Mütze, Tobias

[["dc.bibliographiccitation.firstpage","1980"],["dc.bibliographiccitation.issue","14"],["dc.bibliographiccitation.journal","Statistics in Medicine"],["dc.bibliographiccitation.lastpage","1998"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Zapf, Antonia"],["dc.contributor.author","Asendorf, Thomas"],["dc.contributor.author","Anten, Christoph"],["dc.contributor.author","Mütze, Tobias"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2021-04-14T08:26:15Z"],["dc.date.available","2021-04-14T08:26:15Z"],["dc.date.issued","2020"],["dc.description.abstract","In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate."],["dc.identifier.doi","10.1002/sim.8525"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81880"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1097-0258"],["dc.relation.issn","0277-6715"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.title","Blinded sample size reestimation for negative binomial regression with baseline adjustment"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2385"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Statistical Methods in Medical Research"],["dc.bibliographiccitation.lastpage","2403"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Mütze, Tobias"],["dc.contributor.author","Glimm, Ekkehard"],["dc.contributor.author","Schmidli, Heinz"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2020-12-10T18:38:28Z"],["dc.date.available","2020-12-10T18:38:28Z"],["dc.date.issued","2018"],["dc.description.abstract","Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures."],["dc.description.sponsorship","Deutsches Zentrum für Herz-Kreislaufforschung \t https://doi.org/10.13039/100010447"],["dc.identifier.doi","10.1177/0962280218780538"],["dc.identifier.eissn","1477-0334"],["dc.identifier.issn","0962-2802"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77336"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0334"],["dc.relation.issn","0962-2802"],["dc.title","Group sequential designs with robust semiparametric recurrent event models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2326"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Statistical Methods in Medical Research"],["dc.bibliographiccitation.lastpage","2347"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Mütze, Tobias"],["dc.contributor.author","Glimm, Ekkehard"],["dc.contributor.author","Schmidli, Heinz"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2020-12-10T18:38:28Z"],["dc.date.available","2020-12-10T18:38:28Z"],["dc.date.issued","2019-08"],["dc.description.abstract","Count data and recurrent events in clinical trials, such as the number of lesions in magnetic resonance imaging in multiple sclerosis, the number of relapses in multiple sclerosis, the number of hospitalizations in heart failure, and the number of exacerbations in asthma or in chronic obstructive pulmonary disease (COPD) are often modeled by negative binomial distributions. In this manuscript, we study planning and analyzing clinical trials with group sequential designs for negative binomial outcomes. We propose a group sequential testing procedure for negative binomial outcomes based on Wald statistics using maximum likelihood estimators. The asymptotic distribution of the proposed group sequential test statistics is derived. The finite sample size properties of the proposed group sequential test for negative binomial outcomes and the methods for planning the respective clinical trials are assessed in a simulation study. The simulation scenarios are motivated by clinical trials in chronic heart failure and relapsing multiple sclerosis, which cover a wide range of practically relevant settings. Our research assures that the asymptotic normal theory of group sequential designs can be applied to negative binomial outcomes when the hypotheses are tested using Wald statistics and maximum likelihood estimators. We also propose two methods, one based on Student’s t-distribution and one based on resampling, to improve type I error rate control in small samples. The statistical methods studied in this manuscript are implemented in the R package gscounts, which is available for download on the Comprehensive R Archive Network (CRAN)."],["dc.description.sponsorship","DZHK (German Centre for Cardiovascular Research)"],["dc.identifier.doi","10.1177/0962280218773115"],["dc.identifier.eissn","1477-0334"],["dc.identifier.issn","0962-2802"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77335"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0334"],["dc.relation.issn","0962-2802"],["dc.title","Group sequential designs for negative binomial outcomes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]