医护人员职业紧张的结构方程模型分析

目的：建立医护人员职业紧张的结构方程模型，分析职业紧张因素，作为制定干预措施的依据。方法：应用职业紧张量表（OSI-R）对太原市某医院医务工作者进行问卷调查。结果：应用结构方程模型研究医务工作者职业紧张，结果符合专业解释。结论：结构方程模型用于职业紧张的研究，因考虑了测量误差，较传统线性模型，结果更可靠。     

呼吸机氧气体积分数测量误差与氧电池使用时间的关系分析

目的:研究呼吸机的氧电池实际使用时间与氧气体积分数测量误差的相关性。方法:分析呼吸机质量控制的现状,对比某院呼吸机近3 a质量控制检测结果,结合氧气体积分数报警故障处理案例,进行统计学对比分析。结果:呼吸机的氧气体积分数测量误差与氧电池使用时间呈近似线性相关性,氧电池启用430 d后的氧气体积分数测量误差会显著增加。结论:呼吸机氧气体积分数质量控制测量误差水平可作为氧电池性能评价及更换的重要参考依据。     

双能X射线骨密度仪检定结果分析及临床应用

目的：探讨双能X射线骨密度仪（DXA）检定结果在临床的应用。方法：按照JJG1050-2009《X、γ射线骨密度仪》规程方法检定DXA的重复性、测量误差，采用最小二乘法线性回归分析校正测量结果。结果：检定某型DXA的重复性<1%，临床上在比较患者治疗前后的骨密度变化，在0.05显著性水平，对超过2.8%骨密度的变化判断有效；在观察药物疗效的样本量计算时，要发现骨密度增高0.5%，需42例。该型DXA的测量误差范围在1.5%～13.6%，不满足规程要求，经线性校正后为-3.9%～3.9%，骨密度测量结果可信。某4台DXA的测量误差范围在校正前最大为-10.2%～15.0%，校正后最大为-2.0%～6.4%,4台DXA的测量误差变小且互相趋于一致，测量结果具有可比性。结论：骨密度仪重复性可以确定骨密度变化的最小显著变化值及计算观察药物疗效的样本量，对超差骨密度仪线性校正测量值，可使测量误差变小。不同型号DXA的测量标准模体，并对测量值进行线性校正，其测量结果具有可比性，可以互相校正。     

基于潜变量得分的多水平多变量回归模型在职业紧张评价中的应用

目的介绍基于潜变量得分的多水平多反应变量回归模型在职业紧张评价中的应用。方法为克服测量误差的存在，以职业紧张量表14个分项的潜变量得分，将之作为中问结果引入多水平多反应变量回归模型。结果职业任务各分项不同程度地引起职业紧张，而个体应变能力是减轻职业紧张行之有效的方式。随机系数反映这些影响在不同科室存在着不同。结论采用基于潜变量得分的多水平多反应变量回归模型既可有效降低测量误差，又得以合理地解释。尤其对于系统结构数据，多元线性模型的多水平理论比多水平潜变量分析方法更成熟可信。     

卫生统计

一种基于患者空间目标搜索定位算法的描述与实现；Haddon模型在突发公共卫生事件应对中的探讨；广东省SAILS传播模型实证研究；基于马氏深度函数的多变量参考值范围的统计方法；线性测量误差模型及其在医师职业紧张研究中的应用；试用贝叶斯法估计非脊髓灰质炎急性弛缓性麻痹标准化发病比；多重对应分析及其在工作满意度研究中的应用.     

线性回归中回归稀释偏倚校正的模拟研究

目的比较Peto-MacMahon非参数法(PM)和Rosner回归校准法(RC)对线性回归中回归稀释偏倚的校正效果,同时讨论不同情况下得到回归系数最佳校正效果时所需要的最小样本量。方法用Matlab软件随机模拟产生重复测量数据,建立线性回归模型,用PM法和RC法进行校正,比较设定的真实系数与校正前、后回归系数,评价校正效果。结果总体样本量很大时(大于10000),无论测量误差的大小,当重复测量样本量达到总体样本量的10%～30%,回归系数能达到最佳校正效果;两种方法稳定性差异无统计学意义,但PM法在计算上有更大的优势。总体样本量较小时(小于300),无论测量误差的大小,当重复测量样本量达到总体样本量的15%～30%,回归系数能达到最佳校正效果;但当测量误差很大,样本量小于50时,RC法更稳定。结论无论测量误差的大小,当重复测量数据达到一定样本量时,两种方法对回归系数的校正均有很好效果。在测量误差很大,且重复测量数据很少时,建议采用RC法进行校正;在其他情况下,建议采用PM法。     

测量误差变量与准确测量变量混合对研究真实性的影响

目的探讨测量误差变量与准确测量变量混合情况下测量误差对联系效应估计的影响。方法利用测量误差大小、准确测量变量与测量误差变量之间的相关性、准确测量变量的个数和联系效应之间的函数，采用R软件做图来讨论分析测量误差对研究真实性的影响。结果当连续变量Y和Z能准确测量，连续变量X不能准确测量时，无差异性测量误差使所估计的联系效应值总低于实际值，并随X与Z的相关程度的增加，测量误差所致的偏倚会进一步地恶化。在一个错分二分类变量X和一个准确测量连续变量Z混合的情况下，测量误差所致的偏倚不仅跟暴露测量的灵敏度和特异度有关，而且跟X与Z的相关系数以及X的暴露比例有关，并且随着相关系数的增加，AF值逐渐减少。在ρ=0．5时，AF值为1．419，变量X对应变量Y的联系效应估计值大于实际值，但当ρ增至0．9时，AF值为0．474，其联系效应估计值低于实际值，改变了错分偏倚的方向。结论在准确测量变量和测量误差变量混杂的研究中，用线性回归模型来分析估计多个自变量与应变量之间的联系时，对测量误差所致偏倚的识别、控制和评估是十分必要的，对结果的解释要谨慎。     

对数线性模型处理大型列联表资料的方法

对数线性模型是处理高维列联表的重要方法。本文通过分析Colles骨折在性别上的年龄分布情况,介绍了对数线性模型的应用方法。讨论了选择较优模型的方法和准则。并结合实际问题,着重讨论了模型参数的估计和解释,并适当进行了模型的残差分析。最后给出了应用对数线性模型时的注意事项。     

线性结构模型及其在现场资料分析中的应用研究

本文从多元线性回归和通径分析方法的一些缺点及现场资料潜变量的分析出发引入线性结构模型,对模型的基本原理给予简单地介绍,阐述了它与传统的一些统计方法的关系。结合实例分析了医学现场资料中一些潜变量之间的关系,得到了较满意的结果,较好地解决了多元线性回归和通径分析方法所不能解决的问题。并指出了线性结构模型在医学中的应用前景。     

线性结构模型及其医学应用

目的探：讨线性结构模型理论及其在医学中的应用。方法：通过建立线性结构模型，对医学实例进行分析。结果：由线性结构模型对医学实例的分析获得了较为满意的结果。结论：在生物医学领域中，当变量间具有较强的相关性时，以及当代医学模式主张“多因-多果”病因学观点的情况下，线性结构模型是一种值得推广应用的统计分析方法。     

Considerations for analysis of time‐to‐event outcomes measured with error: Bias and correction with SIMEX

For time‐to‐event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression‐free survival or time to AIDS progression) can be difficult to assess or reliant on self‐report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 common regression models, the Cox and Weibull models, in the setting of measurement error in the failure time outcome. We introduce an extension of the SIMEX method to correct for bias in hazard ratio estimates from the Cox model and discuss other analysis options to address measurement error in the response. A formula to estimate the bias induced into the hazard ratio by classical measurement error in the event time for a log‐linear survival model is presented. Detailed numerical studies are presented to examine the performance of the proposed SIMEX method under varying levels and parametric forms of the error in the outcome. We further illustrate the method with observational data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.     

基于个性化脉搏波传导参数的连续血压测量方法研究

目的：建立基于个性化脉搏波传导参数的连续血压测量方法模型。方法对18名受试者进行了运动实验,在运动实验过程中连续记录受试者的心电和手指容积脉搏波,同时利用动态血压计每2 min采集受试者的血压;每个受试者间隔1周的时间进行第二次实验;采用最小均方误差线性估计的方法来建立脉搏波传导时间和收缩压之间的线性模型。结果同一个受试者脉搏波传导时间与收缩压具有良好的线性关系（r2=0.91±0.06）,线性模型的检测误差为（3.48±1.69）mmHg,且具有较好的稳定性。但对于不同受试者,很难建立统一的线性模型（r2=0.14）。结论基于个性化脉搏波传导参数的连续血压测量方法是可行的,有望实现无袖带的连续血压测量。     

Linear mixed models for replication data to efficiently allow for covariate measurement error

It is well known that measurement error in the covariates of regression models generally causes bias in parameter estimates. Correction for such biases requires information concerning the measurement error, which is often in the form of internal validation or replication data. Regression calibration (RC) is a popular approach to correct for covariate measurement error, which involves predicting the true covariate using error‐prone measurements. Likelihood methods have previously been proposed as an alternative approach to estimate the parameters in models affected by measurement error, but have been relatively infrequently employed in medical statistics and epidemiology, partly because of computational complexity and concerns regarding robustness to distributional assumptions. We show how a standard random‐intercepts model can be used to obtain maximum likelihood (ML) estimates when the outcome model is linear or logistic regression under certain normality assumptions, when internal error‐prone replicate measurements are available. Through simulations we show that for linear regression, ML gives more efficient estimates than RC, although the gain is typically small. Furthermore, we show that RC and ML estimates remain consistent even when the normality assumptions are violated. For logistic regression, our implementation of ML is consistent if the true covariate is conditionally normal given the outcome, in contrast to RC. In simulations, this ML estimator showed less bias in situations where RC gives non‐negligible biases. Our proposal makes the ML approach to dealing with covariate measurement error more accessible to researchers, which we hope will improve its viability as a useful alternative to methods such as RC. Copyright © 2009 John Wiley & Sons, Ltd.     

Power and sample size calculations for generalized regression models with covariate measurement error

Covariate measurement error is often a feature of scientific data used for regression modelling. The consequences of such errors include a loss of power of tests of significance for the regression parameters corresponding to the true covariates. Power and sample size calculations that ignore covariate measurement error tend to overestimate power and underestimate the actual sample size required to achieve a desired power. In this paper we derive a novel measurement error corrected power function for generalized linear models using a generalized score test based on quasi-likelihood methods. Our power function is flexible in that it is adaptable to designs with a discrete or continuous scalar covariate (exposure) that can be measured with or without error, allows for additional confounding variables and applies to a broad class of generalized regression and measurement error models. A program is described that provides sample size or power for a continuous exposure with a normal measurement error model and a single normal confounder variable in logistic regression. We demonstrate the improved properties of our power calculations with simulations and numerical studies. An example is given from an ongoing study of cancer and exposure to arsenic as measured by toenail concentrations and tap water samples.     

There is no impact of exposure measurement error on latency estimation in linear models

Identification of the latency period for the effect of a time-varying exposure is key when assessing many environmental, nutritional, and behavioral risk factors. A pre-specified exposure metric involving an unknown latency parameter is often used in the statistical model for the exposure-disease relationship. Likelihood-based methods have been developed to estimate this latency parameter for generalized linear models but do not exist for scenarios where the exposure is measured with error, as is usually the case. Here, we explore the performance of naive estimators for both the latency parameter and the regression coefficients, which ignore exposure measurement error, assuming a linear measurement error model. We prove that, in many scenarios under this general measurement error setting, the least squares estimator for the latency parameter remains consistent, while the regression coefficient estimates are inconsistent as has previously been found in standard measurement error models where the primary disease model does not involve a latency parameter. Conditions under which this result holds are generalized to a wide class of covariance structures and mean functions. The findings are illustrated in a study of body mass index in relation to physical activity in the Health Professionals Follow-Up Study.     

SEGMENTED REGRESSION WITH ERRORS IN PREDICTORS: SEMI-PARAMETRIC AND PARAMETRIC METHODS

We consider the estimation of parameters in a particular segmented generalized linear model with additive measurement error in predictors, with a focus on linear and logistic regression. In epidemiologic studies segmented regression models often occur as threshold models, where it is assumed that the exposure has no influence on the response up to a possibly unknown threshold. Furthermore, in occupational and environmental studies the exposure typically cannot be measured exactly. Ignoring this measurement error leads to asymptotically biased estimators of the threshold. It is shown that this asymptotic bias is different from that observed for estimating standard generalized linear model parameters in the presence of measurement error, being both larger and in different directions than expected. In most cases considered the threshold is asymptotically underestimated. Two standard general methods for correcting for this bias are considered; regression calibration and simulation extrapolation (simex). In ordinary logistic and linear regression these procedures behave similarly, but in the threshold segmented regression model they operate quite differently. The regression calibration estimator usually has more bias but less variance than the simex estimator. Regression calibration and simex are typically thought of as functional methods, also known as semi-parametric methods, because they make no assumptions about the distribution of the unobservable covariate X. The contrasting structural, parametric maximum likelihood estimate assumes a parametric distributional form for X. In ordinary linear regression there is typically little difference between structural and functional methods. One of the major, surprising findings of our study is that in threshold regression, the functional and structural methods differ substantially in their performance. In one of our simulations, approximately consistent functional estimates can be as much as 25 times more variable than the maximum likelihood estimate for a properly specified parametric model. Structural (parametric) modelling ought not be a neglected tool in measurement error models. An example involving dust concentration and bronchitis in a mechanical engineering plant in Munich is used to illustrate the results. © 1997 by John Wiley & Sons, Ltd.     

A test for gene–environment interaction in the presence of measurement error in the environmental variable

The identification of gene–environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.     

Correcting for binomial measurement error in predictors in regression with application to analysis of DNA methylation rates by bisulfite sequencing

Motivated by a genetic application, this paper addresses the problem of fitting regression models when the predictor is a proportion measured with error. While the problem of dealing with additive measurement error in fitting regression models has been extensively studied, the problem where the additive error is of a binomial nature has not been addressed. The measurement errors here are heteroscedastic for two reasons; dependence on the underlying true value and changing sampling effort over observations. While some of the previously developed methods for treating additive measurement error with heteroscedasticity can be used in this setting, other methods need modification. A new version of simulation extrapolation is developed, and we also explore a variation on the standard regression calibration method that uses a beta‐binomial model based on the fact that the true value is a proportion. Although most of the methods introduced here can be used for fitting non‐linear models, this paper will focus primarily on their use in fitting a linear model. While previous work has focused mainly on estimation of the coefficients, we will, with motivation from our example, also examine estimation of the variance around the regression line. In addressing these problems, we also discuss the appropriate manner in which to bootstrap for both inferences and bias assessment. The various methods are compared via simulation, and the results are illustrated using our motivating data, for which the goal is to relate the methylation rate of a blood sample to the age of the individual providing the sample. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimating relative risk of a log‐transformed exposure measured in pools

Pooling biospecimens prior to performing laboratory assays is a useful tool to reduce costs, achieve minimum volume requirements and mitigate assay measurement error. When estimating the risk of a continuous, pooled exposure on a binary outcome, specialized statistical techniques are required. Current methods include a regression calibration approach, where the expectation of the individual‐level exposure is calculated by adjusting the observed pooled measurement with additional covariate data. While this method employs a linear regression calibration model, we propose an alternative model that can accommodate log‐linear relationships between the exposure and predictive covariates. The proposed model permits direct estimation of the relative risk associated with a log‐transformation of an exposure measured in pools. Published 2016. This article is a U.S. Government work and is in the public domain in the USA     

A comparison of least squares and conditional maximum likelihood estimators under volume endpoint censoring in tumor growth experiments

Measurements in tumor growth experiments are stopped once the tumor volume exceeds a preset threshold: a mechanism we term volume endpoint censoring. We argue that this type of censoring is informative. Further, least squares (LS) parameter estimates are shown to suffer a bias in a general parametric model for tumor growth with an independent and identically distributed measurement error, both theoretically and in simulation experiments. In a linear growth model, the magnitude of bias in the LS growth rate estimate increases with the growth rate and the standard deviation of measurement error. We propose a conditional maximum likelihood estimation procedure, which is shown both theoretically and in simulation experiments to yield approximately unbiased parameter estimates in linear and quadratic growth models. Both LS and maximum likelihood estimators have similar variance characteristics. In simulation studies, these properties appear to extend to the case of moderately dependent measurement error. The methodology is illustrated by application to a tumor growth study for an ovarian cancer cell line. Copyright © 2012 John Wiley & Sons, Ltd.