原发性三叉神经痛两种治疗方法的比较

原发性三叉神经痛的治疗方法以神经毁损术最为常用。通过对经皮射频热凝毁损术和阿霉素化学毁损术的疗效、安全性、复发率、并发症及费用等方面的比较,揭示两种方法的利弊。用比较治疗学的理念及方法来指导三叉神经痛治疗的临床决策,以期能在目前条件下提高患者的治疗效果、降低医疗费用及提高生活质量。     

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing-information identity (Louis, 1982, Journal of the Royal Statistical Society, Series B, 44, 226). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed.     

右美托咪定对老年左布比卡因腋路臂丛神经阻滞的影响

观察右美托咪定对老年左布比卡因腋路臂丛神经阻滞的影响。50例择期行腋路臂丛神经阻滞的老年患者随机分为两组,右美托咪定组（LD组,25例）给予0．5％左布比卡因复合右美托咪定0．5μg·kg-1 30m1,生理盐水组（LS组,25例）给予0．5％左布比卡因复合生理盐水30ml。比较两组感觉运动阻滞起效时间、持续时间、心率...     

连续和同时线索化条件下的返回抑制容量

利用线索-靶子模式进行了返回抑制容量的两个实验。实验一对外周数个位置连续线索化,实验二对外周数个位置同时线索化。结果表明：(1)在连续线索化条件下,当线索化位置是相邻时,返回抑制容量可以达到4个,当线索化位置是间隔时,返回抑制容量只有1个。(2)在同时线索化条件下．当线索化位置是相邻时,返回抑制容量可以达到3个,当线索化位置是间隔时．返回抑制容量只有1个。结果支持认为存在着两种不同的返回抑制的观点,即一种是弥散性的,其容量较大;另一种是集中性的,其容量只有一个。     

Animal foraging and the evolution of goal-directed cognition

Foraging- and feeding-related behaviors across eumetazoans share similar molecular mechanisms, suggesting the early evolution of an optimal foraging behavior called area-restricted search (ARS), involving mechanisms of dopamine and glutamate in the modulation of behavioral focus. Similar mechanisms in the vertebrate basal ganglia control motor behavior and cognition and reveal an evolutionary progression toward increasing internal connections between prefrontal cortex and striatum in moving from amphibian to primate. The basal ganglia in higher vertebrates show the ability to transfer dopaminergic activity from unconditioned stimuli to conditioned stimuli. The evolutionary role of dopamine in the modulation of goal-directed behavior and cognition is further supported by pathologies of human goal-directed cognition, which have motor and cognitive dysfunction and organize themselves, with respect to dopaminergic activity, along the gradient described by ARS, from perseverative to unfocused. The evidence strongly supports the evolution of goal-directed cognition out of mechanisms initially in control of spatial foraging but, through increasing cortical connections, eventually used to forage for information.     

High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

Although the Bock–Aitkin likelihood-based estimation method for factor analysis of dichotomous item response data has important advantages over classical analysis of item tetrachoric correlations, a serious limitation of the method is its reliance on fixed-point Gauss-Hermite (G-H) quadrature in the solution of the likelihood equations and likelihood-ratio tests. When the number of latent dimensions is large, computational considerations require that the number of quadrature points per dimension be few. But with large numbers of items, the dispersion of the likelihood, given the response pattern, becomes so small that the likelihood cannot be accurately evaluated with the sparse fixed points in the latent space. In this paper, we demonstrate that substantial improvement in accuracy can be obtained by adapting the quadrature points to the location and dispersion of the likelihood surfaces corresponding to each distinct pattern in the data. In particular, we show that adaptive G-H quadrature, combined with mean and covariance adjustments at each iteration of an EM algorithm, produces an accurate fast-converging solution with as few as two points per dimension. Evaluations of this method with simulated data are shown to yield accurate recovery of the generating factor loadings for models of upto eight dimensions. Unlike an earlier application of adaptive Gibbs sampling to this problem by Meng and Schilling, the simulations also confirm the validity of the present method in calculating likelihood-ratio chi-square statistics for determining the number of factors required in the model. Finally, we apply the method to a sample of real data from a test of teacher qualifications.     

On local influence analysis of full information item factor models

The full information item factor (FIIF) model is very useful for analyzing relations of dichotomous variables. In this article, we present a feasible procedure to assess local influence of minor perturbations for identifying influence aspects of the FIIF model. The development is based on a Q-displacement function which is closely related with the Monte Carlo EM algorithm in the ML estimation. In the E-step of this algorithm, the conditional expectations are approximated by sample means of observations simulated by the Gibbs sampler from the appropriate conditional distributions. It turns out that these observations can be utilized for computing the building blocks of the proposed diagnostic measures. The diagnoses are based on the conformal normal curvature that can be computed easily. A number of interesting perturbation schemes are considered. The methodology is illustrated with two real examples.The research is fully supported by a grant (CUHK 4356/00H) from the Research Grant Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, Associate Editor, anonymous reviewers, and W.Y. Poon for valuable comments for improving the paper, and to ICPSR and the relevant founding agency for allowing us to use of their data. The assistance of Michael Leung and Esther Tam is gratefully acknowledged.     

The statistical analysis of general processing tree models with the EM algorithm

Multinomial processing tree models assume that an observed behavior category can arise from one or more processing sequences represented as branches in a tree. These models form a subclass of parametric, multinomial models, and they provide a substantively motivated alternative to loglinear models. We consider the usual case where branch probabilities are products of nonnegative integer powers in the parameters, 0_s1, and their complements, 1 - _s. A version of the EM algorithm is constructed that has very strong properties. First, the E-step and the M-step are both analytic and computationally easy; therefore, a fast PC program can be constructed for obtaining MLEs for large numbers of parameters. Second, a closed form expression for the observed Fisher information matrix is obtained for the entire class. Third, it is proved that the algorithm necessarily converges to a local maximum, and this is a stronger result than for the exponential family as a whole. Fourth, we show how the algorithm can handle quite general hypothesis tests concerning restrictions on the model parameters. Fifth, we extend the algorithm to handle the Read and Cressie power divergence family of goodness-of-fit statistics. The paper includes an example to illustrate some of these results.This research was supported by National Science Foundation Grant BNS-8910552 to William H. Batchelder and David M. Riefer. We are grateful to David Riefer for his useful comments, and to the Institute for Mathematical Behavior Sciences for its support.     

Multiple-probe technique: a variation on the multiple baseline

Multiple-baseline and probe procedures are combined into a “multiple-probe” technique. The technique is designed to provide a thorough analysis of the relationship between an independent variable and the acquisition of a successive-approximation or chain sequence. It provides answers to the following questions: (1) What is the initial level of performance on each step in the training sequence? (2) What happens if sequential opportunities to perform each next step in the sequence are provided before training on that step? (3) What happens when training is applied? (4) What happens to the performance of remaining steps in the sequence as criterion is reached in the course of training each prior step? The technique features: (1) one initial probe of each step in the training sequence, (2) an additional probe of every step after criterion is reached on any training step, and (3) a series of “true” baseline sessions conducted just before the introduction of the independent variable to each training step. Intermittent probes also provide an alternative to continuous baseline measurement, when such measurement during extended multiple baselines (1) may prove reactive, (2) is impractical, and/or (3) a strong a priori assumption of stability can be made.     

Measuring nominal scale agreement between a judge and a known standard

Two designs for comparing a judge's ratings with a known standard are presented and compared. Design A pertains to the situation where the judge is asked to categorize each ofN subjects into one ofr (known) classes with no knowledge of the actual number in each class. Design B is employed when the judge is given the actual number in each class and is asked to categorize the individuals subject to these constraints. The probability distribution of the total number of correct choices is developed in each case. A power comparison of the two procedures is undertaken.