Joint modeling of hierarchically clustered and overdispersed non‐gaussian continuous outcomes for comet assay data

Multivariate longitudinal or clustered data are commonly encountered in clinical trials and toxicological studies. Typically, there is no single standard endpoint to assess the toxicity or efficacy of the compound of interest, but co‐primary endpoints are available to assess the toxic effects or the working of the compound. Modeling the responses jointly is thus appealing to draw overall inferences using all responses and to capture the association among the responses. Non‐Gaussian outcomes are often modeled univariately using exponential family models. To accommodate both the overdispersion and hierarchical structure in the data, Molenberghs et al. A family of generalized linear models for repeated measures with normal and conjugate random effects. Statistical Science 2010; 25:325–347 proposed using two separate sets of random effects. This papers considers a model for multivariate data with hierarchically clustered and overdispersed non‐Gaussian data. Gamma random effect for the over‐dispersion and normal random effects for the clustering in the data are being used. The two outcomes are jointly analyzed by assuming that the normal random effects for both endpoints are correlated. The association structure between the response is analytically derived. The fit of the joint model to data from a so‐called comet assay are compared with the univariate analysis of the two outcomes. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Regulating product markets: a growing challenge for open economies – with special references to agro-food product markets

In the present phase of internationalisation, processes of social construction of product markets are under stress. On one hand, the internationalisation of production processes gives more room for manoeuvre to the producers. On the other hand, the development of scientific technologies, embedded with new risks and uncertainties, makes it difficult to control the quality of products and production processes. The drive for competitiveness in a world where only free trade arguments seem to be heard limits the willingness of individual countries to take action. International institutions have been unable to launch the international co-ordination that could have helped countries to get out of this deadlock, though they need to fight climate change and pollution as well as the pressing new environmental hazards, which are illustrated with the case of the agro-food product markets in Europe. They should foster international processes of social construction of product markets to learn how to strike a better balance between free trade and safe trade principles before it is too late.     

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.     

A positive psychology intervention to foster economic integration of Syrian refugees in the Netherlands

Research shows that the labour market integration of refugees is often problematic. Little is known about what kind of measures are effective to help them integrate into the labour market. In the Netherlands, a positive psychology intervention “Mosaic” for Syrian refugees was developed to support their economic participation. This study aims to assess the effect of “Mosaic” on the economic participation and work-related attitudes of Syrian refugees. “Mosaic” was implemented in six municipalities by conducting an RCT with a waiting list control group. The results show that in the short term, the intervention group realizes a significantly larger increase in hours spent on an internship as well as substantially larger gains in confidence to find work in the future. In the long term, the intervention group experiences larger confidence gains related to finding a job that fits their education and experience.