Based on empirical likelihood method and QR decomposition technique, an orthogonality empirical likelihood based estimation method for the fixed effects in linear mixed effects models is proposed. Under some regularit...Based on empirical likelihood method and QR decomposition technique, an orthogonality empirical likelihood based estimation method for the fixed effects in linear mixed effects models is proposed. Under some regularity conditions, the proposed empirical log-likelihood ratio is proved to be asymptotically chi-squared, and then the confidence intervals for the fixed effects are constructed. The proposed estimation procedure is not affected by the random effects,and then the resulting estimator is more effective. Some simulations and a real data application are conducted for further illustrating the performances of the proposed method.展开更多
Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using general...Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.展开更多
In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under so...In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.展开更多
Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, th...Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.展开更多
In this paper, an efficient shrinkage estimation procedure for the partially linear varying coefficient model (PLVC) with random effect is considered. By selecting the significant variable and estimating the nonzero c...In this paper, an efficient shrinkage estimation procedure for the partially linear varying coefficient model (PLVC) with random effect is considered. By selecting the significant variable and estimating the nonzero coefficient, the model structure specification is accomplished by introducing a novel penalized estimating equation. Under some mild conditions, the asymptotic properties for the proposed model selection and estimation results, such as the sparsity and oracle property, are established. Some numerical simulation studies and a real data analysis are presented to examine the finite sample performance of the procedure.展开更多
目的:全膝关节置换术后疼痛严重,须采用围手术期多模式镇痛,关节周围局部浸润(local infiltration analgesia,LIA)是镇痛方法之一,但作用时间较短。本研究通过线性混合效应模型比较全膝关节置换患者使用传统“鸡尾酒”疗法与加入硫酸镁...目的:全膝关节置换术后疼痛严重,须采用围手术期多模式镇痛,关节周围局部浸润(local infiltration analgesia,LIA)是镇痛方法之一,但作用时间较短。本研究通过线性混合效应模型比较全膝关节置换患者使用传统“鸡尾酒”疗法与加入硫酸镁和碳酸氢钠的改良“鸡尾酒”疗法的LIA早期镇痛疗效。方法:回顾性收集武威市人民医院骨二科2024年1月至2025年2月就诊的193例全膝关节置换术患者,以使用含有硫酸镁注射液和碳酸氢钠注射液的改良“鸡尾酒”为暴露因素,将改良“鸡尾酒”治疗患者作为实验组(n=87),传统“鸡尾酒”治疗患者作为对照组(n=106)。收集患者的一般资料,包括性别、年龄、身高、体重、体重指数(body mass index,BMI)、关节活动度(range of motion,ROM)、疼痛视觉模拟量表(Visual Analog Scale,VAS)评分、术前美国麻醉医师协会(American Society of Anesthesiologists,ASA)分级、手术侧别和手术时长。比较2组患者首次服用盐酸羟考酮缓释片的时间,术后12 h、1 d、2 d服用盐酸羟考酮缓释片的患者占比,术后住院时间;记录2组患者术后静息时和活动时VAS评分、膝ROM等重复测量数据用于线性混合效应模型分析。结果:2组共纳入193例患者。2组性别、年龄、身高、体重、BMI、ROM、VAS评分、ASA分级、手术侧别和手术时长等一般资料比较,差异均无统计学意义(均P>0.05);术后首次服用盐酸羟考酮缓释片间隔时间实验组较对照组长,差异有统计学意义(P<0.05),实验组术后12 h和1 d服用盐酸羟考酮缓释片患者占比小于对照组,差异有统计学意义(P<0.05),2组术后2 d服用盐酸羟考酮缓释片患者占比差异无统计学意义(P>0.05);术后住院时间差异无统计学意义(P>0.05)。线性混合效应模型分析结果显示:术后12~72 h实验组静息时VAS与对照组之间存在组间效应、时间效应和交互效应(均P<0.05),术后12~72 h实验组活动时VAS与对照组之间存在组间效应、时间效应和交互效应(均P<0.05),术后24~72 h实验组膝ROM和对照组之间存在时间效应、组间效应和交互效应(均P<0.05)。2组术后并发症发生率差异无统计学意义(P>0.05)。结论:与传统“鸡尾酒”疗法相比,含有硫酸镁和碳酸氢钠的改良“鸡尾酒”疗法在全膝关节置换术患者中可进一步降低术后早期疼痛,减少术后早期盐酸羟考酮缓释片服用量和改善早期膝ROM,但对术后住院时间无明显改善。展开更多
Background: Breeding dispersal is an important ecological process that affects species' population dynamics and colonization of new suitable areas. Knowledge of the causes and consequences of breeding dispersal is...Background: Breeding dispersal is an important ecological process that affects species' population dynamics and colonization of new suitable areas. Knowledge of the causes and consequences of breeding dispersal is fundamental to our understanding of avian ecology and evolution. Although breeding success for a wild and reintroduced population of the Crested Ibis(Nipponia nippon) has been reported, the relationships between individuals' breeding dispersal and their breeding success, age and sex remain unclear.Methods: Ibises' breeding dispersal distance, which is the distance moved by adults between sites of reproduction, was estimated based on the observations of consecutive breeding sites of marked ibis individuals. From observational and capture-recapture data(n as = 102) over 9 years, individuals' breeding dispersal probability in relation to age, sex, and reproductive success wanalyzed via a generalized linear mixed effect modeling approach.Results: Our results show that 55% males and 51% females keep their previous territories following nesting success. Failed breeding attempts increased dispersal probabilities. Both females and males failed in breeding were more likely to disperse with greater distances than successful birds(females: 825 ± 216 m vs 196 ± 101 m, males: 372 Crested Ibis exhibited a female-biased dispersal pattern that the mean dispersal distance± 164 m vs 210 ± 127 m). of females(435 ± 234 m) was much larger than that of males(294 ± 172 m).Conclusion: Our results are fundamental to predict the patterns of breeding dispersal related to reproductive success under different release sites. From the conservation point of view, landscape connectivity between the reintroduced populations should be taken into account in accordance with the distance of breeding dispersal.展开更多
The linear mixed-effects model （LMM） is a very useful tool for analyzing cluster data. In practice, however, the exact values of the variables are often difficult to observe. In this paper, we consider the LMM with ...The linear mixed-effects model （LMM） is a very useful tool for analyzing cluster data. In practice, however, the exact values of the variables are often difficult to observe. In this paper, we consider the LMM with measurement errors in the covariates. The empirical BLUP estimator of the linear combination of the fixed and random effects and its approximate conditional MSE are derived. The application to the estimation of small area is provided. Simulation study shows good performance of the proposed estimators.展开更多
基金Supported by the National Social Science Foundation of China(Grant No.18BTJ035).
文摘Based on empirical likelihood method and QR decomposition technique, an orthogonality empirical likelihood based estimation method for the fixed effects in linear mixed effects models is proposed. Under some regularity conditions, the proposed empirical log-likelihood ratio is proved to be asymptotically chi-squared, and then the confidence intervals for the fixed effects are constructed. The proposed estimation procedure is not affected by the random effects,and then the resulting estimator is more effective. Some simulations and a real data application are conducted for further illustrating the performances of the proposed method.
文摘Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.
基金the Natural Science Foundation of China(10371042,10671038)
文摘In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.
文摘Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.
文摘In this paper, an efficient shrinkage estimation procedure for the partially linear varying coefficient model (PLVC) with random effect is considered. By selecting the significant variable and estimating the nonzero coefficient, the model structure specification is accomplished by introducing a novel penalized estimating equation. Under some mild conditions, the asymptotic properties for the proposed model selection and estimation results, such as the sparsity and oracle property, are established. Some numerical simulation studies and a real data analysis are presented to examine the finite sample performance of the procedure.
文摘目的:全膝关节置换术后疼痛严重,须采用围手术期多模式镇痛,关节周围局部浸润(local infiltration analgesia,LIA)是镇痛方法之一,但作用时间较短。本研究通过线性混合效应模型比较全膝关节置换患者使用传统“鸡尾酒”疗法与加入硫酸镁和碳酸氢钠的改良“鸡尾酒”疗法的LIA早期镇痛疗效。方法:回顾性收集武威市人民医院骨二科2024年1月至2025年2月就诊的193例全膝关节置换术患者,以使用含有硫酸镁注射液和碳酸氢钠注射液的改良“鸡尾酒”为暴露因素,将改良“鸡尾酒”治疗患者作为实验组(n=87),传统“鸡尾酒”治疗患者作为对照组(n=106)。收集患者的一般资料,包括性别、年龄、身高、体重、体重指数(body mass index,BMI)、关节活动度(range of motion,ROM)、疼痛视觉模拟量表(Visual Analog Scale,VAS)评分、术前美国麻醉医师协会(American Society of Anesthesiologists,ASA)分级、手术侧别和手术时长。比较2组患者首次服用盐酸羟考酮缓释片的时间,术后12 h、1 d、2 d服用盐酸羟考酮缓释片的患者占比,术后住院时间;记录2组患者术后静息时和活动时VAS评分、膝ROM等重复测量数据用于线性混合效应模型分析。结果:2组共纳入193例患者。2组性别、年龄、身高、体重、BMI、ROM、VAS评分、ASA分级、手术侧别和手术时长等一般资料比较,差异均无统计学意义(均P>0.05);术后首次服用盐酸羟考酮缓释片间隔时间实验组较对照组长,差异有统计学意义(P<0.05),实验组术后12 h和1 d服用盐酸羟考酮缓释片患者占比小于对照组,差异有统计学意义(P<0.05),2组术后2 d服用盐酸羟考酮缓释片患者占比差异无统计学意义(P>0.05);术后住院时间差异无统计学意义(P>0.05)。线性混合效应模型分析结果显示:术后12~72 h实验组静息时VAS与对照组之间存在组间效应、时间效应和交互效应(均P<0.05),术后12~72 h实验组活动时VAS与对照组之间存在组间效应、时间效应和交互效应(均P<0.05),术后24~72 h实验组膝ROM和对照组之间存在时间效应、组间效应和交互效应(均P<0.05)。2组术后并发症发生率差异无统计学意义(P>0.05)。结论:与传统“鸡尾酒”疗法相比,含有硫酸镁和碳酸氢钠的改良“鸡尾酒”疗法在全膝关节置换术患者中可进一步降低术后早期疼痛,减少术后早期盐酸羟考酮缓释片服用量和改善早期膝ROM,但对术后住院时间无明显改善。
基金completely supported by the National Nature Science Foundation of China(Nos.31572282 and 31172103)
文摘Background: Breeding dispersal is an important ecological process that affects species' population dynamics and colonization of new suitable areas. Knowledge of the causes and consequences of breeding dispersal is fundamental to our understanding of avian ecology and evolution. Although breeding success for a wild and reintroduced population of the Crested Ibis(Nipponia nippon) has been reported, the relationships between individuals' breeding dispersal and their breeding success, age and sex remain unclear.Methods: Ibises' breeding dispersal distance, which is the distance moved by adults between sites of reproduction, was estimated based on the observations of consecutive breeding sites of marked ibis individuals. From observational and capture-recapture data(n as = 102) over 9 years, individuals' breeding dispersal probability in relation to age, sex, and reproductive success wanalyzed via a generalized linear mixed effect modeling approach.Results: Our results show that 55% males and 51% females keep their previous territories following nesting success. Failed breeding attempts increased dispersal probabilities. Both females and males failed in breeding were more likely to disperse with greater distances than successful birds(females: 825 ± 216 m vs 196 ± 101 m, males: 372 Crested Ibis exhibited a female-biased dispersal pattern that the mean dispersal distance± 164 m vs 210 ± 127 m). of females(435 ± 234 m) was much larger than that of males(294 ± 172 m).Conclusion: Our results are fundamental to predict the patterns of breeding dispersal related to reproductive success under different release sites. From the conservation point of view, landscape connectivity between the reintroduced populations should be taken into account in accordance with the distance of breeding dispersal.
基金supported by National Natural Science Foundation of China(Grant No.11301514)partially supported by National Natural Science Foundation of China(Grant Nos.11271355 and 70625004)National Bureau of Statistics of China(Grant No.2012LZ012)
文摘The linear mixed-effects model （LMM） is a very useful tool for analyzing cluster data. In practice, however, the exact values of the variables are often difficult to observe. In this paper, we consider the LMM with measurement errors in the covariates. The empirical BLUP estimator of the linear combination of the fixed and random effects and its approximate conditional MSE are derived. The application to the estimation of small area is provided. Simulation study shows good performance of the proposed estimators.
