BIOMETRICAL JOURNAL 发表关于统计方法及其在生命科学（包括医学、环境科学和农业）中的应用的论文。方法的发展应该受到这些领域中一个有趣且相关的问题的推动。理想情况下，手稿应包括对问题的描述和详细说明将新方法应用于问题的部分。也欢迎案例研究、评论文章和给编辑的信。仅包含广泛数学理论的论文不适合在 BIOMETRICAL JOURNAL 上发表。

The Biometrical Journal publishes papers on statistical methods and their applications in the life sciences, including medicine, environmental science, and agriculture. The development of methods should be driven by interesting and relevant questions in these fields. Ideally, manuscripts should include a description of the problem and a detailed explanation of how the new methods are applied to the problem. Case studies, review articles, and letters to the editor are also welcome. Papers that only contain broad mathematical theory are not suitable for publication in the Biometrical Journal.

《Biometrical Journal》发表关于统计方法及其在生命科学（包括医学、环境科学和农业）中的应用的论文。方法的发展应当受到这些领域中有趣且相关问题的推动。理想情况下，手稿应包括对问题的描述，以及详细说明新方法如何应用于这些问题的部分。我们也欢迎案例研究、评论文章和给编辑的信件。仅包含广泛数学理论的论文不适合在《Biometrical Journal》上发表。

Biometrical Journal

Abstract Extensions of linear models are very commonly used in the analysis of biological data. Whereas goodness of fit measures such as the coefficient of determination ( R 2 ) or the adjusted R 2 are well established for linear models, it is not obvious how such measures should be defined for generalized linear and mixed models. There are by now several proposals but no consensus has yet emerged as to the best unified approach in these settings. In particular, it is an open question how to best account for heteroscedasticity and for covariance among observations present in residual error or induced by random effects. This paper proposes a new approach that addresses this issue and is universally applicable for arbitrary variance‐covariance structures including spatial models and repeated measures. It is exemplified using three biological examples.

Hans‐Peter Piepho

A coefficient of determination (<i>R</i><sup>2</sup>) for generalized linear mixed models

To study the impact of climate variables on morbidity of some diseases in Mexico, we propose a spatiotemporal varying coefficients regression model. For that we introduce a new spatiotemporal-dependent process prior, in a Bayesian context, with identically distributed normal marginal distributions and joint multivariate normal distribution. We study its properties and characterise the dependence induced. Our results show that the effect of climate variables, on the incidence of specific diseases, is not constant across space and time and our proposed model is able to capture and quantify those changes.

Luis E. Nieto‐Barajas

Bayesian regression with spatiotemporal varying coefficients

贝叶斯回归与时空变系数

Biometrical JournalVolume 20, Issue 2 p. 197-197 Book Reveiw HAKEN, H.: Synergetics. An Introduction. Springer-Verlag, Berlin-Heidelberg-New York 1977. XII, 325 S., 125 Abb., DM 72.—. K. SteinmÜLler, K. SteinmÜLlerSearch for more papers by this author K. SteinmÜLler, K. SteinmÜLlerSearch for more papers by this author First published: 1978 https://doi.org/10.1002/bimj.4710200213Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume20, Issue21978Pages 197-197 RelatedInformation

K. SteinmÜLler

HAKEN, H.: Synergetics. An Introduction. Springer‐Verlag, Berlin‐Heidelberg‐New York 1977. XII, 325 S., 125 Abb., DM 72.—.

哈肯：《协同学导论》。斯普林格出版社，柏林-海德堡-纽约，1977年。XII，325页，125幅图，72马克。

The projection of age‐stratified cancer incidence and mortality rates is of great interest due to demographic changes, but also therapeutical and diagnostic developments. Bayesian age–period–cohort (APC) models are well suited for the analysis of such data, but are not yet used in routine practice of epidemiologists. Reasons may include that Bayesian APC models have been criticized to produce too wide prediction intervals. Furthermore, the fitting of Bayesian APC models is usually done using Markov chain Monte Carlo (MCMC), which introduces complex convergence concerns and may be subject to additional technical problems. In this paper we address both concerns, developing efficient MCMC‐free software for routine use in epidemiological applications. We apply Bayesian APC models to annual lung cancer data for females in five different countries, previously analyzed in the literature. To assess the predictive quality, we omit the observations from the last 10 years and compare the projections with the actual observed data based on the absolute error and the continuous ranked probability score. Further, we assess calibration of the one‐step‐ahead predictive distributions. In our application, the probabilistic forecasts obtained by the Bayesian APC model are well calibrated and not too wide. A comparison to projections obtained by a generalized Lee–Carter model is also given. The methodology is implemented in the user‐friendly R‐package BAPC using integrated nested Laplace approximations.

Andrea Riebler

Leonhard Held

Biom. J.

Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations

预测未来癌症负担：基于贝叶斯年龄-时期-队列分析与集成嵌套拉普拉斯近似

ABSTRACT In the individual stepped‐wedge randomized trial (ISW‐RT), subjects are allocated to sequences, each sequence being defined by a control period followed by an experimental period. The total follow‐up time is the same for all sequences, but the duration of the control and experimental periods varies among sequences. To our knowledge, there is no validated sample size calculation formula for ISW‐RTs unlike stepped‐wedge cluster randomized trials (SW‐CRTs). The objective of this study was to adapt the formula used for SW‐CRTs to the case of individual randomization and to validate this adaptation using a Monte Carlo simulation study. The proposed sample size calculation formula for an ISW‐RT design yielded satisfactory empirical power for most scenarios except scenarios with operating characteristic values near the boundary (i.e., smallest possible number of periods, very high or very low autocorrelation coefficient). Overall, the results provide useful insights into the sample size calculation for ISW‐RTs.

Aude Allemang‐Trivalle

Annabel Maruani

Bruno Giraudeau

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

个体阶梯楔形随机试验的样本量计算

ABSTRACT There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed superiority. In such trials, it can be important to ensure that error rates are controlled. This paper introduces a multi‐stage design that enables the addition of new treatment arms, at any point, in a preplanned manner within a platform trial, while still maintaining control over the family‐wise error rate. This paper focuses on finding the required sample size to achieve a desired level of statistical power when treatments are continued to be tested even after a superior treatment has already been found. This may be of interest if there are treatments from different sponsors which are also superior to the current control or multiple doses being tested. The calculations to determine the expected sample size is given. A motivating trial is presented in which the sample size of different configurations is studied. In addition, the approach is compared to running multiple separate trials and it is shown that in many scenarios if family‐wise error rate control is needed there may not be benefit in using a platform trial when comparing the sample size of the trial.

Peter Greenstreet

Thomas Jaki

Alun Bedding

Pavel Mozgunov

A Preplanned Multi‐Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power

一项预先规划的多阶段平台试验，用于在控制家族错误率和功效的情况下发现多种优越治疗方法

Gene set analysis, a popular approach for analyzing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditions. In addition to the multitude of methods available for this task, users are typically left with many options when creating the required input and specifying the internal parameters of the chosen method. This flexibility can lead to uncertainty about the "right" choice, further reinforced by a lack of evidence-based guidance. Especially when their statistical experience is scarce, this uncertainty might entice users to produce preferable results using a "trial-and-error" approach. While it may seem unproblematic at first glance, this practice can be viewed as a form of "cherry-picking" and cause an optimistic bias, rendering the results nonreplicable on independent data. After this problem has attracted a lot of attention in the context of classical hypothesis testing, we now aim to raise awareness of such overoptimism in the different and more complex context of gene set analyses. We mimic a hypothetical researcher who systematically selects the analysis variants yielding their preferred results, thereby considering three distinct goals they might pursue. Using a selection of popular gene set analysis methods, we tweak the results in this way for two frequently used benchmark gene expression data sets. Our study indicates that the potential for overoptimism is particularly high for a group of methods frequently used despite being commonly criticized. We conclude by providing practical recommendations to counter overoptimism in research findings in gene set analysis and beyond.

Milena Wünsch

Christina Sauer

Moritz Herrmann

Ludwig Christian Hinske

Anne‐Laure Boulesteix

To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism

调整还是不调整：基因集分析中灵活性的利用如何导致过度乐观

ABSTRACT In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person‐specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allow to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA data set. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data.

Maren Hackenberg

Michelle Pfaffenlehner

Max Behrens

Astrid Pechmann

Janbernd Kirschner

Harald Binder

Investigating a Domain Adaptation Approach for Integrating Different Measurement Instruments in a Longitudinal Clinical Registry

研究一种领域自适应方法以整合纵向临床注册中不同测量工具

ABSTRACT The progression‐free‐survival ratio is a popular endpoint in oncology trials, which is frequently applied to evaluate the efficacy of molecularly targeted treatments in late‐stage patients. Using elementary calculations and simulations, numerous shortcomings of the current methodology are pointed out. As a remedy to these shortcomings, an alternative methodology is proposed, using a marginal Cox model or a marginal accelerated failure time model for clustered time‐to‐event data. Using comprehensive simulations, it is shown that this methodology outperforms existing methods in settings where the intrapatient correlation is low to moderate. The performance of the model is further demonstrated in a real data example from a molecularly aided tumor trial. Sample size considerations are discussed.

Dominic Edelmann

Tobias Terzer

Peter Horak

Richard Schlenk

Axel Benner

The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

分子辅助肿瘤试验中的无进展生存率比：对当前实践的批判性审视及替代方法的建议

ABSTRACT In biomedical studies, investigators often encounter clustered data. The cluster sizes are said to be informative if the outcome depends on the cluster size. Ignoring informative cluster sizes in the analysis leads to biased parameter estimation in marginal and mixed‐effect regression models. Several methods to analyze data with informative cluster sizes have been proposed; however, methods to test the informativeness of the cluster sizes are limited, particularly for the marginal model. In this paper, we propose a score test and a Wald test to examine the informativeness of the cluster sizes for a generalized linear model, a Cox model, and a proportional subdistribution hazards model. Statistical inference can be conducted through weighted estimating equations. The simulation results show that both tests control Type I error rates well, but the score test has higher power than the Wald test for right‐censored data while the power of the Wald test is generally higher than the score test for the binary outcome. We apply the Wald and score tests to hematopoietic cell transplant data and compare regression analysis results with/without adjusting for informative cluster sizes.

Soyoung Kim

Michael J. Martens

Kwang Woo Ahn

Test Statistics and Statistical Inference for Data With Informative Cluster Sizes

信息集群规模数据的检验统计量与统计推断

Abbreviation	Biom. J.
Journal Impact	1.40
Quartiles(Global)	STATISTICS & PROBABILITY(Q2)

ISSN	0323-3847, 1521-4036
h-index	75

Publication Information	Publisher: Wiley-VCH Verlag，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1977，Proportion of original research papers: 100.00%，Self Citation Rate:15.40%， Gold OA Rate: 38.24%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：容易

Biometrical Journal

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A coefficient of determination (R²) for generalized linear mixed models

Bayesian regression with spatiotemporal varying coefficients

HAKEN, H.: Synergetics. An Introduction. Springer‐Verlag, Berlin‐Heidelberg‐New York 1977. XII, 325 S., 125 Abb., DM 72.—.

Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

A Preplanned Multi‐Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power

To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism

Investigating a Domain Adaptation Approach for Integrating Different Measurement Instruments in a Longitudinal Clinical Registry

The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

Test Statistics and Statistical Inference for Data With Informative Cluster Sizes

Biometrical Journal

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

A coefficient of determination (R2) for generalized linear mixed models

Bayesian regression with spatiotemporal varying coefficients

HAKEN, H.: Synergetics. An Introduction. Springer‐Verlag, Berlin‐Heidelberg‐New York 1977. XII, 325 S., 125 Abb., DM 72.—.

Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

A Preplanned Multi‐Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power

To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism

Investigating a Domain Adaptation Approach for Integrating Different Measurement Instruments in a Longitudinal Clinical Registry

The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

Test Statistics and Statistical Inference for Data With Informative Cluster Sizes

A coefficient of determination (R²) for generalized linear mixed models