A programme evaluation of the Family Crisis Intervention Program (FCIP): relating programme characteristics to change

This study evaluated the Family Crisis Intervention Program (FCIP), focusing on crisis, child safety, family functioning and child behaviour problems. Questionnaires were completed by 183 families in crisis and their FCIP worker. After FCIP, the crisis had decreased and child safety had increased. Although problematic family functioning persisted after intervention, improvements were found in parent–child interaction, parenting stress, parental competence and child behaviour problems. Crisis change, safety change and improved family functioning were associated with programme characteristics, especially the therapeutic relationship, analysis of the crisis situation, duration of the intervention and the solution‐focused approach. The discussion addressed implications of this study with regard to outcome measures in the evaluation of family crisis intervention and the importance of particular programme characteristics.     

An evaluation of the trimmed mean approach in clinical trials with dropout

The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm. The untrimmed values are used to calculated means or mean changes. An explicit intent of trimming is to favor the group with lower dropout because having more completers is a beneficial effect of the drug, or conversely, higher dropout is a bad effect. In the simulation study, difference between treatments estimated from trimmed means was greater than the corresponding effects estimated from untrimmed means when dropout favored the experimental group, and vice versa. The trimmed mean estimates a unique estimand. Therefore, comparisons with other methods are difficult to interpret and the utility of the trimmed mean hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients, and adherence decisions in the trial are sufficiently similar to clinical practice in order to generalize the results. Trimming might be applicable to other inter‐current events such as switching to or adding rescue medicine. Given the well‐known biases in some methods that estimate effectiveness, such as baseline observation carried forward and non‐responder imputation, the trimmed mean may be a useful alternative when its assumptions are justifiable.     

Pawns or Pioneers? The Logic of User Participation in Anti‐poverty Policy‐making in Public Policy Units in Belgium

Since anti‐poverty policy‐making in Belgium is embedded in a logic of user participation, social policy has shown interest in employing users with experience of poverty as experts in public policy units in order to implement and monitor anti‐poverty policies. In this article, we discuss this recent development as a relevant case for contemporary public service delivery in the European context. In the light of the ambiguous practices of user participation in Belgium, the enacted logic of involving service users in public service delivery is discussed, and potential risks and opportunities identified: do these individuals function as pawns or as pioneers? In search of opportunities for the implementation of anti‐poverty policies, we explore different ways in which public policy units can act upon the participation of employed service users in public service delivery. On the level of public service delivery, we argue that the participation of expert users risks their use as a mere blind, de‐responsibilizing those who work in public policy units in providing high‐quality services and being responsive to all service users including those living in poverty. On the level of these individual experts, we argue that the employed service users risk becoming pawns, acquiring a tragic‐optimistic outlook on life. We conclude that enabling service users to participate as pioneers requires the joint and mutual responsibility of the employed service users and those who work in public policy units for implementing and monitoring anti‐poverty strategies rather than exclusive responsibility assigned to individual service users.     

Accounting for variability in individual hierarchical clinical trial data

Meta-analytical approaches have been extensively used to analyze medical data. In most cases, the data come from different studies or independent trials with similar characteristics. However, these methods can be applied in a broader sense. In this paper, we show how existing meta-analytic techniques can also be used as well when dealing with parameters estimated from individual hierarchical data. Specifically, we propose to apply statistical methods that account for the variances (and possibly covariances) of such measures. The estimated parameters together with their estimated variances can be incorporated into a general linear mixed model framework. We illustrate the methodology by using data from a first-in-man study and a simulated data set. The analysis was implemented with the SAS procedure MIXED and example code is offered.     

The effect of personalization on response rates and data quality in web surveys

Personalizing correspondence has often shown to significantly increase survey response rates in mail surveys. This study experimentally tests whether personalization of email invitations acts correspondingly to web survey response rates. Also, it is investigated whether personalization influences data quality. The results of the study, using a large student sample, show that personalization significantly increases the web survey response rate by 8.6 percentage points. The data quality does not appear to be affected in any major way by personalizing the email invitations. However, the analyses do show that respondents of the personalization condition tend to respond with more social desirability bias to sensitive questions. Therefore, it is concluded that personalization has positive effects on the survey response rate, but one should carefully consider whether or not to personalize when a survey on sensitive topics is conducted.     

联盟组合多样性及其与绩效维度的关系

基于480家机构的以自我为中心的网络数据,分析了联盟组合多样性和财务绩效、激进式创新绩效、渐进式创新绩效等三个重要绩效维度之间的关系.其中,联盟组合多样性与财务绩效负相关;而联盟组合多样性与激进式创新绩效呈现倒U形关系;联盟组合多样性与渐进式创新绩效呈现递减收益正相关.结果表明,管理者应该意识到联盟组合多样性的可能的消极影响,并且意识到不同水平的联盟组合多样性是最有益的.     

Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model

The development of a composite indicator (CI) over a set of individual indicators is worthwhile in case the methodological aggregation process is sound and the results are clear. It can then be used as a powerful tool for performance evaluation, benchmarking, and decision making. In this respect, data envelopment analysis (DEA), as a self appraisal technique, has recently received considerable attention in the construction of CIs for policy analysis and public communication. However, due to the ever increasing complexity of numerous performance evaluation problems, more and more potential indicators might be developed to represent an evaluation activity in a more comprehensive way. These indicators might also belong to different categories and further be linked to one another constituting a multilayer hierarchical structure. Simply treating all the indicators to be in the same layer as is the case in the basic DEA model thereby ignores the information on their hierarchical structure, and further leads up to weak discriminating power and unrealistic weight allocations. To overcome this limitation, a multiple layer DEA-based CI model is developed in this study to embody a hierarchical structure of indicators in the DEA framework, and both its primal and dual form are realized. The proposed model is illustrated by constructing a composite road safety performance index for a set of European countries.     

Marginal Correlation in Longitudinal Binary Data Based on Generalized Linear Mixed Models

This work aims at investigating marginal correlation within and between longitudinal data sequences. Useful and intuitive approximate expressions are derived based on generalized linear mixed models. Data from four double-blind randomized clinical trials are used to estimate the intra-class coefficient of reliability for a binary response. Additionally, the correlation between such a binary response and a continuous response is derived to evaluate the criterion validity of the binary response variable and the established continuous response variable.     

Nonignorable Attrition in Multi-Period Panels With Refreshment Samples

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition should not be ignored if it is related to the variables of interest. Hirano, Imbens, Ridder, and Rubin proposed the additively nonignorable (AN) attrition model to correct for the potential selectivity of the attrition in panels with two periods. If a refreshment sample is available in the second period, their model nonparametrically just-identifies the population distribution and the observation probability. We propose the sequential additively nonignorable attrition model that just-identifies the population distribution and the sequence of observation hazards for panels with more than two periods.