以广义估计方程研究浙江省肺结核耐药预测方程

目的 耐药肺结核患者可能对一种或多种抗结核药物耐药。对这类因变量为多结局非独立的数据,本文探讨应用广义估计方程分析耐药危险因素,构建预测方程,探索预警模型建立方向。方法 对浙江省30个耐药监测点的涂阳患者进行药敏检测和问卷调查,以对13种抗结核药物的耐药情况为因变量,可能危险因素为自变量,用SAS的GENMOD模块构建广义估计模型。结果 本研究中基线水平下发生耐药的概率为20.26%,有统计学意义的耐药影响因素包括年龄、保险、是否合并乙型肝炎、治疗史及停药情况。根据各因素对耐药发生的影响程度得到预测方程。结论 广义估计方程解决了耐药数据因变量相关性的问题,有效利用非独立数据提供的信息,且参数估计稳健,为耐药危险因素评价和预警模型构建提供更全面的信息。     

利用因果森林估计异质性人群下个体的处理效应

目的 探讨因果森林在异质性人群中估计个体处理效应的有效性及如何应用于实例数据以挖掘异质性人群特征。方法 设计4种模拟方案,通过模拟试验验证因果森林在不同处理效应环境设置下对个体处理效应进行估计的效果,并应用于右心导管插入术实例数据集进行分析。结果 模拟试验结果表明,在4种不同效应值设置下,用因果森林方法所估计的个体处理效应值都能与总体效应相吻合,符合预期分布;实例数据分析结果显示绝大多数患者个体处理效应为正值,使用RHC会导致该样本人群180 d死亡率增高,2月生存模型估计概率和白蛋白含量偏低的患者在使用RHC后更倾向于有较低的死亡风险。结论 因果森林能够有效地估计个体处理效应,为个体是否接受某种处理提供建议。     

甘肃省2019-2022年原发性免疫性血小板减少症的流行病学特征分析

目的 描述甘肃省2019-2022年原发性免疫性血小板减少症（ITP）人群中发病水平及流行病学特征,为ITP的诊治、病因学研究及该病与疫苗接种的关联性分析判断提供证据支持。方法 使用Spark软件从甘肃省电子病历数据库中提取2019-2022年首次确诊的ITP病例,描述2019-2022年ITP新发病例的发病率及其流行病学特征,采用Poisson分布计算发病率,并估计其95%CI,使用Poisson回归进行组间差异比较。结果 2019-2022年甘肃省首次诊断为ITP共4 159例,总发病率为4.11/10万（95%CI：3.98/10万~4.23/10万）,男性为3.49/10万（95%CI：3.33/10万~3.65/10万）,女性为4.74/10万（95%CI：4.56/10万~4.94/10万）,差异有统计学意义（P＜0.001）;儿童及老年人发病率高,男性在＜10岁儿童及≥80岁老年人群中的发病风险高于女性,女性在成年以后（20~69岁）高于男性;甘肃省ITP发病峰值出现在夏季（6、7、8月）,谷值在2、10月,各年龄组人群发病水平在一年中呈周期性变化;地区分布为东部和西部发病风险高,中部发病风险低。结论 2019-2022年甘肃省儿童及老年人ITP发病率高,且男性发病风险高于女性,在总人群中女性发病风险较高,亦存在季节性和区域性高发现象。     

广州市MSM“互联网+艾滋病综合预防服务体系”对HIV/AIDS关怀效果分析

目的 分析广州市MSM“互联网+艾滋病综合服务体系”对HIV/AIDS关怀效果。方法 通过艾滋病防治基本信息系统下载广州市MSM HIV/AIDS报告及随访数据,分析2008－2014年（发现即治疗策略实施前）的HIV/AIDS随访转介情况和治疗前随访依从性、2017－2018年（发现即治疗策略实施后）的HIV/AIDS治疗转介情况。按照不同服务类型,分为互联网+服务组、艾滋病咨询检测组和医疗服务组。采用广义估计方程（General Estimating Equation,GEE）分析研究对象每年随访保持情况。采用Cox比例风险回归模型比较各组30 d治疗转介率的差异。结果 在发现即治疗策略实施前     

中华预防医学会伤害预防与控制分会妇女和老年人伤害研究学组成立

目的论述结构方程模型(SEM)方法在流行病学研究中的应用。方法简述SEM的主要构成、统计假设和目前常用的软件及这一方法如何在流行病学研究中应用和对应用中的有关问题的处理。结果相对于传统的流行病学方法,SEM是一种综合思维方法,不仅分析因素和疾病之间的关系,也分析因素和因素之间的关系;同样是一种验证性的方法,对于有些复杂问题的流行病学研究,特别是以理论为依据的研究颇为重要;SEM分析能够得到潜在变量的有关参数,并对表述潜在变量的显变量的测量误差做出估计。结论SEM能够应用于流行病学的研究,且具有较传统流行病学分析方法无法比拟的优势。     

新型冠状病毒肺炎的早期传染病流行病学参数估计研究

目的 研究截至2020年1月31日新型冠状病毒肺炎疫情的早期流行动态,估计该疫情的基本再生数(R0)、潜伏期和世代间隔等流行病学参数。方法 使用威布尔、伽马和对数正态分布拟合从报告病例信息中获取的潜伏期和世代间隔数据的概率分布,采用Akaike信息准则确定最优模型。考虑到疫情还在流行中,应用指数增长模型拟合了2020年1月15-31日的疫情数据,并利用指数增长法、最大似然法和SEIR模型估计R0。结果 截至2020年1月26日早期疫情遵循指数增长模式,随后增长趋势有所减缓。平均潜伏期为5.01(95%CI:4.31~5.69)d;平均世代间隔为6.03(95%CI:5.20~6.91)d。3种方法估计的R0分别为3.74(95%CI:3.63~3.87),3.16(95%CI:2.90~3.43)和3.91(95%CI:3.71~4.11)。结论 世代间隔和潜伏期都更符合伽马分布,世代间隔均值比潜伏期均值长1.02 d;R0较高,疫情形势较为严峻;1月26日是疫情动态的一个转折点,之后疫情增长趋势有所减缓。     

辽宁省2004-2010年洪涝灾害对细菌性痢疾发病影响的分析

目的 研究辽宁省洪涝灾害对细菌性痢疾发病的影响。方法 收集辽宁省2004-2010年细菌性痢疾月发病数据、洪涝灾害、气象和人口数据,运用面板Poisson回归模型定量分析洪涝灾害对细菌性痢疾发病的影响。结果 研究期间细菌性痢疾月平均发病率为2.17/10万,病例主要分布于7-9月。Spearman相关分析显示,洪涝灾害对细菌性痢疾的发病不存在滞后效应。在调整了气象因素对疾病发病的影响后,面板数据分析显示,洪涝灾害对细菌性痢疾发病存在影响,IRR=1.4394(95%CI:1.4081~1.4714)。结论 研究期间辽宁省洪涝灾害可使灾后人群细菌性痢疾的发病风险升高。     

天津市2010－2012年社区人群百日咳血清流行病学调查

目的 研究天津市社区人群中百日咳血清流行病学水平.方法 应用ELISA对2010－2012年天津市社区人群单份血百日咳毒素IgG(PT-IgG)抗体进行检测,分析抗体水平与百日咳发病率之间的关系.结果 1 825人的PT-IgG抗体平均阳性率为10.96%,其中0～3岁组最高,为24.37%～13.61%(P<0.001).4～83岁年龄组阳性率为8.84%,估计发病率为10 852/10万,其中51～83岁组估计发病率最高,为18 986/10万(P=0.001),且随着年龄的增长呈线性升高趋势(r=0.976,P<0.001).3年间抗体阳性率的差异有统计学意义(P=0.001),与当年报告发病率之间呈线性相关(r=0.992,P<0.001).3个监测地区间抗体阳性率的差异有统计学意义(P=0.034),与该监测地区年均报告发病率呈线性关系(r=0.996,P<0.001).结论 天津市百日咳发病的流行病学趋势和人群感染的血清流行病学趋势一致.     

藏族农村育龄妇女自然流产的流行病学特征及其影响因素分析

目的 了解藏族农村育龄妇女自然流产状况及其相关影响因素。方法 对2006-2012年藏族农村孕妇采取入户访问获得其生育史,并进行随访直至获得本次妊娠结局。采用广义Poisson回归模型探索影响自然流产的因素,估计各研究因素的OR值及其95%Cl。结果 共随访l557名孕妇,总妊娠2687次,总产次2382次,发生自然流产171人204次;人工流产93人101次。自然流产妊娠比为7.6%,自然流产率为7.9%,发生自然流产的育龄妇女比例为11.O%。孕次是发生自然流产的重要原因,随着孕次增加,育龄妇女发生自然流产的风险增加,孕次超过3次时其风险最大,Poisson回归分析OR=3.921(95%Cl:2.553～6.021),OR=4.722(95%17/:2.834～7,866);随着产次的增加,育龄妇女发生自然流产的风险减少。自然流产的发生风险与怀孕年份有关,2009年后妇女发生自然流产的风险较低,OR=O.419(95%Cl:0.285。0.616),OR=0.580(95%Cl:0.380～0.885)。并未发现社会人口学特征与其自然流产的发生有显著关联。结论 藏族育龄妇女自然流产率并不高于陕西等平原地区,加强孕产期保健、延长生育间隔和减少育龄妇女的妊娠次数有助于降低西藏地区育龄妇女发生自然流产的风险。     

2012年江苏省重点地区职业人群布鲁氏菌病感染现况调查

目的了解江苏省重点地区职业人群布鲁氏菌病(布病)的感染现况及其危险因素。方法选择江苏省重点地区规模较大的3家屠宰厂和牲畜交易市场、饲养场各1家238名从业人员开展布病感染状况的横断面调查并分析其危险因素。结果调查发现感染者50例, 感染率高达21%(50/238), 感染率在性别、年龄、从业年限、工种上的差异均无统计学意义。危险因素分析结果显示屠宰是高危工种(RR=1.80, 95%CI:1.1～3.1), 其中从事家畜屠宰“放血”是高危岗位(RR=1.90, 95%CI:1.1-3.3), 进食前洗手是保护因素(RR=0.25, 95%CI:0.14。0.44)。结论江苏省家畜屠宰、交易、饲养等场所职业人群存在布病感染, 应采取相应控制措施。     

A comparison of the generalized estimating equation approach with the maximum likelihood approach for repeated measurements

Liang and Zeger proposed an extension of generalized linear models to the analysis of longitudinal data. Their approach is closely related to quasi-likelihood methods and can handle both normal and non-normal outcome variables such as Poisson or binary outcomes. Their approach, however, has been applied mainly to non-normal outcome variables. This is probably due to the fact that there is a large class of multivariate linear models available for normal outcomes such as growth models and random-effects models. Further-more, there are many iterative algorithms that yield maximum likelihood estimators )MLEs( of the model parameters. The multivariate linear model approach, based on maximum likelihood )ML( estimation, specifies the joint multivariate normal distribution of outcome variables, while the approach of Liang and Zeger, based on the quasi-likelihood, specifies only the marginal distributions. In this paper, I compare the approach of Liang and Zeger and the ML approach for the multivariate normal outcomes. I show that the generalized estimating equation )GEE( reduces to the score equation only when the data do not have missing observations and the correlation is unstructured. In more general cases, however, the GEE estimation yields consistent estimators that may differ from the MLEs. That is, the GEE does not always reduce to the score equation even when the outcome variables are multivariate normal. I compare the small sample properties of the GEE estimators and the MLEs by means of a Monte Carlo simulation study.     

An efficient and robust method for analyzing population pharmacokinetic data in genome‐wide pharmacogenomic studies: a generalized estimating equation approach

Powerful array‐based single‐nucleotide polymorphism‐typing platforms have recently heralded a new era in which genome‐wide studies are conducted with increasing frequency. A genetic polymorphism associated with population pharmacokinetics (PK) is typically analyzed using nonlinear mixed‐effect models (NLMM). Applying NLMM to large‐scale data, such as those generated by genome‐wide studies, raises several issues related to the assumption of random effects as follows: (i) computation time: it takes a long time to compute the marginal likelihood; (ii) convergence of iterative calculation: an adaptive Gauss–Hermite quadrature is generally used to estimate NLMM; however, iterative calculations may not converge in complex models; and (iii) random‐effects misspecification leads to slightly inflated type‐I error rates. As an alternative effective approach to resolving these issues, in this article, we propose a generalized estimating equation (GEE) approach for analyzing population PK data. In general, GEE analysis does not account for interindividual variability in PK parameters; therefore, the usual GEE estimators cannot be interpreted straightforwardly, and their validities have not been justified. Here, we propose valid inference methods for using GEE even under conditions of interindividual variability and provide theoretical justifications of the proposed GEE estimators for population PK data. In numerical evaluations by simulations, the proposed GEE approach exhibited high computational speed and stability relative to the NLMM approach. Furthermore, the NLMM analysis was sensitive to the misspecification of the random‐effects distribution, and the proposed GEE inference is valid for any distributional form. We provided an illustration by using data from a genome‐wide pharmacogenomic study of an anticancer drug. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of robust estimating equations to the analysis of quantitative longitudinal data.

A model fit by general estimating equations (GEE) has been used extensively for the analysis of longitudinal data in medical studies. To some extent, GEE tries to minimize a quadratic form of the residuals, and therefore is not robust in the sense that it, like least squares estimates, is sensitive to heavy-tailed distributions, contaminated distributions and extreme values. This paper describes the family of truncated robust estimating equations and its properties for the analysis of quantitative longitudinal data. Like GEE, the robust estimating equations aim to assess the covariate effects in the generalized linear model in the complete population of observations, but in a manner that is more robust to the influence of aberrant observations. A simulation study has been conducted to compare the finite-sample performance of GEE and the robust estimating equations under a variety of error distributions and data structures. It shows that the parameter estimates based on GEE and the robust estimating equations are approximately unbiased and the type I errors of Wald tests do not tend to be inflated. GEE is slightly more efficient with pure normal data, but the efficiency of GEE declines much more quickly than the robust estimating equations when the data become contaminated or have heavy tails, which makes the robust estimating equations advantageous with non-normal data. Both GEE and the robust estimating equations are applied to a longitudinal analysis of renal function in the Diabetes Control and Complications Trial (DCCT). For this application, GEE seems to be sensitive to the working correlation specification in that different working correlation structures may lead to different conclusions about the effect of intensive diabetes treatment. On the other hand, the robust estimating equations consistently conclude that the treatment effect is highly significant no matter which working correlation structure is used. The DCCT Research Group also demonstrated a significant effect using a mixed-effects longitudinal model.     

Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random

The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop-outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop-out mechanism is correctly specified. In this approach, observations or person-visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time-specific means of a repeated binary response with MAR drop-outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop-out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation-level weights gave more efficient estimates than a weighted GEE procedure with cluster-level weights.     

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased with non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear generalized estimating equations (GEE) models for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis.     

The analysis of the influence of birth order and other factors in multiple birth data

We compare three methods which can be used to analyse the influence of birth order and other factors on health outcomes in multiple birth data. We consider marginal models based on generalized estimating equations (GEE) and two kinds of conditional models; conditional logistic regression (CLR) and mixed effects models (MEM). Although the models may be written similarly, there are differences in both the interpretation and the numerical values assigned to the parameters. Our main conclusion is that GEE and MEM are preferable to CLR since they provide more flexibility in dealing with missing values and covariates. The choice between GEE and MEM is less obvious and depends on the data, the parameter of interest and statistical power.     

Learning curve estimation in medical devices and procedures: hierarchical modeling

In the use of medical device procedures, learning effects have been shown to be a critical component of medical device safety surveillance. To support their estimation of these effects, we evaluated multiple methods for modeling these rates within a complex simulated dataset representing patients treated by physicians clustered within institutions. We employed unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE) and generalized linear mixed effect models. We found that both methods performed well, but that the GEE may have some advantages over the generalized linear mixed effect models for ease of modeling and a substantially lower rate of model convergence failures. We then focused more on using GEE and performed a separate simulation to vary the shape of the learning curve as well as employed various smoothing methods to the plots. We concluded that while both hierarchical methods can be used with our mathematical modeling of the learning curve, the GEE tended to perform better across multiple simulated scenarios in order to accurately model the learning effect as a function of physician and hospital hierarchical data in the use of a novel medical device. We found that the choice of shape used to produce the ‘learning‐free’ dataset would be dataset specific, while the choice of smoothing method was negligibly different from one another. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data. Copyright © 2017 John Wiley & Sons, Ltd.     

Bias-reduced and separation-proof GEE with small or sparse longitudinal binary data

Generalized estimating equation (GEE) is a popular approach for analyzing correlated binary data. However, the problems of separation in GEE are still unknown. The separation created by a covariate often occurs in small correlated binary data and even in large data with rare outcome and/or high intra-cluster correlation and a number of influential covariates. This paper investigated the consequences of separation in GEE and addressed them by introducing a penalized GEE, termed as PGEE. The PGEE is obtained by adding Firth-type penalty term, which was originally proposed for generalized linear model score equation, to standard GEE and shown to achieve convergence and provide finite estimate of the regression coefficient in the presence of separation, which are not often possible in GEE. Further, a small-sample bias correction to the sandwich covariance estimator of the PGEE estimator is suggested. Simulations also showed that the GEE failed to achieve convergence and/or provided infinitely large estimate of the regression coefficient in the presence of complete or quasi-complete separation, whereas the PGEE showed significant improvement by achieving convergence and providing finite estimate. Even in the presence of near-to-separation, the PGEE also showed superior properties over the GEE. Furthermore, the bias-corrected sandwich estimator for the PGEE estimator showed substantial improvement over the standard sandwich estimator by reducing bias in estimating type I error rate. An illustration using real data also supported the findings of simulation. The PGEE with bias-corrected sandwich covariance estimator is recommended to use for small-to-moderate size sample (N ≤ 50) and even can be used for large sample if there is any evidence of separation or near-to-separation.     

A robust and unified framework for estimating heritability in twin studies using generalized estimating equations

The ‘heritability’ of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17,804 traits;¹ thus twin studies are popular for estimating heritability. Researchers are often interested in estimating heritability for non-normally distributed outcomes such as binary, counts, skewed or heavy-tailed continuous traits. In these settings, the traditional normal ACE model (NACE) and Falconer's method can produce poor coverage of the true heritability. Therefore, we propose a robust generalized estimating equations (GEE2) framework for estimating the heritability of non-normally distributed outcomes. The traditional NACE and Falconer's method are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding ‘GEE2-Falconer’ can incorporate mean and variance-level covariate effects (e.g. let heritability vary by sex or age). Given a non-normally distributed outcome, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend GEE2-Falconer for estimating the heritability of non-normally distributed outcomes in twin studies.     

Properties of analysis methods that account for clustering in volume-outcome studies when the primary predictor is cluster size

In recent years health services researchers have conducted 'volume-outcome' studies to evaluate whether providers (hospitals or surgeons) who treat many patients for a specialized condition have better outcomes than those that treat few patients. These studies and the inherent clustering of events by provider present an unusual statistical problem. The volume-outcome setting is unique in that 'volume' reflects both the primary factor under study and also the cluster size. Consequently, the assumptions inherent in the use of available methods that correct for clustering might be violated in this setting. To address this issue, we investigate via simulation the properties of three estimation procedures for the analysis of cluster correlated data, specifically in the context of volume-outcome studies. We examine and compare the validity and efficiency of widely-available statistical techniques that have been used in the context of volume-outcome studies: generalized estimating equations (GEE) using both the independence and exchangeable correlation structures; random effects models; and the weighted GEE approach proposed by Williamson et al. (Biometrics 2003; 59:36-42) to account for informative clustering. Using data generated either from an underlying true random effects model or a cluster correlated model we show that both the random effects and the GEE with an exchangeable correlation structure have generally good properties, with relatively low bias for estimating the volume parameter and its variance. By contrast, the cluster weighted GEE method is inefficient.