Reduced Cortisol Metabolism during Critical Illness

Critical illness is often accompanied by hypercortisolemia, which has been attributed to stress-induced activation of the hypothalamic-pituitary-adrenal axis. However, low corticotropin levels have also been reported in critically ill patients, which may be due to reduced cortisol metabolism.status: publishe

Multilevel Modeling of Single-Case Experimental Data: Handling data and design complexities

In single-case or single-subject designs (SSED), individual cases are measured repeatedly under different conditions, in order to assess the effect of the condition. Recently, multilevel models were proposed to combine the results of SSED studies, resulting in more general or detailed conclusions. The purpose of the proposed research is to empirically investigate the multilevel approach, using both real data and simulation studies. The research will entail several studies designed to address major complications encountered when synthesizing results from SSED research (see also projects 3H110617, 3H150687, 3H150316 and 3H250079). This specific research project focuses on the problems encountered when applying the multilevel model for the meta-analysis of SSED count data. Simulation studies are set up to analyze how robust the model is against misspecifications: what happens when the basic continuous model is applied to non-normal discrete data? A second question is how to apply the model for the statistical meta-analysis of count data: how can count data be standardized and how can the meta-analysis combine primary studies which report both continuous and count data? The results of the simulation studies will also be illustrated with empirical datasets. The intent of this dissertation is twofold. On the one hand, we empirically validate the basic three-level model and several extensions to it using large simulation studies and giving empirical illustrations. On the other hand, we want inform applied applied SSED researchers about the value of multilevel modeling of SSEDs and how to use these models. As a consequence, this dissertation is informative for methodologists, research analysts and synthesists, but also for applied SSED researchers.status: publishe

Papular cutaneous lesions in a cat associated with feline infectious peritonitis

A 7-month-old-intact male domestic shorthair cat was presented with fever, anterior uveitis in the right eye and respiratory distress when handled. These signs along with mild changes in serum protein levels and the exclusion of other potential causes were suggestive of feline infectious peritonitis (FIP). As the disease progressed, more clinical signs consistent with FIP, including renal involvement and later pleural effusion, became evident. Non-pruritic cutaneous lesions, characterized by slightly raised intradermal papules over the dorsal neck and over both lateral thoracic walls, were recognized at the end stage of the disease. The identification of papules in well-haired skin was difficult, and clipping of the fur facilitated their detection. Definitive diagnosis of FIP was made by histopathology and by immunohistochemical demonstration of coronavirus antigen in macrophages within kidney and skin lesions. The case was classified as a mixed form of FIP. Recognition of associated cutaneous lesions may facilitate a diagnosis of FIP in suspicious cases

One By One: Two Shiny web applications for the design and analysis of single-case experiments

status: publishe

The Application of Meta-Analytic Models with Multiple Random Effects: A Systematic Review

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models or cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of three-level meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified random effects models are not often used although they might account better for the meta-analytic data structure of the analyzed datasets. Also, we have found that the simulation studies done on multilevel meta-analysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed and further suggestions are given

Visual Representations of Meta-Analyses of Multiple Outcomes: Extensions to Forest Plots, Funnel Plots, and Caterpillar Plots

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias

Improving the Methodological Quality of Single-Case Experimental

status: publishe

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling

The MultiSCED web application has been developed to assist applied researchers in behavioral sciences to apply multilevel modeling to quantitatively summarize single-case experimental design (SCED) studies through a user-friendly point-and-click interface embedded within R. In this paper, we offer a brief introduction to the application, explaining how to define and estimate the relevant multilevel models and how to interpret the results numerically and graphically. The use of the application is illustrated through a re-analysis of an existing meta-analytic dataset. By guiding applied researchers through MultiSCED, we aim to make use of the multilevel modeling technique for combining SCED data across cases and across studies more comprehensible and accessible.status: Published onlin