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Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ω as a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.
Background: Mesenchymal stromal cells (MSCs), multipotent progenitors that can be isolated from a variety of different tissues, are becoming increasingly important as cell therapeutics targeting immunopathologies and tissue regeneration. Current protocols for MSC isolation from bone marrow (BM) rely on density gradient centrifugation (DGC), and the production of sufficient MSC doses is a critical factor for conducting clinical MSC trials. Previously, a Good Manufacturing Practice (GMP)–compatible non-woven fabric filter device system to isolate MSCs was developed to increase the MSC yield from the BM. The aim of our study was to compare high-resolution phenotypic and functional characteristics of BM-MSCs isolated with this device and with standard DGC technology.
Methods: Human BM samples from 5 donors were analyzed. Each sample was divided equally, processing by DGC, and with the filter device. Stem cell content was assessed by quantification of colony-forming units fibroblasts (CFU-F). Immunophenotype was analyzed by multicolor flow cytometry. In vitro trilineage differentiation potential, trophic factors, and IDO-1 production were assessed. Functionally, immunomodulatory potential, wound healing, and angiogenesis were assayed in vitro.
Results: The CFU-F yield was 15-fold higher in the MSC preparations isolated with the device compared to those isolated by DGC. Consequently, the MSC yield that could be manufactured at passage 3 per mL collected BM was more than 10 times higher in the device group compared to DGC (1.65 × 109 vs. 1.45 × 108). The immunomodulatory potential and IDO-1 production showed donor-to-donor variabilities without differences between fabric filter-isolated and DGC-isolated MSCs. The results from the wound closure assays, the tube formation assays, and the trilineage differentiation assays were similar between the groups with respect to the isolation method. Sixty-four MSC subpopulations could be quantified with CD140a+CD119+CD146+ as most common phenotype group, and CD140a+CD119+CD146+MSCA-1–CD106–CD271– and CD140a+CD119+CD146–MSCA-1–CD106–CD271– as most frequent MSC subpopulations. As trophic factors hepatocyte growth factor, epidermal growth factor, brain-derived neurotrophic factor, angiopoietin-1, and vascular endothelial growth factor A could be detected in both groups with considerable variability between donors, but independent of the respective MSC isolation technique.
Conclusion: The isolation of MSCs using a GMP-compatible fabric filter system device resulted in higher yield of CFU-F, producing substantially more MSCs with similar subpopulation composition and functional characteristics as MSCs isolated by DGC.
his paper studies heterogeneity in the reaction to rank feedback. In a laboratory experiment, individuals take part in a series of dynamic real-effort contests with intermediate feedback. To solve the identification problem in estimating the causal effect of rank feedback on subsequent effort provision we implement a random multiplier in the first round of each contest. The realization of this multiplier then serves as a valid instrument for rank feedback. While rank feedback has a robust effect on subsequent effort provision on average, an explicit analysis of between-subject heterogeneity reveals that a substantial fraction of participants in fact react entirely opposite than the aggregated results indicate. We further show that this heterogeneity has consequences for overall outcomes, thereby arguing that heterogeneous sensitivities to rank feedback could have implications for the design of various policies in education and organizations.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.