基于《国际功能、残疾和健康分类》的居家脊髓损伤患者功能变化的纵向研究

目的:以移动APP“脊髓损伤患者远程延续护理系统”中的《国际功能、残疾和健康分类》(ICF)相关随访指标为结局评定指标,纵向探讨脊髓损伤患者出院后功能状态的变化趋势.方法:2018年6月-2019年9月,选取广州市、十堰市和成都市4家医院的6个临床科室的符合纳入排除标准的91例脊髓损伤患者,随访护士以APP中的32个随...     

术中超声辅助下环形减压术治疗多节段胸椎后纵韧带骨化症

目的：分析多节段胸椎后纵韧带骨化症(ossification of the posterior longitudinal ligament, OPLL)术中超声辅助下环形减压术的手术疗效和术后神经功能改善情况。方法：选择2016年1月至2021年1月北京大学第三医院多节段胸椎OPLL患者的病例资料进行回顾性分析,所有病例均完成后壁切除后行术中超声检查确定环形减压节段,并进行环形减压。纳入研究的30例患者男性14例,女性16例,平均年龄(49.3±11.4)岁。首发症状以下肢麻木无力为主(83.3%),平均症状持续时间为(33.9±42.9)个月(1～168个月)。神经功能通过术前及末次随访时改良日本骨科协会(modified Japanese Orthopedic Association, mJOA)评分(0～11分)评估,神经功能改善率根据Harabayashi法计算。根据神经功能改善率是否大于25%将患者分为较优改善组和较差改善组,收集两组患者的年龄、体重指数(body mass index, BMI)、病程时间、手术时间、出血量、mJOA评分、手术节段、脑脊液漏并进行分析比较。结...     

An autoregressive linear mixed effects model for the analysis of unequally spaced longitudinal data with dose-modification

The assessment of the dose-response relationship is important but not straightforward when the therapeutic agent is administered repeatedly with dose-modification in each patient and a continuous response is measured repeatedly. We recently proposed an autoregressive linear mixed effects model for such data in which the current response is regressed on the previous response, fixed effects, and random effects. The model represents profiles approaching each patient's asymptote, takes into account the past dose history, and provides a dose-response relationship of the asymptote as a summary measure. In an autoregressive model, intermittent missing data mean the missing values in previous responses as covariates. We previously provided the marginal (unconditional on the previous response) form of the proposed model to deal with intermittent missing data. Irregular timings of dose-modification or measurement can also be treated as equally spaced data with intermittent missing values by selecting an adequately small unit of time. The likelihood is, however, expressed by matrices whose sizes depend on the number of observations for a patient, and the computational burden is large. In this study, we propose a state space form of the autoregressive linear mixed effects model to calculate the marginal likelihood without using large matrices. The regression coefficients of the fixed effects can be concentrated out of the likelihood in this model by the same way of a linear mixed effects model. As an illustration of the approach, we analyzed immunologic data from a clinical trial for multiple sclerosis patients and estimated the dose-response curves for each patient and the population mean.     

A robust approach to t linear mixed models applied to multiple sclerosis data

We discuss a robust extension of linear mixed models based on the multivariate t distribution. Since longitudinal data are successively collected over time and typically tend to be auto-correlated, we employ a parsimonious first-order autoregressive dependence structure for the within-subject errors. A score test statistic for testing the existence of autocorrelation among the within-subject errors is derived. Moreover, we develop an explicit scoring procedure for the maximum likelihood estimation with standard errors as a by-product. The technique for predicting future responses of a subject given past measurements is also investigated. Results are illustrated with real data from a multiple sclerosis clinical trial.     

Joint analysis of multiple longitudinal outcomes: application of a latent class model

We address the problem of joint analysis of more than one series of longitudinal measurements. The typical way of approaching this problem is as a joint mixed effects model for the two outcomes. Apart from the large number of parameters needed to specify such a model, perhaps the biggest drawback of this approach is the difficulty in interpreting the results of the model, particularly when the main interest is in the relation between the two longitudinal outcomes. Here we propose an alternative approach to this problem. We use a latent class joint model for the longitudinal outcomes in order to reduce the dimensionality of the problem. We then use a two-stage estimation procedure to estimate the parameters in this model. In the first stage, the latent classes, their probabilities and the mean and covariance structure are estimated based on the longitudinal data of the first outcome. In the second stage, we study the relation between the latent classes and patient characteristics and the other outcome(s). We apply the method to data from 195 consecutive lung cancer patients in two outpatient clinics of lung diseases in The Hague, and we study the relation between denial and longitudinal health measures. Our approach clearly revealed an interesting phenomenon: although no difference between classes could be detected for objective measures of health, patients in classes representing higher levels of denial consistently scored significantly higher in subjective measures of health. Copyright (c) 2008 John Wiley & Sons, Ltd.     

Gaussianization-based quasi-imputation and expansion strategies for incomplete correlated binary responses

New quasi-imputation and expansion strategies for correlated binary responses are proposed by borrowing ideas from random number generation. The core idea is to convert correlated binary outcomes to multivariate normal outcomes in a sensible way so that re-conversion to the binary scale, after performing multiple imputation, yields the original specified marginal expectations and correlations. This conversion process ensures that the correlations are transformed reasonably which in turn allows us to take advantage of well-developed imputation techniques for Gaussian outcomes. We use the phrase 'quasi' because the original observations are not guaranteed to be preserved. We argue that if the inferential goals are well-defined, it is not necessary to strictly adhere to the established definition of multiple imputation. Our expansion scheme employs a similar strategy where imputation is used as an intermediate step. It leads to proportionally inflated observed patterns, forcing the data set to a complete rectangular format. The plausibility of the proposed methodology is examined by applying it to a wide range of simulated data sets that reflect alternative assumptions on complete data populations and missing-data mechanisms. We also present an application using a data set from obesity research. We conclude that the proposed method is a promising tool for handling incomplete longitudinal or clustered binary outcomes under ignorable non-response mechanisms.     

HIV viral dynamic models with dropouts and missing covariates

In recent years HIV viral dynamic models have received great attention in AIDS studies. Often, subjects in these studies may drop out for various reasons such as drug intolerance or drug resistance, and covariates may also contain missing data. Statistical analyses ignoring informative dropouts and missing covariates may lead to misleading results. We consider appropriate methods for HIV viral dynamic models with informative dropouts and missing covariates and evaluate these methods via simulations. A real data set is analysed, and the results show that the initial viral decay rate, which may reflect the efficacy of the anti-HIV treatment, may be over-estimated if dropout patients are ignored. We also find that the current or immediate previous viral load values may be most predictive for patients' dropout. These results may be important for HIV/AIDS studies.