检索结果-内蒙古大学图书馆

A shared parameter model for the estimation of longitudinal concomitant intervention effects

BIOSTATISTICS 2011年第4期12卷 737-749页

作者： Wu, Colin O. Tian, Xin Jiang, Wenhua NHLBI Off Biostat Res Bethesda MD 20892 USA

We investigate a change-point approach for modeling and estimating the regression effects caused by a concomitant intervention in a longitudinal study. Since a concomitant intervention is often introduced when a patient's health status exhibits undesirable trends, statistical models without properly incorporating the intervention and its starting time may lead to biased estimates of the intervention effects. We propose a shared parameter change-point model to evaluate the pre- and postintervention time trends of the response and develop a likelihood-based method for estimating the intervention effects and other parameters. Application and statistical properties of our method are demonstrated through a longitudinal clinical trial in depression and heart disease and a simulation study.

关键词： Change-point model Concomitant intervention Likelihood Longitudinal study shared parameter model

来源：评论

学校读者我要写书评

暂无评论

Predictors of refraction prediction error after cataract surgery: a shared parameter model to account for missing post-operative measurements

引用

STATISTICAL METHODS AND APPLICATIONS 2022年第2期31卷 343-364页

作者： Miller, D. Claire MaWhinney, Samantha Patnaik, Jennifer L. Christopher, Karen L. Lynch, Anne M. Wagner, Brandie D. Colorado Sch Publ Hlth Dept Biostat & Informat Aurora CO 80045 USA Univ Colorado Dept Ophthalmol Sch Med Aurora CO 80045 USA

Cataract surgery is a common procedure that involves removing the cataractous lens of the eye and implanting a new clear plastic lens, with a goal of improving vision and often changing the refraction of the eye. Unexpected refractive outcomes, or refraction prediction error (PE), may be more likely to occur among patients with certain preexisting ocular conditions. However, longitudinal refractive measurements are often missing after surgery, making it difficult to accurately assess which ocular comorbidities lead to increased PE. Moreover, patients with ideal refractive outcomes in one or both eyes may be less likely to return to the clinic, thus more likely to have missing measurements that are missing not at random (MNAR). Despite this potential complication to data analysis, very few studies evaluating PE address the missing data mechanism and the effect it may have on the results. We propose the application of a shared parameter model to reduce bias in situations of MNAR data and compare this to a linear mixed model that is not able to account for this mechanism. We also conduct a simulation study to better understand the most plausible missing mechanism in our study and to characterize situations in which the shared parameter model may be necessary. Applied to the cataract surgery data, the shared parameter model gives similar results as the linear mixed model, finding that a history of LASIK, PRK, RK, high myopia, hyperopia, astigmatism, or combined surgery lead to increased PE. In addition, the simulations confirm that under certain scenarios, a shared parameter model will greatly reduce bias in situations of MNAR.

关键词： Joint model shared parameter model Missing not at random Refraction prediction error Cataract surgery outcomes

来源：评论

学校读者我要写书评

暂无评论

A shared parameter location scale mixed effect model for EMA data subject to informative missing

引用

HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2018年第4期18卷 227-243页

作者： Lin, Xiaolei Mermelstein, Robin Hedeker, Donald Univ Chicago Dept Publ Hlth Sci Chicago IL 60637 USA Univ Illinois Inst Hlth Res & Policy Chicago IL USA

In this paper, we address the problem of accounting for informative missing in the context of ecological momentary assessment studies (sometimes referred to as intensive longitudinal studies), where each study unit gets measured intensively over time and intermittent missing is usually present. We present a shared parameter modeling approach that links the primary longitudinal outcome with potentially informative missingness by a common set of random effects that summarize a subjects' specific traits in terms of their mean (location) and variability (scale). The primary outcome, conditional on the random effects, are allowed to exhibit heterogeneity in terms of both the mean and within subject variance. Unlike previous methods which largely rely on numerical integration or approximation, we estimate the model by a full Bayesian approach using Markov Chain Monte Carlo. An adolescent mood study example is illustrated together with a series of simulation studies. Results in comparison to more conventional approaches suggest that accounting for the common but unobserved random subject mean and variance effects, shared between the primary outcome and missingness models, can significantly improve the model fit, and also provide the benefit of understanding how missingness can affect the inference for the primary outcome.

关键词： Ecological momentary assessments Informative missing Mixed effects shared parameter model

来源：评论

学校读者我要写书评

暂无评论

A SAS macro for the joint modeling of longitudinal outcomes and multiple competing risk dropouts

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 138卷 23-30页

作者： Wang, Wei Wang, Wanmei Mosley, Thomas H. Griswold, Michael E. Univ Mississippi Med Ctr Ctr Biostat & Bioinformat New Guyton Res Bldg G5622500 North State St Jackson MS 39216 USA Univ Mississippi Med Ctr Dept Med Div GeriatrMemory Impairment & Neurodegenerat Di Off Annex Bldg2500 North State St Jackson MS 39216 USA

Background and objectives: The joint modeling of longitudinal and survival data to assess effects of multiple informative dropout mechanisms on longitudinal outcomes inference has received considerable attention during recent years;related statistical programs to apply these methods have been lacking. This paper provides a SAS macro implementation of a shared parameter model to accommodate the analysis of longitudinal outcomes in the presence of multiple competing survival/dropout events. Methods: In this macro, we assumed that the associations between the survival and the longitudinal submodels are linked through a set of shared random effects. The submodel for the longitudinal outcome takes the form of a linear mixed effects model, with specifications for the random intercept and/or random slope. The survival submodel allows up to three different competing causes for dropout, each allowing either an exponential or Weibull parametric baseline hazard function. In addition, information criterion fit statistics AIC and BIC are provided to assist with parametric baseline hazard function selection. Results: We illustrate the SAS Macro in a cognitive decline study sensitivity analysis using data from the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). In addition, we also conduct a simulation study to show that the joint model provides unbiased parameter estimates when informative dropout exists compared against separate model approach which assumes missing at random dropout mechanisms. Conclusions: We have presented a SAS macro to implement a shared parameter model for a longitudinal outcome and multiple cause-specific dropouts and made the macro code freely available for download. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

关键词： Joint modeling shared parameter model Longitudinal submodels Competing causes for dropout

来源：评论

学校读者我要写书评

暂无评论

A Semiparametric Joint model for Longitudinal and Survival Data with Application to Hemodialysis Study

引用

BIOMETRICS 2009年第3期65卷 737-745页

作者： Li, Liang Hu, Bo Greene, Tom Cleveland Clin Dept Quantitat Hlth Sci Cleveland OH 44195 USA Univ Utah Div Epidemiol Dept Internal Med Salt Lake City UT 84148 USA

P>In many longitudinal clinical studies, the level and progression rate of repeatedly measured biomarkers on each subject quantify the severity of the disease and that subject's susceptibility to progression of the disease. It is of scientific and clinical interest to relate such quantities to a later time-to-event clinical endpoint such as patient survival. This is usually done with a shared parameter model. In such models, the longitudinal biomarker data and the survival outcome of each subject are assumed to be conditionally independent given subject-level severity or susceptibility (also called frailty in statistical terms). In this article, we study the case where the conditional distribution of longitudinal data is modeled by a linear mixed-effect model, and the conditional distribution of the survival data is given by a Cox proportional hazard model. We allow unknown regression coefficients and time-dependent covariates in both models. The proposed estimators are maximizers of an exact correction to the joint log likelihood with the frailties eliminated as nuisance parameters, an idea that originated from correction of covariate measurement error in measurement error models. The corrected joint log likelihood is shown to be asymptotically concave and leads to consistent and asymptotically normal estimators. Unlike most published methods for joint modeling, the proposed estimation procedure does not rely on distributional assumptions of the frailties. The proposed method was studied in simulations and applied to a data set from the Hemodialysis Study.

关键词： Corrected score Cox model Joint modeling Measurement error Regression spline shared parameter model

来源：评论

学校读者我要写书评

暂无评论

A two-part joint model for the analysis of survival and longitudinal binary data with excess zeros

引用

BIOMETRICS 2008年第2期64卷 611-619页

作者： Rizopoulos, Dimitris Verbeke, Geert Lesaffre, Emmanuel Vanrenterghem, Yves Catholic Univ Louvain Ctr Biostat UZ St Rafael B-3000 Louvain Belgium Univ Hosp Gasthuisberg Dept Nephrol Louvain Belgium

Many longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on patients with a renal allograft, in which interest lies in the association between longitudinal proteinuria (a dichotomous variable) measurements and the time to renal graft failure. An interesting feature of the sample at hand is that nearly half of the patients were never tested positive for proteinuria (>= 1g/day) during follow-up, which introduces a degenerate part in the random-effects density for the longitudinal process. In this article we propose a two-part shared parameter model framework that effectively takes this feature into account, and we investigate sensitivity to the various dependence structures used to describe the association between the longitudinal measurements of proteinuria and the time to renal graft failure.

关键词： copulas joint modeling sensitivity analysis shared parameter model

来源：评论

学校读者我要写书评

暂无评论

Nonignorable models for Intermittently Missing Categorical Longitudinal Responses

引用

BIOMETRICS 2010年第3期66卷 834-844页

作者： Tsonaka, Roula Rizopoulos, Dimitris Verbeke, Geert Lesaffre, Emmanuel Katholieke Univ Leuven Interuniv Inst Biostat & Stat Bioinformat B-3000 Louvain Belgium Erasmus MC Dept Biostat NL-3000 CA Rotterdam Netherlands

A class of nonignorable models is presented for handling nonmonotone missingness in categorical longitudinal responses. This class of models includes the traditional selection models and shared parameter models. This allows us to perform a broader than usual sensitivity analysis. In particular, instead of considering variations to a chosen nonignorable model, we study sensitivity between different missing data frameworks. An appealing feature of the developed class is that parameters with a marginal interpretation are obtained, while algebraically simple models are considered. Specifically, marginalized mixed-effects models (Heagerty, 1999, Biometrics 55, 688-698) are used for the longitudinal process that model separately the marginal mean and the correlation structure. For the correlation structure, random effects are introduced and their distribution is modeled either parametrically or non-parametrically to avoid potential misspecifications.

关键词： Categorical responses Constrained vertex-exchange method Marginalized models Non-parametric maximum likelihood Selection model shared parameter model

来源：评论

学校读者我要写书评

暂无评论

A COPULA APPROACH TO JOINT modelING OF LONGITUDINAL MEASUREMENTS AND SURVIVAL TIMES USING MONTE CARLO EXPECTATION-MAXIMIZATION WITH APPLICATION TO AIDS STUDIES

引用

JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2015年第5期25卷 1077-1099页

作者： Ganjali, M. Baghfalaki, T. Shahid Beheshti Univ Dept Stat Tehran Iran Tarbiat Modares Univ Dept Stat Tehran Iran Inst Res Fundamental Sci IPM Sch Biol Sci Tehran Iran

Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.

关键词： Copula models Expectation-maximization algorithm Longitudinal model Non-ignorability shared parameter model Time to event model

来源：评论

学校读者我要写书评

暂无评论

Joint models for predicting transplant-related mortality from quality of life data

引用

QUALITY OF LIFE RESEARCH 2015年第1期24卷 31-39页

作者： Terrin, Norma Rodday, Angie Mae Parsons, Susan K. Tufts Med Ctr Inst Clin Res & Hlth Policy Studies Boston MA 02111 USA Tufts Univ Tufts Clin & Translat Sci Inst Boston MA 02111 USA Tufts Univ Sch Med Dept Med Boston MA 02111 USA

To test whether longitudinally measured health-related quality of life (HRQL) predicts transplant-related mortality (TRM) in pediatric hematopoietic stem cell transplant (HSCT). The predictors of interest were emotional functioning, physical functioning, role functioning, and global HRQL, as rated by the parent about the child up to 6 times over 12 months of follow-up and measured by the Child Health Ratings Inventories. We used joint models, specifically shared parameter models, with time to TRM as the outcome of interest and other causes of mortality as a competing risk, via the JM software package in R. Choosing shared parameter models instead of standard survival models, such as Cox models with time-dependent covariates, enabled us to address measurement error in the HRQL trajectories and appropriately handle missing data. The nonlinear trajectories for each HRQL domain were modeled by random spline functions. The survival submodels were adjusted for baseline patient, family, and transplant characteristics. Hazard ratios per one-half standard deviation difference in emotional, physical, and role functioning, and global HRQL were 0.61 (95 % CI 0.46-0.81;p < 0.001), 0.70 (0.51-0.96;p = 0.03), 0.54 (0.34-0.85;p = 0.007), and 0.57 (0.41-0.79;p < 0.001), respectively. HRQL trajectories were predictive of TRM in pediatric HSCT, even after adjusting the survival outcome for baseline characteristics.

关键词： Hematopoietic stem cell transplant Health-related quality of life Joint model for longitudinal and time-to-event outcomes shared parameter model Transplant-related mortality Survival

来源：评论

学校读者我要写书评

暂无评论

Dynamic Predictions and Prospective Accuracy in Joint models for Longitudinal and Time-to-Event Data

引用

BIOMETRICS 2011年第3期67卷 819-829页

作者： Rizopoulos, Dimitris Erasmus MC Dept Biostat NL-3000 CA Rotterdam Netherlands

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. This type of research question has given rise to a rapidly developing field of biostatistics research that deals with the joint modeling of longitudinal and time-to-event data. In this article, we consider this modeling framework and focus particularly on the assessment of the predictive ability of the longitudinal marker for the time-to-event outcome. In particular, we start by presenting how survival probabilities can be estimated for future subjects based on their available longitudinal measurements and a fitted joint model. Following we derive accuracy measures under the joint modeling framework and assess how well the marker is capable of discriminating between subjects who experience the event within a medically meaningful time frame from subjects who do not. We illustrate our proposals on a real data set on human immunodeficiency virus infected patients for which we are interested in predicting the time-to-death using their longitudinal CD4 cell count measurements.

关键词： Area under the curve Discrimination ROC methodology shared parameter model Survival analysis Time-dependent covariates

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：