Choice of Parametric Accelerated Life and Proportional Hazards Models for Survival Data: Asymptotic Results

We discuss the impact of misspecifying fully parametric proportional hazards and accelerated life models. For the uncensored case, misspecified accelerated life models give asymptotically unbiased estimates of covariate effect, but the shape and scale parameters depend on the misspecification. The covariate, shape and scale parameters differ in the censored case. Parametric proportional hazards models do not have a sound justification for general use: estimates from misspecified models can be very biased, and misleading results for the shape of the hazard function can arise. Misspecified survival functions are more biased at the extremes than the centre. Asymptotic and first order results are compared. If a model is misspecified, the size of Wald tests will be underestimated. Use of the sandwich estimator of standard error gives tests of the correct size, but misspecification leads to a loss of power. Accelerated life models are more robust to misspecification because of their log-linear form. In preliminary data analysis, practitioners should investigate proportional hazards and accelerated life models; software is readily available for several such models.     

Marginal Regression of Gaps Between Recurrent Events

Recurrent event data typically exhibit the phenomenon of intra-individual correlation, owing to not only observed covariates but also random effects. In many applications, the population may be reasonably postulated as a heterogeneous mixture of individual renewal processes, and the inference of interest is the effect of individual-level covariates. In this article, we suggest and investigate a marginal proportional hazards model for gaps between recurrent events. A connection is established between observed gap times and clustered survival data with informative cluster size. We subsequently construct a novel and general inference procedure for the latter, based on a functional formulation of standard Cox regression. Large-sample theory is established for the proposed estimators. Numerical studies demonstrate that the procedure performs well with practical sample sizes. Application to the well-known bladder tumor data is given as an illustration.     

The exact solution to the mle of a decreasing density based on arbitrarily right censored data

Nonparametric maximum likelihood estimation of decreasing and unimodal density functions, based on observations subject to arbitrary right censorship, was considered by McNichols and Padgett(1982). In order to compute their estimators, however, nonlinear equations with linear constraints had to be maximized using numerical techniques. The exact solution to this problem can now be found. An example illustrates the simplicity of the method.     

文学艺术在地球生态系统中的序位--《生态文艺学》论稿之一

地球生态系统是一个有机、有序、运动着的“活体” ;地球生态系统大体包括自然生态系统、社会生态系统、精神生态系统 ;文学艺术活动是生存的最为辉煌灿烂的景观     

Penalized spline joint models for longitudinal and time-to-event data

The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.     

清朝咸同年间滇东南回民捍卫民族生存斗争的性质辨析——对马德新、马如龙的重新评价

清代咸同年间爆发的滇东南回民反对清政府的抗暴自卫斗争,具有维护民族生存自立的性质,与滇西杜文秀领导的各族反清大起义不同,故对其领导人马德新、马如龙不能以简单的投降、叛徒论之,而应给予客观评价。     

全国新农村建设综合评价研究

针对我国各地新农村建设情况，提出一种有效的评价方法。从生产发展、生活保障、文明与环境这3个方面，建立全国新农村建设综合评价指标体系。以31个省、市、自治区为例，运用层次分析法建立综合评价模型，经过运算，确定各地新农村建设的综合发展状况，并给出具体分析。     

Improving the Efficiency of the Nelson–Aalen Estimator: the Naive Local Constant Estimator

Abstract.  The Nelson–Aalen estimator is well known to be an asymptotically efficient estimator of the cumulative hazard function, see Andersen et al. ( Statistical models based on counting processes , Springer-Verlag, New York, 1993) among many others. In this paper, we show that the efficiency of the Nelson–Aalen estimator can be considerably improved by using more information in the estimation process than the traditional Nelson–Aalen estimator uses. While our approach results in a biased estimator, the variance improvement is substantial. By optimizing the balance between the bias loss and the variance improvement, we obtain results on the efficiency gain. Several examples for known failure time distributions are used to illustrate these ideas.     

大麦籽粒灌浆特性的研究

本文通过对十二个大麦品种籽粒灌浆“S”曲线的描绘,发现决定粒重的关键时期是在开花后22～37天,此期积累的物质占最大粒重的60%以上,单粒体积一般在开花后37天前后达到最大值。籽粒含水量在开花台47天就迅速降到20%左右。     

The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables

In this paper the Jackknife estimate of covariance of two Kaplan–Meier integrals with covariates is introduced. Its strong consistency is established under mild conditions. Several applications of the estimator are discussed.