Moritz Fabian Danzer, Andreas Faldum, Thorsten Simon, Barbara Hero, Rene Schmidt
{"title":"Confirmatory Adaptive Designs for Clinical Trials With Multiple Time-to-Event Outcomes in Multi-state Markov Models","authors":"Moritz Fabian Danzer, Andreas Faldum, Thorsten Simon, Barbara Hero, Rene Schmidt","doi":"10.1002/bimj.202300181","DOIUrl":"https://doi.org/10.1002/bimj.202300181","url":null,"abstract":"<p>The analysis of multiple time-to-event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data-dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression-free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004-HR study.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patrick B. Langthaler, Kai-Philipp Gladow, Oliver Krüger, Jonas Beck
{"title":"A Novel Method for Nonparametric Statistical Inference for Niche Overlap in Multiple Species","authors":"Patrick B. Langthaler, Kai-Philipp Gladow, Oliver Krüger, Jonas Beck","doi":"10.1002/bimj.202400013","DOIUrl":"10.1002/bimj.202400013","url":null,"abstract":"<p>The understanding of species interactions and ecosystem dynamics hinges upon the study of ecological niches. Quantifying the overlap of Hutchinsonian-niches has garnered significant attention, with many recent publications addressing the issue. Prior work on estimating niche overlap often did not provide confidence intervals or assumed multivariate normality, seriously limiting applications in ecology, and biodiversity research. This paper extends a nonparametric approach, previously applied to the two-species case, to multiple species. For estimation, a consistent plug-in estimator based on rank sums is proposed and its asymptotic distribution is derived under weak conditions. The novel methodology is then applied to a study comparing the ecological niches of the Eurasian eagle owl, common buzzard, and red kite. These species share a habitat in Central Europe but exhibit distinct population trends. The analysis explores their breeding habitat preferences, considering the intricate competition dynamics and utilizing the nonparametric approach to niche overlap estimation. Our proposed method provides a valuable inferential tool for the quantitative evaluation of differences and overlap between niches.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202400013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood","authors":"Maike Hohberg, Andreas Groll","doi":"10.1002/bimj.202300020","DOIUrl":"10.1002/bimj.202300020","url":null,"abstract":"<p>In this work, a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, via P-splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time-varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in the <span>R</span> function <span>coxlasso</span>, which is now integrated into the package <span>PenCoxFrail</span>, and will be compared to other packages for regularized Cox regression.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ping Xie, Mikael Escobar-Bach, Ingrid Van Keilegom
{"title":"Testing for Sufficient Follow-Up in Censored Survival Data by Using Extremes","authors":"Ping Xie, Mikael Escobar-Bach, Ingrid Van Keilegom","doi":"10.1002/bimj.202400033","DOIUrl":"10.1002/bimj.202400033","url":null,"abstract":"<div>\u0000 \u0000 <p>In survival analysis, it often happens that some individuals, referred to as cured individuals, never experience the event of interest. When analyzing time-to-event data with a cure fraction, it is crucial to check the assumption of “sufficient follow-up,” which means that the right extreme of the censoring time distribution is larger than that of the survival time distribution for the noncured individuals. However, the available methods to test this assumption are limited in the literature. In this article, we study the problem of testing whether follow-up is sufficient for light-tailed distributions and develop a simple novel test. The proposed test statistic compares an estimator of the noncure proportion under sufficient follow-up to one without the assumption of sufficient follow-up. A bootstrap procedure is employed to approximate the critical values of the test. We also carry out extensive simulations to evaluate the finite sample performance of the test and illustrate the practical use with applications to leukemia and breast cancer data sets.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study","authors":"Antonia Zapf, Cornelia Frömke, Juliane Hardt, Gerta Rücker, Dina Voeltz, Annika Hoyer","doi":"10.1002/bimj.202300101","DOIUrl":"https://doi.org/10.1002/bimj.202300101","url":null,"abstract":"<p>The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf et al., <i>Biometrical Journal</i>. 2021; 63(4): 699–711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Post-Estimation Shrinkage in Full and Selected Linear Regression Models in Low-Dimensional Data Revisited","authors":"Edwin Kipruto, Willi Sauerbrei","doi":"10.1002/bimj.202300368","DOIUrl":"https://doi.org/10.1002/bimj.202300368","url":null,"abstract":"<p>The fit of a regression model to new data is often worse due to overfitting. Analysts use variable selection techniques to develop parsimonious regression models, which may introduce bias into regression estimates. Shrinkage methods have been proposed to mitigate overfitting and reduce bias in estimates. Post-estimation shrinkage is an alternative to penalized methods. This study evaluates effectiveness of post-estimation shrinkage in improving prediction performance of full and selected models. Through a simulation study, results were compared with ordinary least squares (OLS) and ridge in full models, and best subset selection (BSS) and lasso in selected models. We focused on prediction errors and the number of selected variables. Additionally, we proposed a modified version of the parameter-wise shrinkage (PWS) approach named non-negative PWS (NPWS) to address weaknesses of PWS. Results showed that no method was superior in all scenarios. In full models, NPWS outperformed global shrinkage, whereas PWS was inferior to OLS. In low correlation with moderate-to-high signal-to-noise ratio (SNR), NPWS outperformed ridge, but ridge performed best in small sample sizes, high correlation, and low SNR. In selected models, all post-estimation shrinkage performed similarly, with global shrinkage slightly inferior. Lasso outperformed BSS and post-estimation shrinkage in small sample sizes, low SNR, and high correlation but was inferior when the opposite was true. Our study suggests that, with sufficient information, NPWS is more effective than global shrinkage in improving prediction accuracy of models. However, in high correlation, small sample sizes, and low SNR, penalized methods generally outperform post-estimation shrinkage methods.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jan Gertheiss, David Rügamer, Bernard X. W. Liew, Sonja Greven
{"title":"Functional Data Analysis: An Introduction and Recent Developments","authors":"Jan Gertheiss, David Rügamer, Bernard X. W. Liew, Sonja Greven","doi":"10.1002/bimj.202300363","DOIUrl":"https://doi.org/10.1002/bimj.202300363","url":null,"abstract":"<p>Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael J. Cartwright, Tim Friede, David Lawrence, Emma May, Tobias Mütze, Kit Roes
{"title":"Stakeholders' Perspectives on Current Issues in Data Monitoring Committees","authors":"Michael J. Cartwright, Tim Friede, David Lawrence, Emma May, Tobias Mütze, Kit Roes","doi":"10.1002/bimj.202300384","DOIUrl":"10.1002/bimj.202300384","url":null,"abstract":"<div>\u0000 \u0000 <p>Data Monitoring Committees (DMCs) are groups of experts that review accumulating data from one or more ongoing clinical studies and advise the Sponsor regarding the continuing safety of study subjects along with the continuing validity and scientific merit of the study. Although DMCs are widely used, considerable variability exists in their conduct. This paper offers recommendations, derived from sessions given at the 2023 Central European Network International Biometric and Statisticians in the Pharmaceutical Industry Conferences' and the authors' experiences. We focus on four topics that are part of the DMC process and where there is unclarity and inconsistency in current practices: (1) Communication with the DMC—We reflect on the importance of effective, proper communication channels between the DMC and relevant stakeholders to foster collaboration and exchange of critical information while retaining study integrity throughout. (2) Open sessions—We discuss the benefits of incorporating open sessions in DMC meetings to enhance transparency, inclusivity, and the consideration of diverse perspectives, as well as pitfalls of open sessions. (3) Access to efficacy data—We highlight the need for appropriate access to efficacy data by DMCs and discuss how to implement this in practice and how to address potential concerns regarding multiplicity. (4) Interactive data displays—We outline the utilization of interactive data displays to facilitate a more intuitive understanding of study results by the DMC. By addressing these topics, we aim to provide comprehensive practical recommendations that bridge the gap between current practices and optimal DMC functionality.</p>\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudia Angelini, Daniela De Canditiis, Italia De Feis, Antonella Iuliano
{"title":"A Network-Constrain Weibull AFT Model for Biomarkers Discovery","authors":"Claudia Angelini, Daniela De Canditiis, Italia De Feis, Antonella Iuliano","doi":"10.1002/bimj.202300272","DOIUrl":"10.1002/bimj.202300272","url":null,"abstract":"<p>We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions","authors":"Rahul Ghosal, Marcos Matabuena","doi":"10.1002/bimj.202400042","DOIUrl":"10.1002/bimj.202400042","url":null,"abstract":"<p>We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are suboptimal because (i) they fail to utilize the dependence between the distributional predictors and (ii) neglect the correlation structure of the response. To overcome these limitations, we propose a multivariate distributional analysis framework that harnesses the power of multivariate density functions and multitask learning. We develop a computationally efficient semiparametric estimation method for modeling the effect of the latent joint density on the multivariate response of interest. Additionally, we introduce a new conformal prediction algorithm for quantifying the uncertainty of our multivariate predictions based on subject characteristics and individualized distributional predictors, providing valuable insights into the conditional distribution of the response. We validate the effectiveness of our proposed method through comprehensive numerical simulations, clearly demonstrating its superior performance compared to traditional methods. The application of the proposed method is demonstrated on triaxial accelerometer data from the National Health and Nutrition Examination Survey 2011–2014 for modeling the association between cognitive scores across various domains and distributional representation of physical activity among the older adult population. Our results highlight the advantages of the proposed approach, emphasizing the significance of incorporating multidimensional distributional information in the triaxial accelerometer data.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 7","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202400042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}