these results to a complete subject analysis. Our logistic regression model included a binary exposure and two confounders. We generated 10, 25, and 40% attrition through three missing data mechanisms: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR), and used four covariance matrices to vary attrition. We compared true and estimated mean odds ratios (ORs), standard deviations (SDs), and coverage. With data MCAR and MAR for all attrition rates, the complete subject analysis produced results at least as valid as those from the imputation and weighting methods. With data MNAR, no method provided unbiased estimates of the OR at attrition rates of 25 or 40%. When observations are not MAR or MCAR, imputation and weighting methods may not effectively reduce attrition bias.     

Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation

We consider the situation of estimating the marginal survival distribution from censored data subject to dependent censoring using auxiliary variables. We had previously developed a nonparametric multiple imputation approach. The method used two working proportional hazards (PH) models, one for the event times and the other for the censoring times, to define a nearest neighbor imputing risk set. This risk set was then used to impute failure times for censored observations. Here, we adapt the method to the situation where the event and censoring times follow accelerated failure time models and propose to use the Buckley–James estimator as the two working models. Besides studying the performances of the proposed method, we also compare the proposed method with two popular methods for handling dependent censoring through the use of auxiliary variables, inverse probability of censoring weighted and parametric multiple imputation methods, to shed light on the use of them. In a simulation study with time‐independent auxiliary variables, we show that all approaches can reduce bias due to dependent censoring. The proposed method is robust to misspecification of either one of the two working models and their link function. This indicates that a working proportional hazards model is preferred because it is more cumbersome to fit an accelerated failure time model. In contrast, the inverse probability of censoring weighted method is not robust to misspecification of the link function of the censoring time model. The parametric imputation methods rely on the specification of the event time model. The approaches are applied to a prostate cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.     

Using Propensity Scores for Causal Inference: Pitfalls and Tips

Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (ie, matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated.Key words: propensity score, matching, inverse probability weighting, target population     

Multinomial logistic regression with missing outcome data: An application to cancer subtypes

Many diseases such as cancer and heart diseases are heterogeneous and it is of great interest to study the disease risk specific to the subtypes in relation to genetic and environmental risk factors. However, due to logistic and cost reasons, the subtype information for the disease is missing for some subjects. In this article, we investigate methods for multinomial logistic regression with missing outcome data, including a bootstrap hot deck multiple imputation (BHMI), simple inverse probability weighted (SIPW), augmented inverse probability weighted (AIPW), and expected estimating equation (EEE) estimators. These methods are important approaches for missing data regression. The BHMI modifies the standard hot deck multiple imputation method such that it can provide valid confidence interval estimation. Under the situation when the covariates are discrete, the SIPW, AIPW, and EEE estimators are numerically identical. When the covariates are continuous, nonparametric smoothers can be applied to estimate the selection probabilities and the estimating scores. These methods perform similarly. Extensive simulations show that all of these methods yield unbiased estimators while the complete-case (CC) analysis can be biased if the missingness depends on the observed data. Our simulations also demonstrate that these methods can gain substantial efficiency compared with the CC analysis. The methods are applied to a colorectal cancer study in which cancer subtype data are missing among some study individuals.     

Survival analysis using auxiliary variables via non-parametric multiple imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to recover information for censored observations. To conduct the imputation, we use two working survival models to define a nearest neighbour imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two non-parametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier imputation. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan-Meier imputation method correspond to the weighted Kaplan-Meier estimator. We also show that the Kaplan-Meier imputation method is robust to mis-specification of either one of the two working models. In a simulation study with time independent and time-dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the efficiency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable.     

Selection bias modeling using observed data augmented with imputed record-level probabilities

PurposeSelection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up.MethodsWe demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants.ResultsThe “true” or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data.ConclusionsThis internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study.