广义估计方程与准最小二乘在社区纵向数据分析中的应用

目的 应用广义估计方程和准最小二乘方法分析社区卫生服务中心纵向数据,探讨纵向数据分析的问题,为社区的随访的纵向数据的分析提供科学的方法. 方法 对收集的社区卫生服务中心的糖尿病病人血糖的纵向数据,分别使用广义估计方程和准最小二乘方法以及传统的线性回归模型进行分析并比较结果.同时比较三种方法的标准化残差图. 结果 广义估计方程不收敛时与传统线性模型的结果相同,显示糖尿病人血糖与教育水平相关,而广义估计方程收敛时与准最小二乘的结果相同,显示教育无统计学意义.从标准化残差图看广义估计方程和准最小二乘法对数据的拟合比传统回归好. 结论 广义估计方程和准最小二乘法都能有效的处理纵向数据.与广义估计方程相比,准最小二乘法有一些优势.     

解释变量非正交时logistic回归系数的估计

目的；研究解释变量非正交时，ｌｏｇｉｓｔｉｃ回归模型参数的估计。方法：引用线性回归系数主成分估计的思想，提出主成分改进的ｌｏｇｉｓｔｉｃ回归系数的加权最小二乘估计。结果：改进的方法能克服多元共线性引起的一般回归系数加权最小二乘估计方差扩大现象，估计值优于一般加权最小二乘估计。结论：用主成分改进的加权最小二乘估计为基础来筛选变量，从而避免变量间共线关系对变量选择的影响。     

二项型指数曲线在药-时曲线拟合中的应用

目的 探讨二项型指数曲线在药-时曲线拟合中的应用及其SAS实现.方法 借助SAS软件,调用REG过程以常规的残数法得到曲线参数的估计值,然后以这些估计值为初始值,调用NLIN过程进行非线性最小二乘法估计,得到优化的曲线模型.结果 残数法与非线性最小二乘法相结合所得最终曲线模型较残数法计算而来的模型有更好的拟合效果.结论 SAS编程可便捷实现二项型指数曲线对药-时曲线的拟合,且拟合效果较好,具有广阔的应用前景.     

核偏最小二乘回归及其在医学中的应用

目的研究用于处理解释变量与反应变量之间非线性关系或复杂关系的一种基于核函数的回归方法:核偏最小二乘回归。方法运用Monte-Carlo模拟方法,对核偏最小二乘回归的模型拟合效果和预测效果予以分析。结果模拟试验结果表明:核偏最小二乘回归估计性能均较高。结论核偏最小二乘回归是基于核函数的非线性回归方法,模型构建基于样本,而非解释变量空间,该方法特别适合于处理医学研究中各种类型资料,能够有效地处理解释变量与反应变量之间的非线性关系或复杂关系等方面。     

重复观测数据的半参数回归分析

目的 研究重复预测数据的回归分析技术。方法 利用半参数回归分析的原理与方法，结合重复观测数据的特点，建立重复观测数据的半参数回归模型，并进一步讨论模型参数的估计方法及假设检验公式。结果 讨论了重复观测数据的半参数回归模型的模型误差，分析了重复因素的效应及参数的影响，给出了其模型的方差分析表。结论 通过实例分析，表明对重复观测数据的处理，半参数回归分析的效果优于普通的最小二乘法和广义最小二乘法。     

半参数回归模型及模拟实例分析

目的 放宽经典线性模型中的解释变量的线性假定和探讨半参数回归分析模型。方法 利用最小惩罚二乘原理构造加权惩罚平方和,通过广义交互有效得分函数自动选择光滑参数值,用直接法求解方程组。结果 用SAS程序实现了半参数回归分析。得到了回归系数向量和样条函数的最小惩罚二乘估计,模拟实例表明,半参数回归模型较传统的线性模型有较强的适应性。结论 半参数回归模型是经典线性模型和非参数回归模型的一个混合体。可作为回归分析的一种新技术得到广泛应用。     

光滑样条非参数回归方法及医学应用

目的 改进回归分析的经典最小二乘估计方法和探讨光滑样条非参数回归分析方法。方法 利用三次函数和粗糙度惩罚方法的有机结合，构造惩罚平方和，通过广义交互有效得分函数和模式搜索法自动选择光滑参数值。结果 用ＳＡＳ程序实现了光滑样条非参数回归分析，得到了回归函数的最小惩罚二乘估计，实例表明，该方法优于传统方法和非参数Ｍｏｎｏｔｏｎｉｃ回归。结论 非参数回归分析方法能够最佳地兼顾拟合优度和光滑度，改进了经典     

区域人群相对医疗服务需要的测量方法及其在卫生资源配置中的应用研究

理论模型:医疗服务利用受需要、供给和社会经济学因素影响,供给在影响利用的同时,受到社会经济学因素的影响.方法:采用两阶段最小二乘法估计利用与需要、供给和社会经济学因素的关系模型,找到了合理的需要变量--标化两周患病率.引入反映各市区差异的哑变量,拟合利用与需要的关系模型,得出各市区需要变量系数.结果:将需要变量系数与人口加权后,得到各市区权重人口占总人口的比例,这是各市区医疗资源结构调整的标准和依据.     

2011-2016年中国艾滋病疫情时空特征分析

目的 研究2011-2016年全国31个省、直辖市和自治区的客运量、人均国内生产总值（gross do mestic product,GDP）、人口密度和每千人医疗机构床位数对艾滋病发病数影响的时空变化特性,为预防艾滋病提供依据。方法 建立时空加权泊松回归模型,采用局部线性地理加权回归方法和迭代加权最小二乘估计对系数函数进行估计及可视化,分析不同地区、不同年份下宏观因素对艾滋病发病数影响的时空非平稳性。结果 全国各地区艾滋病发病区存在明显的时空聚集性和变化趋势;不同地区、不同时间的宏观因素对艾滋病发病数的影响各不相同。结论 拟合优度诊断统计量（R2,AIC,MSE）验证时空加权泊松回归模型拟合效果优于泊松回归模型,更好地反映时空数据中时空交互效应和非平稳特征,表明中国艾滋病发病数的时空分布与四个宏观因素的变化密切相关。     

三项型指数曲线在“药-时”曲线拟合中的应用

目的 探讨三项型指数曲线在“药-时”曲线拟合中的应用及其SAS实现.方法 借助SAS软件,调用REG过程以常规的残数法得到曲线上各参数的估计值,然后以这些估计值为初始值,调用NLIN过程进行非线性最小二乘估计,得到优化的曲线回归方程.结果 残数法与非线性最小二乘法相结合所得最终曲线回归方程较仅用残数法计算而得的曲线回归方程有更好的拟合效果.结论 运用高级SAS编程技术可便捷地对“药-时”关系资料实现三项型指数曲线的拟合,且拟合效果较好,具有广阔的应用前景.     

疾病空间分布趋势面模型的共线性偏倚及其测量与控制

目的探讨趋势面分析中共线性偏倚及其测量与控制方法.方法以疾病监测资料为基础,引用回归诊断方法识别趋势面模型的共线性偏倚,进一步用岭回归趋势面模型控制共线性偏倚.结果趋势面分析往往存在共线性偏倚,利用岭回归趋势面分析可以在一定程度上控制共线性偏倚.结论在作趋势面分析时应当考虑共线性偏倚对结果的影响,并设法予以控制.     

不同混杂结构下广义倾向性评分法的模拟研究及应用

目的 通过构建存在不同混杂结构的广义倾向性评分(generalized propensity score,GPS)模型和结局模型,探索比较三种GPS估计法:广义倾向性评分-最小二乘法(generalized propensity score-ordinary least squares,GPS-OLS),广义倾向性评分...     

Comparing the performance of propensity score methods in healthcare database studies with rare outcomes

Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity‐based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a ‘plasmode’ simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes. Copyright © 2017 John Wiley & Sons, Ltd.     

产前超声估测胎儿体重研究进展

产前正确估测胎儿体重有利于及早发现宫内胎儿异常,及时治疗,减少产科并发症和围产儿病率和死亡率.产前利用超声测量胎儿各种生长径线,通过单参数公式或多参数公式估测胎儿体重,是目前使用最为广泛的方法,但准确性有待提高;采用三维超声测量胎儿各个器官的容积从而估测体重,可使准确性有所提高,方法较费时有待完善;神经网络方法通过输入一定的训练样本量来获取数据间的非线性关系,从而降低胎重估测的误差,但也存在训练数据选取,网络收敛稳定性的问题.总之,各种方法都有一定的优点和局限性.     

Logistic regression when binary predictor variables are highly correlated

Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.     

Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso

Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions. By introducing adaptive penalties to the groups of basis expansions, the nonlinear, linear, or non‐existent effects of observed and latent variables in the structural equation can be automatically detected. A simulation study demonstrates that the proposed method performs satisfactorily. This paper presents an application of revealing the observed and latent risk factors of diabetic kidney disease. Copyright © 2015 John Wiley & Sons, Ltd.     

复杂抽样下截取因变量回归系数方差估计的模拟研究

目的探讨复杂抽样下截取因变量数据拟合回归模型后其回归系数的方差估计。方法模拟复杂抽样下分别从左右方向发生截取的数据,按照是否考虑抽样特征分别拟合参数与半参数回归模型,给出两种情况下模型中回归系数的标准误,比较这两种情况所得结果的异同。结果在样本量固定的前提下拟合截取回归模型,考虑复杂抽样特征后估计所得的回归系数与假设完全随机抽样一致,但其回归系数的标准误却不同于复杂抽样的情形。如果群内异质性高,群内相关系数很小,在复杂抽样条件下回归系数的标准误要低于不考虑复杂抽样特征的情形。结论对于抽样框完整的复杂抽样截取数据,进行数据处理时应尽可能地将抽样特征考虑在内,运用复杂抽样数据方差估计得到的结果更接近于实际情况,统计推断结果更加真实可靠。     

Two‐step estimation in ratio‐of‐mediator‐probability weighted causal mediation analysis

This study investigates appropriate estimation of estimator variability in the context of causal mediation analysis that employs propensity score‐based weighting. Such an analysis decomposes the total effect of a treatment on the outcome into an indirect effect transmitted through a focal mediator and a direct effect bypassing the mediator. Ratio‐of‐mediator‐probability weighting estimates these causal effects by adjusting for the confounding impact of a large number of pretreatment covariates through propensity score‐based weighting. In step 1, a propensity score model is estimated. In step 2, the causal effects of interest are estimated using weights derived from the prior step's regression coefficient estimates. Statistical inferences obtained from this 2‐step estimation procedure are potentially problematic if the estimated standard errors of the causal effect estimates do not reflect the sampling uncertainty in the estimation of the weights. This study extends to ratio‐of‐mediator‐probability weighting analysis a solution to the 2‐step estimation problem by stacking the score functions from both steps. We derive the asymptotic variance‐covariance matrix for the indirect effect and direct effect 2‐step estimators, provide simulation results, and illustrate with an application study. Our simulation results indicate that the sampling uncertainty in the estimated weights should not be ignored. The standard error estimation using the stacking procedure offers a viable alternative to bootstrap standard error estimation. We discuss broad implications of this approach for causal analysis involving propensity score‐based weighting.     

Simultaneous estimation of parameters in the bivariate Emax model

In this paper, we explore inference in multi‐response, nonlinear models. By multi‐response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose‐response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation‐by‐equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation‐by‐equation estimation. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting SF-6D from the European Organization for Treatment and Research of Cancer Quality of Life Questionnaire Scores in Patients with Colorectal Cancer

ObjectivesTo develop a mapping model for estimating six-dimensional health state short form (SF-6D) utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaires (QLQ-C30 and QLQ-CR29) scores in patients with colorectal cancer (CRC), with and without adjustment for clinical and demographic characteristics.MethodsOrdinary least squares regression models were applied to a cross-sectional data set of 216 patients with CRC collected from a regional hospital in Hong Kong. Item responses or scale scores of cancer-specific (QLQ-C30) and colorectal-specific health-related quality-of-life (QLQ-CR38/CR29) data and selected demographic and clinical characteristics of patients were used to predict the SF-6D scores. Model goodness of fit was examined by using exploratory power (R² and adjusted R²), Akaike information criterion, and Bayesian information criterion, and predictive performance was evaluated by using root mean square error, mean absolute error, and Spearman’s correlation coefficients between predicted and observed SF-6D scores. Models were validated by using an independent data set of 56 patients with CRC.ResultsBoth scale and item response models explained more than 67% of the variation in SF-6D scores. The best-performing model based on goodness of fit (R² = 75.02%), predictive ability in the estimation (root mean square error = 0.080, mean absolute error = 0.065), and validation data set prediction (root mean square error = 0.103, mean absolute error = 0.081) included variables of main and interaction effects of the QLQ-C30 supplemented by QLQ-CR29 subset scale responses and a demographic (sex) variable.ConclusionsSF-6D scores can be predicted from QLQ-C30 and QLQ-CR38/CR29 scores with satisfactory precision in patients with CRC. The mapping model can be applied to QLQ-C30 and QLQ-CR38/CR29 data sets to produce utility scores for the appraisal of clinical interventions targeting patients with CRC using economic evaluation.