降纤酶治疗急性脑梗死的临床疗效评价

采用随机同期对照的临床研究方法,对发病在24h内的90例急性脑梗死患者,分别进行降纤酶与常规治疗方法的治疗,观察其疗效,应用Logistic回归分析方法进行评价。结果显示,降纤酶可降低血浆纤维蛋白原的水平,有效地促进神经功能恢复,改善急性脑梗死患者的临床症状,有着与常规治疗方法相同的临床疗效。     

The educational boundary1

In this contribution, the authors defi ne and discuss the educational boundary in analytic training, which they believe is an often neglected and useful concept in psychoanalytic education. The framework on which their discussion rests includes the recent attention of psychoanalysts to issues of boundaries and ethics. Their understanding of how clinical work affects the mind of the analyst educator, as well as the ways the personalities of various analysts affect their dealings with faculty peers and students, are the other cornerstones of their discussion. The authors contend that many of the institutional problems encountered in the training of analysts can be better understood when viewed through the prism of the educational boundary. They present examples which illustrate several of the ways psychoanalytic educators complicate the training experience of candidates, offer specifi c explanations as to why analysts struggle as they try to manage their educational interventions, and indicate in a discussion of potential remedies that those behaviors might be avoided if the educational boundary is in focus. They also provide an example of how the educational boundary can be more effectively managed.     

On the edge: The psychoanalyst's transference1

Countertransference is a central topic in analytic work and in the literature. The concept of countertransference includes a basic question which has been understood in different ways. The author attempts to differentiate between the psychoanalyst's transference and his countertransference in the analytic process. It is hard to draw a line between them; analysts are always on the edge. The analyst's transference will be explored and described using three approaches: narcissism, regression profile and the analyst's phase of life. Regression profile is a new concept developed by the author, which may help us to understand the core of the analyst's transference in the analytic situation. She illustrates the topic by clinical vignettes.     

In Spite of Indeterminacy Many Common Factor Score Estimates Yield an Identical Reproduced Covariance Matrix

It was investigated whether commonly used factor score estimates lead to the same reproduced covariance matrix of observed variables. This was achieved by means of Schönemann and Steiger’s (1976) regression component analysis, since it is possible to compute the reproduced covariance matrices of the regression components corresponding to different factor score estimates. It was shown that Thurstone’s, Ledermann’s, Bartlett’s, Anderson-Rubin’s, McDonald’s, Krijnen, Wansbeek, and Ten Berge’s, as well as Takeuchi, Yanai, and Mukherjee’s score estimates reproduce the same covariance matrix. In contrast, Harman’s ideal variables score estimates lead to a different reproduced covariance matrix.     

Does Agrammatic Speech Constitute a Regression to Child Language? A Three-Way Comparison between Agrammatic, Child, and Normal Ellipsis

When children are in the process of learning their mother tongue, they show frequent use of nonfinite clauses, even though they produce finite clauses at the same time, thereby demonstrating the availability of the functional domain associated with finiteness. In this study the hypothesis was tested that this behavior results from an overuse of the normal elliptical repertoire that has also been observed in agrammatic aphasia. The purpose of this overuse is prevention of computational overload. In support of the hypothesis it was found that children behaved very similar to aphasics and normal adults with respect to the following parameters: (a) distribution of types of ellipsis, (b) elaboration of ellipses, (c) word order, (d) subject omission, (e) frequency of weak subject pronouns, and (f) verb type (eventivity). The results also support the Jackson/Jakobson regression hypothesis, at least at the grammatical level.     

定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Order‐constrained linear optimization

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order‐constrained linear least‐squares (OCLO) optimization algorithm that maximizes the linear least‐squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out‐of‐sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat‐tailed distributions), OCLO shows less bias and variance, and substantially better out‐of‐sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.     

AFFECT,SYMPTOM, FANTASY,DREAM: CLINICAL AND THEORETICAL CONSIDERATIONS

A symptom being studied in the process of analysis can be seen as not unlike the unconscious affect it sprang from. The author presents a case in which a symptom, premature ejaculation, was analogous to the unconscious affect of guilt, which itself seemed to be a premature defensive transformation of a deeper current of anger. Guilt was interpreted as if it were a psychic premature ejaculation, a defensive derailment of anger. Fantasy and dream seemed to be engaged in similar transformations, with a fantasy of “premature incarceration” not unlike the symptom itself in its analogous functioning. Analysis of affect, symptom, fantasy, and dream in complex, integrative analytic process led not only to resolution of the symptom itself, but also to a deeper understanding of the mind's complex functioning in general.     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.