高校学报改革断想

高校学报要实现从"全、散、小、弱"向"专、特、大、强"转型,必须从实际出发,客观认识学报存在的问题,既要有历史眼光又要与时俱进,不断改革创新。要对学报改革进行具体分析,改变目前的学术评价机制,注重培养一批优秀学报编辑。     

Fitting semiparametric accelerated failure time models for nested case–control data

A nested case–control (NCC) study is an efficient cohort-sampling design in which a subset of controls are sampled from the risk set at each event time. Since covariate measurements are taken only for the sampled subjects, time and efforts of conducting a full scale cohort study can be saved. In this paper, we consider fitting a semiparametric accelerated failure time model to failure time data from a NCC study. We propose to employ an efficient induced smoothing procedure for rank-based estimating method for regression parameters estimation. For variance estimation, we propose to use an efficient resampling method that utilizes the robust sandwich form. We extend our proposed methods to a generalized NCC study that allows a sampling of cases. Finite sample properties of the proposed estimators are investigated via an extensive stimulation study. An application to a tumor study illustrates the utility of the proposed method in routine data analysis.     

“民国文学”概念的提出及民国旧体文学研究现状

一部客观完整的＂民国文学史＂既需要关注＂新文学＂,也需要关注＂旧文学＂,对近年来＂民国文学＂概念的提出过程进行梳理,对民国时期作为古代文学延续的旧体文学如诗词、戏剧、小说等的研究现状和前景进行分析,都将有利于我国文学研究的全面深入开展和繁荣。     

The Poisson–exponential distribution: a Bayesian approach

In this paper, we proposed a new two-parameter lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk scenario. The properties of the proposed distribution are discussed, including a formal proof of its density function and an explicit algebraic formulae for its quantiles and survival and hazard functions. Also, we have discussed inference aspects of the model proposed via Bayesian inference by using Markov chain Monte Carlo simulation. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumptions of non-informative priors. Further, some discussions on models selection criteria are given. The developed methodology is illustrated on a real data set.     

Partial logistic relevance vector machines in survival analysis

The use of relevance vector machines to flexibly model hazard rate functions is explored. This technique is adapted to survival analysis problems through the partial logistic approach. The method exploits the Bayesian automatic relevance determination procedure to obtain sparse solutions and it incorporates the flexibility of kernel-based models. Example results are presented on literature data from a head-and-neck cancer survival study using Gaussian and spline kernels. Sensitivity analysis is conducted to assess the influence of hyperprior distribution parameters. The proposed method is then contrasted with other flexible hazard regression methods, in particular the HARE model proposed by Kooperberg et al. [16]. A simulation study is conducted to carry out the comparison. The model developed in this paper exhibited good performance in the prediction of hazard rate. The application of this sparse Bayesian technique to a real cancer data set demonstrated that the proposed method can potentially reveal characteristics of the hazards, associated with the dynamics of the studied diseases, which may be missed by existing modeling approaches based on different perspectives on the bias vs. variance balance.     

Progression-free survival in oncological clinical studies: Assessment time bias and methods for its correction

Progression-free survival (PFS) is a frequently used endpoint in oncological clinical studies. In case of PFS, potential events are progression and death. Progressions are usually observed delayed as they can be diagnosed not before the next study visit. For this reason potential bias of treatment effect estimates for progression-free survival is a concern. In randomized trials and for relative treatment effects measures like hazard ratios, bias-correcting methods are not necessarily required or have been proposed before. However, less is known on cross-trial comparisons of absolute outcome measures like median survival times. This paper proposes a new method for correcting the assessment time bias of progression-free survival estimates to allow a fair cross-trial comparison of median PFS. Using median PFS for example, the presented method approximates the unknown posterior distribution by a Bayesian approach based on simulations. It is shown that the proposed method leads to a substantial reduction of bias as compared to estimates derived from maximum likelihood or Kaplan–Meier estimates. Bias could be reduced by more than 90% over a broad range of considered situations differing in assessment times and underlying distributions. By coverage probabilities of at least 94% based on the credibility interval of the posterior distribution the resulting parameters hold common confidence levels. In summary, the proposed approach is shown to be useful for a cross-trial comparison of median PFS.     

Dose intra-subject escalation to an event (DIETE): A new method for phase 1 dose-finding utilizing systematic intra-subject dose escalation with application to T-cell engagers

T-cell engagers are a class of oncology drugs which engage T-cells to initiate immune response against malignant cells. T-cell engagers have features that are unlike prior classes of oncology drugs (e.g., chemotherapies or targeted therapies), because (1) starting dose level often must be conservative due to immune-related side effects such as cytokine release syndrome (CRS); (2) dose level can usually be safely titrated higher as a result of subject's immune system adaptation after first exposure to lower dose; and (3) due to preventive management of CRS, these safety events rarely worsen to become dose limiting toxicities (DLTs). It is generally believed that for T-cell engagers the dose intensity of the starting dose and the peak dose intensity both correlate with improved efficacy. Existing dose finding methodologies are not designed to efficiently identify both the initial starting dose and peak dose intensity in a single trial. In this study, we propose a new trial design, dose intra-subject escalation to an event (DIETE) design, that can (1) estimate the maximum tolerated initial dose level (MTD1); and (2) incorporate systematic intra-subject dose-escalation to estimate the maximum tolerated dose level subsequent to adaptation induced by the initial dose level (MTD2) with a survival analysis approach. We compare our framework to similar methodologies and evaluate their key operating characteristics.     

An investigation into the two‐stage meta‐analytic copula modelling approach for evaluating time‐to‐event surrogate endpoints which comprise of one or more events of interest

Clinical trials of experimental treatments must be designed with primary endpoints that directly measure clinical benefit for patients. In many disease areas, the recognised gold standard primary endpoint can take many years to mature, leading to challenges in the conduct and quality of clinical studies. There is increasing interest in using shorter‐term surrogate endpoints as substitutes for costly long‐term clinical trial endpoints; such surrogates need to be selected according to biological plausibility, as well as the ability to reliably predict the unobserved treatment effect on the long‐term endpoint. A number of statistical methods to evaluate this prediction have been proposed; this paper uses a simulation study to explore one such method in the context of time‐to‐event surrogates for a time‐to‐event true endpoint. This two‐stage meta‐analytic copula method has been extensively studied for time‐to‐event surrogate endpoints with one event of interest, but thus far has not been explored for the assessment of surrogates which have multiple events of interest, such as those incorporating information directly from the true clinical endpoint. We assess the sensitivity of the method to various factors including strength of association between endpoints, the quantity of data available, and the effect of censoring. In particular, we consider scenarios where there exist very little data on which to assess surrogacy. Results show that the two‐stage meta‐analytic copula method performs well under certain circumstances and could be considered useful in practice, but demonstrates limitations that may prevent universal use.     

Bidirectional Associations Between Parental Responsiveness and Executive Function During Early Childhood

In this study, we examined bidirectional associations between parental responsiveness and executive function (EF) processes in socioeconomically disadvantaged preschoolers. Participants were 534 3‐ to 5‐year‐old children (71 percent Hispanic/Latino; 28 percent African American; 1 percent European American) attending Head Start programs. At Time 1 (T1) and 6.5 months later at Time 2 (T2), parents and children participated in a videotaped free play session and children completed delay inhibition (gift delay‐wrap, gift delay‐bow) and conflict EF (bear/dragon, dimensional change card sort) tasks. Parental warm acceptance, contingent responsiveness, and verbal scaffolding were coded from the free play videos and aggregated to create a parental responsiveness latent variable. A cross‐lagged panel structural equation model indicated that higher T1 parental responsiveness significantly predicted more positive gain in delay inhibition and conflict EF from T1 to T2. Higher T1 delay inhibition, but not T1 conflict EF, significantly predicted more positive change in parental responsiveness from T1 to T2. These associations were not explained by several possible confounding variables, including children's age, gender, race/ethnicity, and verbal ability. Findings suggest that parental responsiveness may support EF development in disadvantaged children, with reciprocal effects of delay inhibition on parental responsiveness.     

基于生存分析法的医疗电子病历系统采纳与扩散研究

医疗电子病历系统作为中国医疗信息化建设的核心,关注其采纳与扩散机理,对推进医疗卫生信息化建设,以及实现有意义地使用具有重要的理论和实践意义。基于云南省222家医院信息化建设的调研数据,运用半参数生存分析法即Cox回归模型探讨医院采纳电子病历系统的影响因素及扩散机理。研究表明,教学状态、医院规模和建院时间均为有利因素,即这三个因素积极促进医院采纳电子病历系统;时间×规模为不利因素,即它对医院采纳电子病历系统有负向影响;地理位置在分层和删减样本的模型分析中均表现为不利因素;医院等级影响并不明显。