Treatment design selection effects on parameter estimation in dynamic logistic models for longitudinal binary data

In a longitudinal set-up, to examine the effects of certain fixed covariates on the repeated binary responses, there exists an approach to model the binary probabilities through a dynamic logistic relationship. In some practical situations such as in longitudinal clinical studies, it may happen that some of the covariates such as treatments are selected randomly following an adaptive design, whereas the rest of the covariates may be fixed by nature. The purpose of this study is to examine the effects of the design weights selection on the parameter estimation including the treatment effects, after taking the longitudinal correlations of the repeated binary responses into account.     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

Generation of pseudo random numbers and estimation under Cox models with time-dependent covariates

ABSTRACT

We study the method for generating pseudo random numbers under various special cases of the Cox model with time-dependent covariates when the baseline hazard function may not be constant and the random variable may equal infinity with a positive probability. During our simulation studies in computing the partial likelihood estimates, in between 3% and 20% of the time with a moderate sample size, it happens that the partial likelihood estimate of the regression coefficient is ∞ for the data from the Cox model. We propose a semi-parametric estimator as a modification for such a case. We present simulation results on the asymptotic properties of the semi-parametric estimator.     

实时/时变路网环境下城市出救点选择与救援车辆路径的集成动态优化

为了提升城市突发公共事件应急资源调配效率,考虑实时/时变路网环境下出救点选择与救援车辆路径的集成优化问题(CERFSVRP),设计了一种实时/时变交通信息的结合策略,并提出了满足先进先出原则的路段行驶时间计算方法.在此基础上,综合考虑出救点选择、供应能力以及车辆路径连续性等约束条件,以应急响应时间最短为目标,基于虚拟出救点概念和滚动时域策略建立了CERFSVRP动态优化模型.针对该模型的特点,设计了一种改进遗传算法和线性规划法相结合的两阶段算法.算例分析结果表明该模型和算法可以有效解决实时/时变路网环境下城市突发公共事件CERFSVRP动态优化问题.     

Dynamic Prediction by Landmarking in Event History Analysis

Abstract.  This article advocates the landmarking approach that dynamically adjusts predictive models for survival data during the follow up. This updating is achieved by directly fitting models for the individuals still at risk at the landmark point. Using this approach, simple proportional hazards models are able to catch the development over time for models with time-varying effects of covariates or data with time-dependent covariates (biomarkers). To smooth the effect of the landmarking, sequences of models are considered with parametric effects of the landmark time point and fitted by maximizing appropriate pseudo log-likelihoods that extend the partial log-likelihood to cover the landmarking approach.     

离心突然近似下原子碰撞分子振转激发的含时波包公式

把离心突然近似应用于原子碰撞分子的振转激发过程,从而大大减少了耦合微分方程组的空间维数,通过使用含时波包方法,将求解耦合微分方程组问题化成求解入射初值问题,对单个初始波包的一次计算即可获得一个能量范围内的态-态跃迁几率.     

Joint modeling of censored longitudinal and event time data

Censoring of a longitudinal outcome often occurs when data are collected in a biomedical study and where the interest is in the survival and or longitudinal experiences of a study population. In the setting considered herein, we encountered upper and lower censored data as the result of restrictions imposed on measurements from a kinetic model producing “biologically implausible” kidney clearances. The goal of this paper is to outline the use of a joint model to determine the association between a censored longitudinal outcome and a time to event endpoint. This paper extends Guo and Carlin's [6] paper to accommodate censored longitudinal data, in a commercially available software platform, by linking a mixed effects Tobit model to a suitable parametric survival distribution. Our simulation results showed that our joint Tobit model outperforms a joint model made up of the more naïve or “fill-in” method for the longitudinal component. In this case, the upper and/or lower limits of censoring are replaced by the limit of detection. We illustrated the use of this approach with example data from the hemodialysis (HEMO) study [3] and examined the association between doubly censored kidney clearance values and survival.     

Multistage Modeling of Lung Cancer Mortality Among Arsenic-Exposed Copper-Smelter Workers

Multistage modeling incorporating a time-dependent exposure pattern is applied to lung cancer mortality data obtained from a cohort of 2802 arsenic-exposed copper-smelter workers who worked 1 or more years during the period 1940-1964 at a copper smelter at Tacoma, Washington. The workers were followed for death through 1976. There were 100 deaths due to lung cancer during the follow-up period. Exposures to air arsenic levels measured in micrograms/m3 were estimated from departmental air arsenic and workers urinary arsenic measurements. Relationships of different temporal variables with excess death rates are examined to judge qualitatively the implications of the multistage cancer process. Analysis to date indicates a late stage effect of arsenic although an additional early stage effect cannot be ruled out.     

时间相依利率扩散模型的非参数估计

利率的变动模式会随着时间的推移、经济环境的变化和金融制度的改变而发生变化.时变的扩散模型能更好的描述短期利率的随机行为,文章采用基于核回归的非参数方法,估计中国银行间市场7天回购利率的时间相依CKLS模型.最后比较了时间相依CKLS模型与时齐CKLS模型在波动率预报上的表现,结果表明,时间相依CKLS模型更好的反映了利率实时的变化,提高了预测利率变化的精度.     

A Stochastic Two-Stage Model for Cancer Risk Assessment. II. The Number and Size of Premalignant Clones

Mathematical expressions are derived, under different dosing patterns, for the number and size of premalignant clones within the framework of a two-mutation model for carcinogenesis, which has previously been shown to be consistent with a large body of epidemiologic and experimental data.