Semiparametric Estimation of Stochastic Production Frontier Models

This article extends the linear stochastic frontier model proposed by Aigner, Lovell, and Schmidt to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudolikelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte Carlo results show that the proposed estimators perform well in finite samples. An empirical application is presented. Extensions to a partially linear frontier function and to more flexible one-sided error distributions than the half-normal are discussed     

Informing the selection of futility stopping thresholds: case study from a late-phase clinical trial

In an environment where (i) potential risks to subjects participating in clinical studies need to be managed carefully, (ii) trial costs are increasing, and (iii) there are limited research resources available, it is necessary to prioritize research projects and sometimes re-prioritize if early indications suggest that a trial has low probability of success. Futility designs allow this re-prioritization to take place. This paper reviews a number of possible futility methods available and presents a case study from a late-phase study of an HIV therapeutic, which utilized conditional power-based stopping thresholds. The two most challenging aspects of incorporating a futility interim analysis into a trial design are the selection of optimal stopping thresholds and the timing of the analysis, both of which require the balancing of various risks. The paper outlines a number of graphical aids that proved useful in explaining the statistical risks involved to the study team. Further, the paper outlines a decision analysis undertaken which combined expectations of drug performance with conditional power calculations in order to produce probabilities of different interim and final outcomes, and which ultimately led to the selection of the final stopping thresholds.     

Simulation of close-to-reality population data for household surveys with application to EU-SILC

Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).     

Response Styles in Telephone and Household Interviewing: A Field Experiment

Two independent groups from the Los Angeles metropolitan areawere sampled and interviewed—one was sampled by a formof random digit dialing and interviewed by telephone; the otherwas sampled on an area probability basis and interviewed face-to-facein households. Few sociodemographic differences were found betweenthe telephone and household samples, suggesting that the twomethods sampled essentially the same population. There weredifferences in the quantity and quality of the data obtained,however, and the telephone sample had more missing data forfamily income, more acquiescence, evasiveness, and extremenessresponse bias on attitude questions, and more responses to checklists.     

Indicators of family resilience after the death of a child

The aim of this study was to identify and describe resilience qualities in families after losing a child. Questionnaires, including an open-ended question, were utilized to collect data independently from the parents and siblings of the deceased in 89 Belgian families. The results indicate that family strengths in general, and commitment to the family in particular, helped the families' adaptation after the loss. In addition, the adaptation process after the loss was aided if the family members viewed the crisis as a challenge. Both the siblings and the parents indicated that the extent to which a family experienced support from the community was directly related to family adaptation after the loss. Redefining the situation and utilizing social support from friends and family were underlined as effective family coping strategies. The findings could be used in interventions to promote family resilience, thereby affirming the reparative potential of families.