The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomised controlled trials have tried to answer this question, but these can, by definition, only analyse a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different pre-specified timing strategies for renal replacement therapy initiation based on time-updated levels of serum potassium, pH and fluid balance in critically ill patients with AKI with the aim to minimize 30-day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a non-parametric one - using an inverse probability weighted Aalen-Johansen estimator - and a semiparametric one - using dynamic-regime marginal structural models. Furthermore, we suggest an easy to implement cross-validation technique that can be used for the out-of-sample performance assessment of the optimal dynamic treatment regime. Our work illustrates the potential of data-driven medical decision support based on routinely collected observational data.
Sensitivity Analysis of Treatment Effect to Unmeasured Confounding in Observational Studies with Survival and Competing Risks Outcomes
当使用倾向得分和逆概率加权等方法时，在估计观察性数据的处理效应时，通常假设没有未测量的混杂因素。然而，在许多这样的研究中，由于数据库的限制，收集的混杂因素并不详尽，关键是要检查所得到的估计值对未测量的混杂因素的敏感程度。我们考虑生存和竞争风险数据的这个问题。由于此类数据模型的复杂性，我们采用了Carnegie et al.（2016）的模拟潜在混杂因素方法，该方法为未测量混杂因素的敏感性分析提供了通用工具。更具体地说，我们指定一个敏感性参数来量化未测量的混杂因素与治疗分配之间的关联，另一组参数来量化混杂因素与事件发生时间结局之间的关联。通过改变灵敏度参数的大小，我们使用随机EM和期望最大化算法来估计感兴趣的处理效应。我们证明了我们的方法在模拟数据上的性能，并将其应用于炎症性肠病（IBD）的比较有效性研究。
A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Department of Statistics, TU Dortmund University（图恩-多特蒙德大学统计学系）；Research Center Trustworthy Data Science and Security, University Alliance Ruhr (UA Ruhr)（鲁尔大学联盟可信数据科学与安全研究中心）；Institute for Mathematics, University of Augsburg（艾希施泰特大学数学研究所）
tteICE: An R Package for Estimating Treatment Effects on Time-to-Event Outcomes with Intercurrent Events in Two-Arm Trials
Kobe University（神户大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院 北京协和医学院）
Bayesian model selection in the $\mathcal{M}$-open setting -- Approximate posterior inference and probability-proportional-to-size subsampling for efficient large-scale leave-one-out cross-validation
Predicting Individualized Effects of Internet-Based Treatment for Genito-Pelvic Pain/Penetration Disorder: Development and Internal Validation of a Multivariable Decision Tree Model
Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation
Thurgau Institute for Digital Transformation (TIDIT)（图尔豪数字转型研究所）；HTWG Konstanz – University of Applied Sciences Konstanz（康斯坦茨应用科学大学）；Department of Neurology, University Hospital Zurich (USZ)（苏黎世大学神经科部）；University of Zurich (UZH)（苏黎世大学）
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Case Western Reserve University（凯斯西储大学）；University of Mons（蒙斯大学）；Cleveland Clinic（克利夫兰诊所）；Yale School of Medicine（耶鲁医学院）；Institut Jules Bordet（朱尔斯·博尔代研究所）；University Hospitals Cleveland Medical Center（大学医院克利夫兰医学中心）；Louis Stokes Cleveland VA Medical Center（路易斯·斯托克斯克利夫兰退伍军人事务医疗中心）
预测对新辅助治疗的反应是乳腺癌中一个令人烦恼的挑战。在这项研究中，我们评估了深度学习从治疗前获得的治疗前动态对比增强（DCE）MRI预测对HER 2靶向新辅助化疗（NAC）的反应的能力。在一项回顾性研究中，包括来自5家机构的157名HER 2+乳腺癌患者的DCE-MRI数据，我们开发并验证了一种深度学习方法，用于预测治疗前对HER 2靶向NAC的病理完全缓解（pCR）。在单一机构接受HER 2靶向新辅助化疗的100名患者用于训练（n=85）和调整（n=15）卷积神经网络（CNN）以预测pCR。确定了利用造影前和造影后DCE-MRI采集的多输入CNN，以实现验证集内的最佳响应预测（AUC=0.93）。然后，使用治疗前DCE-MRI数据在两个独立的测试队列中对该模型进行测试。在第二家机构的28例患者测试集中（AUC=0.85，95% CI 0.67-1.0，p= 0.0008）和包括来自另外3家机构的数据的29例患者多中心试验中（AUC=0.77，95% CI 0.58-0.97，p=0.006），它实现了强大的性能。发现基于深度学习的反应预测模型超过了包含预测临床变量（测试队列中AUC < .65）和半定量DCE-MRI药代动力学测量模型（测试队列中AUC < .60）的多变量模型。这项工作在多个研究中心提出的结果表明，通过进一步验证，深度学习可以提供一种有效可靠的工具来指导乳腺癌的…
