替吉奥与卡培他滨联合奥沙利铂治疗晚期胃癌效果对比分析

目的分析比较替吉奥联合奥沙利铂与卡培他滨联合奥沙利铂治疗术后复发转移性胃癌的效果。方法 2010年5月至2011年5月收治26例术后复发转移晚期胃癌患者,观察组12例予奥沙利铂30mg/m2,第1天,静脉滴注;替吉奥按体表面积选择40mg~60mg/次,2次/日,口服,第1~14天。对照组予奥沙利铂30mg/m2,第1天,静脉滴注;卡培他滨1500mg/次,2次/日,口服,第1~14天。以上均为21天为1周期,每2周期评价疗效,并随访病情进展。结果 26例病例均可评价;观察组和对照组的近期有效率(RR)分别为50%和42·8%,两组比较差异无显著性,但治疗组的不良反应及严重反应发生率均明显低于对照组。结论替吉奥治疗术后复发转移性胃癌的疗效较好,不良反应轻,值得临床进一步研究。     

Analysis of supersaturated designs via the Dantzig selector

A supersaturated design is a design whose run size is not enough for estimating all the main effects. It is commonly used in screening experiments, where the goals are to identify sparse and dominant active factors with low cost. In this paper, we study a variable selection method via the Dantzig selector, proposed by Candes and Tao [2007. The Dantzig selector: statistical estimation when p$p$ is much larger than n$n$. Annals of Statistics 35, 2313–2351], to screen important effects. A graphical procedure and an automated procedure are suggested to accompany with the method. Simulation shows that this method performs well compared to existing methods in the literature and is more efficient at estimating the model size.     

Semiparametric estimation for count data through weighted distributions

This paper is concerned with semiparametric discrete kernel estimators when the unknown count distribution can be considered to have a general weighted Poisson form. The estimator is constructed by multiplying the Poisson estimate with a nonparametric discrete kernel-type estimate of the Poisson weight function. Comparisons are then carried out with the ordinary discrete kernel probability mass function estimators. The Poisson weight function is thus a local multiplicative correction factor, and is considered as the uniform measure to detect departures from the equidispersed Poisson distribution. In this way, the effects of dispersion and zero-proportion with respect to the standard Poisson distribution are also minimized. This method of estimation is also applied to the weighted binomial form for the count distribution having a finite support. The proposed estimators, in addition to being simple, easy-to-implement and effective, also outperform the competing nonparametric and parametric estimators in finite-sample situations. Two examples illustrate this new semiparametric estimation.     

Designing two factor experiments for selecting interactions

This paper considers the problem of devising a single stage procedure for selecting the treatment combination associated with the largest interaction in a two-factor rfx× c experiment having independent normally distributed observations with common known variance. The intuitive procedure based on the best linear unbiased estimators of the population interactions is employed. Initially an indifference zone formulation is used; the problem of determining the least favorable configuration is reduced to a nonlinear programming problem with log concave objective function and a convex polytope as feasible region. A solution technique is introduced in the context of an illustrative example. The problem is also considered using a preferred population formulation; this approach requires a strengthened version of the indifference zone probability requirement. It is shown that the same sample size guarantees this requirement as does the earlier one.     

A lower-bound oracle inequality for a blockwise-shrinkage estimate

Efromovich-Pinsker and Stein blockwise-shrinkage estimates are traditionally studied via upper-bound oracle inequalities, which bound the estimate's risk from above by the oracle's risk plus a remainder term. These bounds allow one to establish sufficient conditions for attaining the oracle's risk. To explore necessary conditions, this article develops a lower-bound oracle inequality, which bounds the estimate's risk from below by the oracle's risk minus a remainder term. In particular, the lower bound implies that thresholds must vanish for attaining the oracle's risk.     

Adaptive wavelet estimation of a density from mixtures under multiplicative censoring

In this paper, a mixture model under multiplicative censoring is considered. We investigate the estimation of a component of the mixture (a density) from the observations. A new adaptive estimator based on wavelets and a hard thresholding rule is constructed for this problem. Under mild assumptions on the model, we study its asymptotic properties by determining an upper bound of the mean integrated squared error over a wide range of Besov balls. We prove that the obtained upper bound is sharp.