This special issue is based on peer‐reviewed manuscripts that were presented at the 40th Annual Conference of the International Society for Clinical Biostatistics (ISCB) in Leuven (Belgium) held in the period 14–18 July 2019 and chaired by Tomasz Burzykowski (U Hasselt, Belgium). As conference theme, Novel Aspects in Biostatistics was chosen. The conference was quite successful attracting 775 participants from 44 countries, and was one of the largest attended ISCB conferences ever. The scientific program, put together by the Scientific Program Committee chaired by Emmanuel Lesaffre (KU Leuven and U Hasselt, Belgium), consisted of eight preconference courses, two keynote presentations, eight invited sessions, 224 contributed talks, and 274 posters. A call was organized to submit high‐quality manuscripts for a special issue in this journal of any subject relevant in biostatistics, but there was a focus on the following five topics: causal inference and mediation analysis, surrogate marker research, new developments in Bayesian clinical trial methodology, high dimensional biostatistical data, and recent developments in survival models.

There were 22 submissions evaluated by five special guest editors (in alphabetic order): Ariel Alonso Abad (KU Leuven, Belgium), Hélène Jacqmin‐Gadda (Université de Bordeaux, France), Theis Lange (University of Copenhagen, Denmark), Emmanuel Lesaffre (KU Leuven and U Hasselt, Belgium), Gary Rosner (Johns Hopkins University), and Roula Tsonaka (Leiden University Medical Center, The Netherlands). Thirteen manuscripts were accepted after peer review with first authors from nine different countries.

A tribute to Doug Altman: An enthusiastic visionary biostatistician and a warm personality

At the meeting, a special session was organized honoring Doug Altman, who sadly passed away on June 3, 2018. Doug was a regular visitor of ISCB meetings and inspired many fellow biostatisticians, but also clinical researchers. Colleagues and friends of Doug were invited to write a tribute to him. Willi Sauerbrei and six long‐standing friends and colleagues of Doug Altman summarized parts of Doug's contributions to regression modeling, reporting, and prognosis research, as well as some more general issues. Of course, it is impossible to cover in one paper the whole spectrum of the methodological output of this visionary leader who drove critical appraisal and improvements in the quality of methodological and medical research during the last 40 years.

The remaining contributions to this special issue deal with popular topics presented at ISCB meetings in the last decade. Namely, topics on the design and analysis of clinical trials, models in survival analysis with a focus on joint modeling, the treatment of missing data, and meta‐analyses.

Novel Bayesian developments in clinical trials

Yan et al. discuss the practical issue of designing a pilot study to aid developing a full‐scale sequential multiple assignment randomized trial (SMART). The authors focus on the precision of the effect of a dynamic treatment regime as the objective. They consider different outcome types and use the half‐width of a confidence interval as their measure of precision for the purpose of study design, rather than traditional power. They provide formulas allowing computation of sample sizes in a two‐stage SMART and demonstrate its performance by simulations.

Cantagello and colleagues propose a new measure of treatment effect in a clinical trial involving competing risks. It is well known that competing risks complicate quantification of differences between treatment groups, because efficient test methods building on cause‐specific hazards do not directly provide an easily interpretable effect measure. The novel methods proposed by Cantagallo et al. fill this gap in methodology. The methods and their performance are explored using simulations. The paper includes an illustration in oncology studies.

In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. Such a problem entails modeling multivariate drug responses on high‐dimensional molecular feature sets in typically >1000 cell lines. Munch et al. propose to model the drug responses through a linear regression with shrinkage enforced through a normal inverse Gaussian prior incorporating external information. Model parameters are estimated using an empirical‐variational Bayes framework. Their approach is applied to publicly available Genomics of Drug Sensitivity in Cancer data.

Recent developments in survival analysis

A popular topic in survival analysis is the joint analysis of survival times and longitudinal data. Joint modeling of the two sources of information can better deal with time‐varying covariates and with missing‐not‐at random mechanisms. Spreafico and Ieva describe a joint modeling approach exploiting time‐varying covariates for dynamic monitoring of the effects of adherence to medication on survival in heart failure patients. Their approach to study treatment adherence is different from a classical approach that considers adherence as time‐fixed variables. The novelty is that it allows real‐time monitoring of patient adherence and individual prediction of health outcomes. The second contribution to joint modeling is by Böhnstedt and colleagues who model interval counts of recurrent events and death. Their joint frailty model is useful to account for possible dependent censoring when the terminal event and the recurrent processes are associated or to investigate the relationship between the two processes. With a piecewise constant baseline risk, they estimate regression coefficients and model parameters by marginal likelihood and provide a score test to evaluate the association between the two processes. The third contribution in survival analysis is by Syriopoulou, Rutherford, and Lambert. The authors propose a generalization of classic mediation analysis to include relative survival with a special focus on cancer research. The proposed method does not need any new causal/structural assumptions compared to a traditional mediation analysis, but does permit more clinically relevant interpretations.

The statistical analysis of data in the presence of missing data

For many decades, the analysis of data in the presence of missing data has intrigued statisticians, and this is not different at ISCB meetings. De Silva et al. conduct a comparison of multiple imputation methods implemented in STATA for handling missing values in longitudinal studies with sampling weights. The authors focus on how to incorporate sampling weights or design variables and compare imputation methods by simulations. They recommend multivariate normal imputation with the design stratum as a covariate in the imputation model. Faucheux et al. present methodology for clustering with both missing and left‐censored data with an unknown number of clusters. The methodology was evaluated by means of simulations, while considering various missing‐data handling methods, including multiple imputation, single imputation, and complete‐case analysis.

Meta‐analyses and network meta‐analyses

The special issue ends with four contributions on meta‐analyses. Hamaguchi et al. look at the frequentist performance of Bayesian prediction intervals for random‐effects meta‐analysis. The authors consider 11 noninformative prior distributions for the between‐study variance in their simulation study and in their analyses of eight published meta‐analyses. They conclude that the frequentist coverage is not well maintained with predictive intervals when there are fewer than 10 studies in the meta‐analysis. Verde suggests a new Bayesian hierarchical model, called the bias‐corrected meta‐analysis model, to combine different study types in meta‐analysis. The model is based on a mixture of two random effects distributions, where the first component corresponds to the model of interest and the second component to the hidden bias structure. His approach addresses the hurdle that, when combining disparate evidence in a meta‐analysis, one not only combines results of interest but also multiple biases. The novel model is illustrated on a meta‐analysis to assess effectiveness of vaccination to prevent invasive pneumococcal disease and on the effectiveness of stem cell treatment in heart disease patients. His results show that ignoring internal validity bias in a meta‐analysis may lead to misleading conclusions. Sofeu, Emura, and Rondeau propose a method for the meta‐analytic validation of failure‐time surrogate endpoints in clinical trials. The meta‐analytic approach for the evaluation of surrogate endpoints requires fitting of complex hierarchical models. These models are often fitted in two steps, which leads to estimation issues. When both the surrogate and true endpoints are failure times, the presence of censoring adds to the complexity of the problem. The authors suggest a one‐step method based on a joint frailty‐copula model to overcome the issues encountered with previous approaches. Their model includes two correlated random effects for treatment‐by‐trial interaction and a shared random effect associated with the baseline risks. At the individual level, the joint survivor functions of failure‐time endpoints are linked using copula functions. Estimation is based on a semiparametric penalized likelihood approach. Finally, the contribution of Rücker, Schmitz, and Schwarzer deals with network meta‐analysis. Such a meta‐analysis usually requires a connected network. In case of a disconnected network one may add evidence from nonrandomized comparisons, using propensity score or matching‐adjusted indirect comparisons methods. However, such nonrandomized comparisons may be associated with an unclear risk of bias. Rücker, Schmitz, and Schwarzer present a re‐analysis of a network meta‐analysis performed by Schmitz et al. on treatments for multiple myeloma. These authors used single‐arm observational studies for bridging the gap between two disconnected networks. Here, a component network meta‐analysis (CNMA) is proposed entirely based on RCTs. Such an approach makes use of the fact that many of the treatments consisted of common treatment components occurring in both networks. The authors argue that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model.

In summary, the present special issue reflects the high quality of contributions to the ISCB meeting held in Leuven in 2019. We thank all of the authors who contributed their work. We also thank all associate editors, the referees of the papers, and the editor of this journal for his guidance throughout the referee process.

中文翻译：

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生物统计学新方面特刊的社论

本期特刊基于同行评审手稿，这些手稿于 2019 年 7 月 14 日至 18 日在鲁汶（比利时）举行的国际临床生物统计学会 (ISCB) 第 40 届年会上发表，由 Tomasz Burzykowski (U Hasselt ， 比利时）。作为会议主题，生物统计学的新方面被选中。此次会议非常成功，吸引了来自 44 个国家的 775 名与会者，是有史以来参加人数最多的 ISCB 会议之一。该科学计划由 Emmanuel Lesaffre（比利时鲁汶大学和 U Hasselt）主持的科学计划委员会制定，包括八门会前课程、两场主题演讲、八场特邀会议、224 场演讲和 274 张海报。组织了一次电话会议，要求为本期刊任何与生物统计学相关主题的特刊提交高质量的手稿，但重点关注以下五个主题：因果推断和中介分析、替代标记研究、贝叶斯临床的新发展试验方法、高维生物统计数据和生存模型的最新发展。

五位特邀编辑（按字母顺序）对 22 份提交进行了评估：Ariel Alonso Abad（比利时鲁汶大学）、Hélène Jacqmin-Gadda（法国波尔多大学）、Theis Lange（丹麦哥本哈根大学）、Emmanuel Lesaffre （KU Leuven 和 U Hasselt，比利时）、Gary Rosner（约翰霍普金斯大学）和 Roula Tsonaka（荷兰莱顿大学医学中心）。在与来自 9 个不同国家的第一作者进行同行评审后，13 篇手稿被接受。

向道格·奥特曼致敬：一位热情有远见的生物统计学家和热情的个性

在会议上，组织了一场特别会议，以纪念于 2018 年 6 月 3 日不幸去世的道格·奥特曼。道格是 ISCB 会议的常客，激励了许多生物统计学家同行，也激励了临床研究人员。道格的同事和朋友受邀为他写信悼念。Willi Sauerbrei 和 Doug Altman 的六位老朋友和同事总结了 Doug 对回归建模、报告和预后研究的部分贡献，以及一些更普遍的问题。当然，在过去 40 年里，这位有远见的领导者推动了方法论和医学研究质量的批判性评估和改进，不可能在一篇论文中涵盖整个方法论产出的范围。

本期特刊的其余稿件涉及过去十年在 ISCB 会议上提出的热门话题。即，关于临床试验的设计和分析、以联合建模为重点的生存分析模型、缺失数据的处理和荟萃分析的主题。

临床试验中贝叶斯的新进展

严等人。讨论设计试点研究以帮助开发全面的顺序多重分配随机试验 (SMART) 的实际问题。作者以动态治疗方案的效果精确度为目标。他们考虑不同的结果类型，并使用置信区间的半宽作为研究设计目的的精确度度量，而不是传统的功效。他们提供了允许在两阶段 SMART 中计算样本大小的公式，并通过模拟证明其性能。

Cantagello 及其同事在一项涉及竞争风险的临床试验中提出了一种新的治疗效果衡量标准。众所周知，竞争风险使治疗组之间差异的量化变得复杂，因为建立在特定原因危害的有效测试方法并不能直接提供易于解释的效果测量。Cantagallo 等人提出的新方法。填补方法论上的这一空白。使用模拟来探索这些方法及其性能。该论文包括肿瘤学研究中的插图。

在精准医学中，一个常见的问题是从癌细胞组织细胞系中预测药物敏感性。这样的问题需要在通常 > 1000 个细胞系的高维分子特征集上对多元药物反应进行建模。蒙克等人。建议通过线性回归对药物反应进行建模，并在合并外部信息之前通过正态逆高斯强制执行收缩。使用经验变分贝叶斯框架估计模型参数。他们的方法应用于公开可用的癌症数据中药物敏感性基因组学。

生存分析的最新进展

生存分析中的一个热门话题是生存时间和纵向数据的联合分析。两种信息源的联合建模可以更好地处理随时间变化的协变量和缺失的非随机机制。Spreafico 和 Ieva 描述了一种联合建模方法，利用随时间变化的协变量动态监测依从性对心力衰竭患者生存率的影响。他们研究治疗依从性的方法不同于将依从性视为时间固定变量的经典方法。新颖之处在于它允许实时监控患者依从性和个人健康结果预测。Böhnstedt 及其同事对联合建模的第二个贡献是对复发事件和死亡的间隔计数进行建模。他们的联合脆弱性模型有助于解释当终止事件和循环过程相关时可能的依赖审查或调查两个过程之间的关系。使用分段恒定基线风险，他们通过边际似然估计回归系数和模型参数，并提供评分测试来评估两个过程之间的关联。生存分析的第三个贡献是 Syriopoulou、Rutherford 和 Lambert。作者建议将经典中介分析推广到包括相对生存期，并特别关注癌症研究。与传统的中介分析相比，所提出的方法不需要任何新的因果/结构假设，但允许更多的临床相关解释。

存在缺失数据时的数据统计分析

几十年来，在存在缺失数据的情况下对数据进行分析引起了统计学家的兴趣，这在 ISCB 会议上也没有什么不同。德席尔瓦等。对 STATA 中实施的多种插补方法进行比较，以处理具有抽样权重的纵向研究中的缺失值。作者专注于如何合并抽样权重或设计变量，并通过模拟比较插补方法。他们推荐使用设计层作为插补模型中的协变量的多变量正态插补。Faucheux 等人。目前的聚类方法是对未知聚类数的缺失数据和左删失数据进行聚类。该方法通过模拟进行评估，同时考虑了各种缺失数据处理方法，包括多重插补、单一插补和完整案例分析。

元分析和网络元分析

特刊以四篇关于荟萃分析的贡献结束。滨口等人。看看贝叶斯预测区间在随机效应荟萃分析中的频率表现。作者在他们的模拟研究和他们对八项已发表的荟萃分析的分析中考虑了 11 个非信息性先验分布的研究间方差。他们得出的结论是，当荟萃分析中的研究少于 10 项时，频率论的覆盖率不能很好地保持在预测区间。Verde 提出了一种新的贝叶斯分层模型，称为偏差校正的荟萃分析模型，以在荟萃分析中结合不同的研究类型。该模型基于两个随机效应分布的混合，其中第一个分量对应于感兴趣的模型，第二个分量对应于隐藏的偏差结构。他的方法解决了一个障碍，即在荟萃分析中结合不同的证据时，不仅结合了感兴趣的结果，而且结合了多种偏见。新模型在荟萃分析中得到说明，以评估疫苗接种预防侵袭性肺炎球菌疾病的有效性以及干细胞治疗心脏病患者的有效性。他的结果表明，在荟萃分析中忽略内部效度偏差可能会导致误导性结论。Sofeu、Emura 和 Rondeau 提出了一种在临床试验中对失败时间替代终点进行元分析验证的方法。用于评估替代终点的元分析方法需要拟合复杂的分层模型。这些模型通常分两步拟合，这会导致估计问题。当代理和真实端点都是失败时间时，审查的存在增加了问题的复杂性。作者提出了一种基于联合脆弱性-copula 模型的一步法来克服以前方法遇到的问题。他们的模型包括两个相关的试验相互作用的随机效应和一个与基线风险相关的共享随机效应。在个体层面，故障时间端点的联合幸存者函数使用 copula 函数链接。估计基于半参数惩罚似然方法。最后，Rücker、Schmitz 和 Schwarzer 的贡献涉及网络元分析。这种荟萃分析通常需要一个连接的网络。在网络断开的情况下，可以从非随机比较中添加证据，使用倾向评分或匹配调整的间接比较方法。然而，这种非随机比较可能与不明确的偏倚风险相关。Rücker、Schmitz 和 Schwarzer 对 Schmitz 等人进行的网络荟萃分析进行了重新分析。关于多发性骨髓瘤的治疗。这些作者使用单臂观察性研究来弥合两个断开连接的网络之间的差距。在这里，完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。这种非随机比较可能与不明确的偏倚风险有关。Rücker、Schmitz 和 Schwarzer 对 Schmitz 等人进行的网络荟萃分析进行了重新分析。关于多发性骨髓瘤的治疗。这些作者使用单臂观察性研究来弥合两个断开连接的网络之间的差距。在这里，完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。这种非随机比较可能与不明确的偏倚风险有关。Rücker、Schmitz 和 Schwarzer 对 Schmitz 等人进行的网络荟萃分析进行了重新分析。关于多发性骨髓瘤的治疗。这些作者使用单臂观察性研究来弥合两个断开连接的网络之间的差距。在这里，完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。Schwarzer 对 Schmitz 等人进行的网络荟萃分析进行了重新分析。关于多发性骨髓瘤的治疗。这些作者使用单臂观察性研究来弥合两个断开连接的网络之间的差距。在这里，完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。Schwarzer 对 Schmitz 等人进行的网络荟萃分析进行了重新分析。关于多发性骨髓瘤的治疗。这些作者使用单臂观察性研究来弥合两个断开连接的网络之间的差距。在这里，完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。完全基于 RCT 提出了组件网络荟萃分析 (CNMA)。这种方法利用了这样一个事实，即许多治疗由两个网络中出现的共同治疗成分组成。作者认为，研究人员遇到具有共同组件的不同子网中处理的断开网络时，应考虑使用 CNMA 模型。

总之，本期特刊反映了对 2019 年在鲁汶举行的 ISCB 会议的高质量贡献。我们感谢所有贡献其工作的作者。我们还要感谢所有的副主编、论文的审稿人和本期刊的编辑在整个审稿过程中的指导。