Die Arbeitsstelle „Theologie der Friedenskirchen“ im Fachbereich Evangelische Theologie der Universität Hamburg

Der vorliegende Band dokumentiert die Reden, die während der akademischen Feier anlässlich der Verleihung der Würde eines Dr. theol. honoris causa an Prof. Dr. Helmut Greve am 19. November 2007 in der Universität Hamburg gehalten wurden.This volume documents the speeches that were given during the academic celebration on the occasion of the award of the dignity of Dr. theol. honoris causa to Prof. Dr. Helmut Greve at the University of Hamburg on November 19, 2007

ICS Bulletin, Number 4, 2015

Der vorliegende Band dokumentiert die Reden, die während der akademischen Feier anlässlich der Verleihung der Würde eines Dr. theol. honoris causa an Prof. Dr. Helmut Greve am 19. November 2007 in der Universität Hamburg gehalten wurden.This volume documents the speeches that were given during the academic celebration on the occasion of the award of the dignity of Dr. theol. honoris causa to Prof. Dr. Helmut Greve at the University of Hamburg on November 19, 2007

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions

A Multidimensional Array Representation of State-Transition Model Dynamics

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy-tail distributions (multivariate Student’s t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89±0.90 (4.35, 7.86) and 2.04±0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy-tail distributions (multivariate Student’s t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89±0.90 (4.35, 7.86) and 2.04±0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals

Efficacy and Safety of Elamipretide in Individuals With Primary Mitochondrial Myopathy: The MMPOWER-3 Randomized Clinical Trial

BACKGROUND AND OBJECTIVES: Primary mitochondrial myopathies (PMMs) encompass a group of genetic disorders that impair mitochondrial oxidative phosphorylation, adversely affecting physical function, exercise capacity, and quality of life (QoL). Current PMM standards of care address symptoms, with limited clinical impact, constituting a significant therapeutic unmet need. We present data from MMPOWER-3, a pivotal, phase-3, randomized, double-blind, placebo-controlled clinical trial that evaluated the efficacy and safety of elamipretide in participants with genetically confirmed PMM. METHODS: After screening, eligible participants were randomized 1:1 to receive either 24 weeks of elamipretide at a dose of 40 mg/d or placebo subcutaneously. Primary efficacy endpoints included change from baseline to week 24 on the distance walked on the 6-minute walk test (6MWT) and total fatigue on the Primary Mitochondrial Myopathy Symptom Assessment (PMMSA). Secondary endpoints included most bothersome symptom score on the PMMSA, NeuroQoL Fatigue Short-Form scores, and the patient global impression and clinician global impression of PMM symptoms. RESULTS: Participants (N = 218) were randomized (n = 109 elamipretide; n = 109 placebo). The m0ean age was 45.6 years (64% women; 94% White). Most of the participants (n = 162 [74%]) had mitochondrial DNA (mtDNA) alteration, with the remainder having nuclear DNA (nDNA) defects. At screening, the most frequent bothersome PMM symptom on the PMMSA was tiredness during activities (28.9%). At baseline, the mean distance walked on the 6MWT was 336.7 ± 81.2 meters, the mean score for total fatigue on the PMMSA was 10.6 ± 2.5, and the mean T score for the Neuro-QoL Fatigue Short-Form was 54.7 ± 7.5. The study did not meet its primary endpoints assessing changes in the 6MWT and PMMSA total fatigue score (TFS). Between the participants receiving elamipretide and those receiving placebo, the difference in the least squares mean (SE) from baseline to week 24 on distance walked on the 6MWT was -3.2 (95% CI -18.7 to 12.3; p = 0.69) meters, and on the PMMSA, the total fatigue score was -0.07 (95% CI -0.10 to 0.26; p = 0.37). Elamipretide treatment was well-tolerated with most adverse events being mild to moderate in severity. DISCUSSION: Subcutaneous elamipretide treatment did not improve outcomes in the 6MWT and PMMSA TFS in patients with PMM. However, this phase-3 study demonstrated that subcutaneous elamipretide is well-tolerated. TRIAL REGISTRATION INFORMATION: Trial registered with clinicaltrials.gov, Clinical Trials Identifier: NCT03323749; submitted on October 12, 2017; first patient enrolled October 9, 2017. CLINICALTRIALS: gov/ct2/show/NCT03323749?term = elamipretide&draw = 2&rank = 9. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that elamipretide does not improve the 6MWT or fatigue at 24 weeks compared with placebo in patients with primary mitochondrial myopathy

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline