Semiparametric additive intensity model with frailty for recurrent events

The seminal Cox’s proportional intensity model with multiplicative frailty is a popular approach to analyzing the frequently encountered recurrent event data in scientific studies. In the case of violating the proportional intensity assumption, the additive intensity model is a useful alternative. Both the additive and proportional intensity models provide two principal frameworks for studying the association between the risk factors and the disease recurrences. However, methodology development on the additive intensity model with frailty is lacking, although would be valuable. In this paper, we propose an additive intensity model with additive frailty to formulate the effects of possibly time-dependent covariates on recurrent events as well as to evaluate the intra-class dependence within recurrent events which is captured by the frailty variable. The asymptotic properties for both the regression parameters and the association parameters in frailty distribution are established. Furthermore, we also investigate the large-sample properties of the estimator for the cumulative baseline intensity function.     

伽玛刀联合紫杉醇治疗高级别胶质瘤的临床研究

目的 探讨伽玛刀联合紫杉醇治疗术后复发的高级别胶质瘤的临床疗效及不良反应。方法 将73 例胶质瘤术后 患者分为联合治疗组（35 例）和伽玛刀组（38 例）。联合治疗组在伽玛刀治疗前2h 静脉滴注紫杉醇100mg/m2,伽玛刀组仅行伽玛刀治疗。所有患者伽玛刀治疗方案均采用50%剂量曲线包绕病灶,病灶中心剂量28~32Gy,周边剂量14~16Gy。结果 联合治疗组与伽玛刀组近期有效率分别为37.1%和39.5%,无统计学差异（P=0.838）;近期疾病控制率分别为97.1%和81.6%,两组间比较有统计学差异（P=0.041）;生活质量改善率分别为42.9%和42.1%,无统计学差异（P=0.948）;两组中改善及稳定的患者比例分别为97.1%和76.3%（P=0.010）;无进展生存率分别为57.1%和34.2%,两组间比较有统计学差异（P=0.049）;总生存率分别为17.1%和13.2%,组间比较无统计学差异（P=0.634）。治疗不良反应发生率两组比较无统计学差异（P=0.258）。结论 伽玛刀联合紫杉醇治疗能提高术后复发高级别胶质瘤患者的近期疾病控制率,提高临床症状稳定改善率,并能延长患者无进展生存期,改善生活质量。     

Dynamics of immunotherapy antitumor models with impulsive control strategy

In this paper, using the methods of killing tumors and impulsive differential equations, two immunotherapy antitumor models for describing therapies of general tumors and advanced solid tumors are established. By using the theories of impulsive equations, small amplitude perturbation techniques, and the comparison technique, we obtain the conditions which guarantee the global asymptotical stability of the tumor-eliminated periodic solution and system permanence, when immunotherapy alone is performed. The numerical results of the influences of the impulsive perturbation on the inherent oscillation show rich dynamics, such as period-doubling bifurcation and chaos. Moreover, the effects of the combination of radiotherapy with immunotherapy on antitumor are obtained, including the threshold value of stability conditions of tumor-eradication periodic solution when the mixed combination treatment of immunotherapy and radiotherapy is performed. Some numerical simulations for the effects of the timing of radiotherapy application and the timing of injection T cells on the threshold value are performed. Finally, we present some theoretical methods for suppressing the growth of tumors.     

Smoothing estimation of rate function for recurrent event data with informative censoring

This paper proposes kernel estimation of the occurrence rate function for recurrent event data with informative censoring. An informative censoring model is considered with assumptions made on the joint distribution of the recurrent event process and the censoring time without modeling the censoring distribution. Under the validity of the informative censoring model, we also show that an estimator based on the assumption of independent censoring becomes inappropriate and is generally asymptotically biased. To investigate the asymptotic properties of the proposed estimator, the explicit form of its asymptotic mean squared risk and the asymptotic normality are derived. Meanwhile, the empirical consistent smoothing estimator for the variance function of the estimator is suggested. The performance of the estimators are also studied through Monte Carlo simulations. An epidemiological example of intravenous drug user data is used to show the influence of informative censoring in the estimation of the occurrence rate functions for inpatient cares over time.     

Exponential stability criteria for discrete-time recurrent neural networks with time-varying delay

In this paper, the robust global exponential stability is investigated for the discrete-time recurrent neural networks (RNNs) with time-varying interval delay. By choosing an augmented Lyapunov–Krasovskii functional, delay-dependent results guaranteeing the global exponential stability and the robust exponential stability of the concerned neural network are obtained. The results are shown to be a generalization of some previous results, and less conservative than the existing works. Two numerical examples are given to demonstrate the applicability of the proposed method.     

Scheduling preparation of doses for a chemotherapy service

A fast realization of drugs is an important part in the quality of service of a hospital. In this paper we propose a scheduling method for the preparation of chemotherapy doses in order to reduce the patient waiting time. Two approaches have been defined: an off-line approach and a real time approach. The off-line approach is using a linear programming model for minimizing the maximum tardiness of jobs in a production day. This method is re-used during the real-time resolution combined with a greedy algorithm. The solution obtained respects constraints on the production center and the hospital organization. Our model is currently used in software which helps the decision maker of the service and allows increasing the patient satisfaction and the productivity of the service.     

New results on input-to-state convergence for recurrent neural networks with variable inputs

In this paper, we study a class of recurrent neural networks (RNNs) arising from optimization problems. By constructing appropriate Lyapunov functions, we prove two new results on input-to-state convergence of RNNs with variable inputs. Numerical simulations are also given to demonstrate the convergence of the solutions.     

Ergodic theorem for generalized long-range exclusion processes with positive recurrent transition probabilities

This paper is devoted to the ergodicity of generalized long-range exclusion processes with positive recurrent transition probabilities. The set of invariant probability measures and the corresponding domain of attraction for each invariant probability measure are described. This paper is partially supported the National Natural Science Foundation of China.     

A class of box-cox transformation models for recurrent event data with a terminal event

In this article, we study a class of Box-Cox transformation models for recurrent event data in the presence of terminal event, which includes the proportional means models as special cases. Estimating equation approaches and the inverse probability weighting technique are used for estimation of the regression parameters. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed methods is examined through simulation studies, and an application to a heart failure study is presented to illustrate the proposed method.