Unifying CRM and EWOC designs for phase I cancer clinical trials

The main goal of phase I cancer clinical trials is to determine the highest dose of a new therapy associated with an acceptable level of toxicity for the use in a subsequent phase II trial. The continual reassessment method (CRM) [O’Quigley, J., Pepe, M., Fisher, L., 1990. Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics 46, 33–48] and escalation with overdose control (EWOC) [Babb, J., Rogatko, A., Zacks, S., 1998. Cancer phase I clinical trials: efficient dose escalation with overdose control. Statist. Med. 17 (10), 1103–1120] are two model-based designs used for phase I cancer clinical trials. A few modifications of the (original) CRM and EWOC have been made by many authors. In this paper, we show how CRM and EWOC can be unified and present a hybrid design. We study the characteristics of the approach of the hybrid design. The comparisons of the three designs (CRM, EWOC, and the hybrid design) are presented by convergence rates and overdose proportions. The simulation results show that the hybrid design generally has faster convergence rates than EWOC and smaller overdose proportions than CRM, especially when the true maximum tolerated dose (MTD) is above the mid-level of the dose range considered. The performance of these three designs is also evaluated in terms of sensitivity to outliers.     

Statistical Properties of a Modified Accelerated Design for Phase I Cancer Clinical Trials

We consider the semiparametric profile likelihood inference for the distribution function under doubly censored data. For further developments of the statistical inference based on the profile likelihood ratio and alternative tools such as the score or Wald-type inference, we discuss the structures of the profile likelihood estimators and their derivatives included in the score function and the Fisher function of the profile likelihood, establishing the consistencies of their estimators.     

High-order extensions of the Double Chain Markov Model

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.     

步进频连续波体制生命迹象探测方法研究

基于步进频连续波,利用MTD滤波,通过时域、频域联合检测,研究了生命迹象探测的新方法,在检测到人体的同时,实现测距的目的。首先,基于人体微动模型建立步进频连续波生命迹象探测的回波模型;然后由回波得到距离像信息,划分距离门,经时域检测后,对有信号的距离门内距离峰值变化信息进行傅里叶变换;再通过MTD滤波,滤除静止目标的影响,经频域检测,即可检测到人体。仿真证明了该方法的有效性。     

An EM algorithm for estimation in the mixture transition distribution model

The mixture transition distribution (MTD) model was introduced by Raftery to face the need for parsimony in the modeling of high-order Markov chains in discrete time. The particularity of this model comes from the fact that the effect of each lag upon the present is considered separately and additively, so that the number of parameters required is drastically reduced. However, the efficiency for the MTD parameter estimations proposed up to date still remains problematic on account of the large number of constraints on the parameters. In this article, an iterative procedure, commonly known as expectation–maximization (EM) algorithm, is developed cooperating with the principle of maximum likelihood estimation (MLE) to estimate the MTD parameters. Some applications of modeling MTD show the proposed EM algorithm is easier to be used than the algorithm developed by Berchtold. Moreover, the EM estimations of parameters for high-order MTD models led on DNA sequences outperform the corresponding fully parametrized Markov chain in terms of Bayesian information criterion. A software implementation of our algorithm is available in the library seq++at http://stat.genopole.cnrs.fr/seqpp.     

最陡下降算法在MTD滤波器设计中的应用

针对雷达中动目标检测技术中滤波器组设计,从普遍采用基于最小最大化的优化原则出发提出了一种改进优化原则。该方法具有明确物理含义,即是使输出杂波功率最小,从而使得优化问题简化为最小化目标函数,大大简化了MTD滤波器设计中优化问题的求解难度。文中采用最陡下降算法求解该问题,通过计算机仿真,可以看到该方法与原来采用方法具有相似的结果,为工程应用提供了一种简单、实用的方法。