Morbidity measures in the presence of recurrent composite endpoints

The analysis of recurrent event data in clinical trials presents a number of difficulties. The statistician is faced with issues of event dependency, composite endpoints, unbalanced follow‐up times and informative dropout. It is not unusual, therefore, for statisticians charged with responsibility for providing reliable and valid analyses to need to derive new methods specific to the clinical indication under investigation. One method is proposed that appears to have possible advantages over those that are often used in the analysis of recurrent event data in clinical trials. Based on an approach that counts periods of time with events instead of single event counts, the proposed method makes an adjustment for patient time on study and incorporates heterogeneity by estimating an individual per‐patient risk of experiencing a morbid event. Monte Carlo simulations demonstrate that, with use of a real clinical study data, the proposed method consistently outperforms other measures of morbidity. Copyright © 2003 John Wiley & Sons, Ltd.     

The Labour Market

Social Indicators Research -     

Towards a Typology of European Welfare Production: Summary and Discussion

Social Indicators Research -     

Authors’ response: on the role of data,statistics and decisions in a pandemic

AStA Advances in Statistical Analysis -     

How People Perceive and Will Cope with Risks from the Diffusion of Ubiquitous Information and Communication Technologies

Information and communication technologies (ICTs) are increasingly pervasive and continue to reshape our environment. This trend carries different risks. Therefore, the early sensitization of people to these risks, as well as improving their capacity for protective coping behavior, is essential. Based on the protection motivation theory (PMT), we examined with structural equation modeling the relationships between different components of threat and coping appraisal to explain protective and nonprotective responses. Calculations were performed with data from a representative survey on perception and use of ICT among German residents (N= 5,030). Our findings largely supported the proposed model: an increased perceived threat was positively related to the intentions to react protectively and nonprotectively. Perceived coping efficacy increased the protective and decreased the nonprotective responses. Negative affect enhanced the perceived threat and the nonprotective response, but inhibited protective intentions. The implications of these findings on how to sensitize people to the risks of these new technologies are outlined.     

Positive Effekte sozialen Faulenzens beim Lösen komplexer Probleme

The term “social loafing” refers to a decrease in motivation and achievement in group work due to sinking responsibility of the individual for the groups’ outcome. Recent studies assume a paradoxical relation between motivation and outcome, which should produce better outcome with decreased motivation. The current study addresses this question. Two experiments with N=60 subjects working in groups of 3 are reported. Subjects had to deal with a computer simulated scenario of a fire fighting situation. There was a simple and a more complex version; also, individual responsibility for the groups’ outcome was either given (coactive condition) or not (collective condition). During experiment 1, subjects could only deal with their own section of the scenario; in experiment 2, they were allowed to work on the whole field. Dependent variables were effort (in terms of commands given) and achievement (in terms of space saved). It turned out that under collective responsibility effort was reduced but this did not lead to decreased achievement. Under the complex condition, with decreased effort even an increase in achievement could be demonstrated. Implications for the collective effort model presented by Karau and Williams are discussed.     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

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