高校职业道德教育应强化意志培养

本文通过分析三鹿集团破产的原因,揭示了职业道德意志培养的重要性,并对高校学生职业道德意志的培养提出相应的对策。     

Nonparametrics: The Early Years—Impressions and Recollections

In 1942 Wolfowitz introduced the term nonparametric into the statistical literature to call attention to the need for extending then-existing statistical theory beyond the customary parametric framework. Subsequently, statistical methods that did not depend on a strictly parametric setup became known as nonparametric methods. This article surveys developments in nonparametrics roughly up to 1960. The suggestion is made that statistics might be better served by eliminating the term nonparametric altogether from the statistical vocabulary.     

Using recurrent time-to-event models with multinomial outcomes to generate toxicity profiles

Most clinical studies, which investigate the impact of therapy simultaneously, record the frequency of adverse events in order to monitor safety of the intervention. Study reports typically summarise adverse event data by tabulating the frequencies of the worst grade experienced but provide no details of the temporal profiles of specific types of adverse events. Such 'toxicity profiles' are potentially important tools in disease management and in the assessment of newer therapies including targeted treatments and immunotherapy where different types of toxicity may be more common at various times during long-term drug exposure. Toxicity profiles of commonly experienced adverse events occurring due to exposure to long-term treatment could assist in evaluating the costs of the health care benefits of therapy. We show how to generate toxicity profiles using an adaptation of the ordinal time-to-event model comprising of a two-step process, involving estimation of the multinomial response probabilities using multinomial logistic regression and combining these with recurrent time to event hazard estimates to produce cumulative event probabilities for each of the multinomial adverse event response categories. Such a model permits the simultaneous assessment of the risk of events over time and provides cumulative risk probabilities for each type of adverse event response. The method can be applied more generally by using different models to estimate outcome/response probabilities. The method is illustrated by developing toxicity profiles for three distinct types of adverse events associated with two treatment regimens for patients with advanced breast cancer.     

Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials

In rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta-analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between-study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(−10, 10) heterogeneity prior on the log(τ²) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta-analyzes of a few small trials.     

Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards

The U.S. federal government regulates the reliability of bulk power systems, while the reliability of power distribution systems is regulated at a state level. In this article, we review the history of regulating electric service reliability and study the existing reliability metrics, indices, and standards for power transmission and distribution networks. We assess the foundations of the reliability standards and metrics, discuss how they are applied to outages caused by large exogenous disturbances such as natural disasters, and investigate whether the standards adequately internalize the impacts of these events. Our reflections shed light on how existing standards conceptualize reliability, question the basis for treating large‐scale hazard‐induced outages differently from normal daily outages, and discuss whether this conceptualization maps well onto customer expectations. We show that the risk indices for transmission systems used in regulating power system reliability do not adequately capture the risks that transmission systems are prone to, particularly when it comes to low‐probability high‐impact events. We also point out several shortcomings associated with the way in which regulators require utilities to calculate and report distribution system reliability indices. We offer several recommendations for improving the conceptualization of reliability metrics and standards. We conclude that while the approaches taken in reliability standards have made considerable advances in enhancing the reliability of power systems and may be logical from a utility perspective during normal operation, existing standards do not provide a sufficient incentive structure for the utilities to adequately ensure high levels of reliability for end‐users, particularly during large‐scale events.     

Modeling Repeatable Events Using Discrete‐Time Data: Predicting Marital Dissolution

I join two methodologies by illustrating the application of multilevel modeling principles to hazard‐rate models with an emphasis on procedures for discrete‐time data that contain repeatable events. I demonstrate this application using data taken from the 1995 National Survey of Family Growth (NSFG) to ascertain the relationship between multiple covariates and risk of subsequent marital dissolution. I consider both fixed‐ and random‐effects versions of the multilevel model, as well as a Generalized Estimating Equation alternative to estimating random effects. I compare results obtained from the various estimators, noting why differences occur, and recommend when to choose the various alternatives. I also provide a set of SAS and STATA programs that can be used to analyze the NSFG data.     

Power Law Selection Model for Repairable Systems

A repairable system, under minimal repair, is usually modeled according to a Non-Homogeneous Poisson Process (NHPP) assuming a Power Law intensity function. A traditional approach considers iid NHPPs in order to conduct a statistical analysis based on a sample of systems. However, systems might be heterogeneous due to unmeasured variables such as age, suppliers, and so on. In order to verify this assumption a frequentist approach is proposed in this article. Some possible model scenarios considering different systems heterogeneity are compared using likelihood ratio tests and information criteria. Real data sets illustrate the proposed methodology.     

Family-Like Group in a Correctional Institution

This paper examines a particular residential group within a correctional treatment institution. The examination focuses on the family-likeness of this group. The concept of family-like as it exists in the literature is described and the features of family-likeness in the group under discussion are outlined. A comparison is made to other residential groups as well as to other groups described as family-like.     

EDITOR's NOTES

Abstract

Over the past 20 years investigators have explored the incidence, experience, and extent of grieving following miscarriage for heterosexual partnered women and, to some degree, for men. Prior investigations confirm an increase in disturbed emotions following unexpected pregnancy loss and suggest a variety of predisposing social and obstetrical factors that lead to such responses. Little is known, however, about the ways in which lesbian women experience and respond to miscarriage. An argument is made that given the increased number of lesbian women attempting pregnancy through alternative methods of conception, research is warranted that explores their experiences and potentially unique caring needs following miscarriage.     

Cross cultural differences in decisions from experience: Evidence from Denmark,Israel, and Taiwan

Three studies are presented that compare decisions from experience in Denmark, Israel, and Taiwan. They focus on two change-related cultural differences suggested by previous research on dialectical vs. analytic approach to thinking. The first implies that East Asians are more likely to change their behavior over time (i.e., are less consistent), the second that they expect more changes in the environment. The results show that the “less consistency in the East” hypothesis has a high predictive value. This hypothesis accurately predicts a behavioral pattern that was documented in all three studies, as well as a non-trivial effect of limited feedback in Study 3: When feedback was limited to the obtained payoff, the participants from Taiwan exhibited less risk aversion than the Israeli. Analysis of the “expecting more changes in the East” hypothesis reveals mixed results. This hypothesis was supported in Study 2, which examined relatively complex multi-alternative multi-outcome tasks, but not in Studies 1 and 3, which examined simple two-alternative two-outcome choice tasks. A possible explanation for the different predictive value of the two examined hypotheses is discussed.