《露水情》的社会和历史解读

第二次世界大战后工党统治下的英国被称誉为“福利国家”,其表面的繁荣掩盖了中下层人民的贫困。年轻的一代对此产生迷惘 ,愤怒之情油然而生。因此 ,英国文坛出现了一批“愤怒的青年”的作品。《露水情》便是当时较为成功的一部。小说深刻地对当时的英国进行了社会批判。     

转型期中国公众的分配公平感：结果公平与机会公平

本文考察了中国公众的结果公平感和机会公平感,检验并比较了社会结构解释和相对剥夺解释在中国的适用性。研究发现,大多数公众认可结果公平和机会公平,且机会公平感高于结果公平感,但二者仅微弱相关。结果公平感由收入水平决定,机会公平感主要受教育水平影响。外资和私营单位雇员比国有和集体单位雇员更具机会公平感,城市中下层就业者对结果和机会分配均持批评态度。“个体相对剥夺”而不是“群体相对剥夺”对分配公平感有决定性影响,结果公平感只受横向剥夺影响,而机会公平感则主要受纵向剥夺影响。     

我国移居老年人的居住意识研究

利用大连市432份针对60岁以上本地老年人和移居老年人的调查问卷,运用交叉分析和卡方检验方法,对我国移居老年人的居住意识进行假设检验。将居住意识详解为定居意向。移居意向和不确定意向。研究表明：①本地老年人的定居意向非常强。移居老年人容易产生返乡意向。②在移居老年人中,主观适应感强的老年人具有较强的定居意识。主观适应感较弱或不太适应社会的老年人容易产生不确定的居住意向和返乡意向。③移居老年人中．不孤独的老年人具有较强的定居意向。感觉孤独的老年人容易具有不确定的居住意向和返乡意向。④移居老年人中。地区归属感越强。则定居意向越强。无归属感的老年人更容易具有返乡意向和不确定意向。对移居老年人居住意识的探讨有利于把握人口流动过程中老年人群体的生活状况。为提升老年人生活质量的各种制度和政策制定提供实践依据。     

“You’re a ‘high-risk’ customer”: A qualitative study of women’s experiences of receiving information from health professionals regarding health problems or complications in pregnancy

BackgroundExperiencing complications in pregnancy is stressful for women and can impact on fetal and maternal outcomes. Supportive encounters with health professionals can reduce the worry women experience. Further research is needed to understand women’s perspectives on communicating with their healthcare providers about their concerns.AimThis study explored women’s experiences of receiving information about pregnancy complications from healthcare providers and their interactions with multiple professionals and services during pregnancy.MethodsThis was a qualitative interpretive study. Semi-structured interviews were conducted with 20 women experiencing pregnancy complications recruited from antenatal services at two hospitals in Sydney. Inductive thematic analysis was used to analyse the data.FindingsWomen had a range of reactions to their diagnoses, including concern for their baby, for themselves and for their labour. Most women reported that communication with healthcare providers was distressing, they were not listened to and staff used insensitive, abrupt language. Women were also distressed by delays in education, receiving contradictory information and having to repeatedly share their stories with different health professionals. In some cases, this damaged the therapeutic relationship and reduced trust towards healthcare providers. Midwives were generally preferred over doctors because they had a more woman-centred approach.ConclusionTo improve women’s experiences of care for pregnancy complications, it is critical to improve the communication skills of maternity service providers. Women’s need for information, resources and support can best be provided by continuity of care with a named health professional, for example, a midwife working within an integrated multidisciplinary antenatal service model.     

Effects of Outcome and Probabilistic Ambiguity on Managerial Choices

Information ambiguity is prevalent in organizations and likely influences management decisions. This study examines, given imprecise probabilities and outcomes, how managers make choices when they are provided with single-figure benchmarks. Seventy-nine MBA students completed two experiments. We found that, in a decision framed as a decision under certainty involving an ambiguous outcome, the majority of the subjects were ambiguity prone in the loss condition and switched to ambiguity aversion in the gain condition. However, in the presence of probabilistic ambiguity in a decision under risk, this expected switching pattern was shown only when the difference in riskiness between the two choice options (in the loss condition) was perceived to be relatively small. In a companion study, we used a written protocol approach to identify factors that affect decision makers' investment choices when faced with ambiguous outcomes. Protocols frequently mentioned that the ambiguous outcome option was risky, even in the case which was framed as a decision under certainty in the problem statement. In a decision under risk with ambiguous outcomes, the combination of probabilistic risk and outcome ambiguity was seen as even more risky.     

Explaining inconsistencies between patients and collaterals: validity in outcome studies of coercive alcoholism treatment

This study aims to assess the consistency of replies to questionnaires mailed to patients and two kinds of collaterals, i.e., social workers and significant others, at a public treatment center with socially unstable and compulsory committed patients. It compares the quantity and kind of discrepancies between replies by patients and collaterals on outcome data concerning social situation and drinking habits. It aims to measure the amount of systematic bias among factors that may explain inconsistencies between reports, especially the factors compulsory commitment, worse outcome, frequency of contact and type of collateral. The responders generally agreed. Variables in which there was less agreement were explored in logistic regressions using ten explanatory variables. Significant relations did not exceed those expected by chance. Discrepancies were not systematic in size and kind. On a six-rank ordinal scale of alcohol use or abuse, however, patients tended to underestimate the extent of their abuse. Inconsistencies here concerned the degree but not the presence of abuse. No difference in consistency due to type of collateral was found. In conclusion, the consistency of the questionnaires was high and independent of the social situation of the patient, of compulsory commitment and of other background or treatment factors, as well as of treatment outcome and type of collateral.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

A study to investigate the human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and non-informative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.     

General purpose multiply robust data integration procedures for handling nonprobability samples

In recent years, there has been an increased interest in combining probability and nonprobability samples. Nonprobability sample are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been discussed in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. Also, nonparametric approaches may suffer from the curse of dimensionality and poor efficiency. We propose a data integration approach by combining multiple outcome regression models and propensity score models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions, and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. The asymptotic properties of point and variance estimators are established. The results from a simulation study show the benefits of the proposed method in terms of bias and efficiency. Finally, we apply the proposed method using data from the Korea National Health and Nutrition Examination Survey and data from the National Health Insurance Sharing Services.     

Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non-inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non-inferiority for two primary outcomes. While non-inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one-sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non-inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment-related adverse outcomes.