尽管响应自适应随机化（RAR）在文献中获得了大量关注，但其在临床试验中的应用仍然有限。除其他原因外，在真实世界试验中实施 RAR 引发了重要的实际问题，而这些问题在技术文献中常被忽视。受一项创新的 II 期分层 RAR 罕见病试验启发，本文解决了两个挑战：（1）如何确保 RAR 分配是理想的，即既可接受又忠实于预期概率，特别是在小样本情况下？以及（2）在存在缺失数据时，期中分析后应触发何种适应性调整？针对问题（1），我们提出了一种映射策略，将随机化概率离散化为分配比率向量，从而改善了频率主义误差。在实施映射策略的基础上，我们通过分析选定场景中缺失数据对操作特性的影响来回答问题（2）。最后，我们讨论了其他关注点，包括：跨试验分层合并数据、分析试验中的盲法水平以及报告安全性结果。
英文摘要：
Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in real world trials raises important practical questions, often neglected in the technical literature. Motivated by an innovative phase-II stratified RAR rare-disease trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are desirable i.e. both acceptable and faithful to the intended probabilities, particularly in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? To answer (1), we propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we answer (2) by analysing the impact of missing data on operating characteristics in selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.
Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined precision gains from these two methods, which can lead to wasted resources in terms of sample size and trial duration. We derive consistency and asymptotic normality of model-robust estimators that combine these two methods, and show that these estimators can lead to substantial gains in precision and power. Our theorems cover a class of estimators that handle continuous, binary, and time-to-event outc…
Data-Adaptive and Model-Robust Covariate Adjustment for Time-to-Event Outcomes in Stratified Randomized Trials
Cornell Tech, Cornell University（康奈尔科技校区，康奈尔大学）；Department of Biostatistics, Department of Population Health, New York University School of Medicine（纽约大学医学院生物统计学系、人口健康系）
Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements may be evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomised two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritising participants at high risk of infection, can result in s…
Conventional methods for analyzing composite endpoints in clinical trials often only focus on the time to the first occurrence of all events in the composite. Therefore, they have inherent limitations because the individual patients' first event can be the outcome of lesser clinical importance. To overcome this limitation, the concept of the win ratio (WR), which accounts for the relative priorities of the components and gives appropriate priority to the more clinically important event, was examined. For example, because mortality has a higher priority than hospitalization, it is reasonable to…
The past few years have seen an increasing number of initiatives aimed at integrating information generated outside of confirmatory randomised clinical trials (RCTs) into drug development. However, data generated non-concurrently and through observational studies can provide results that are difficult to compare with randomised trial data. Moreover, the scientific questions these data can serve to answer often remain vague. Our starting point is to use clearly defined objectives for evidence generation, which are formulated towards early discussion with health technology assessment (HTA) bodie…
Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno-oncology. Many novel statistical methods that aim to efficiently capture potential long-term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most (if not all) large confirmatory studies currently employ a stratified primary analysis. In this article, we combine recently proposed weighted log-rank tests that have been designed to work well under a delayed separation of survival curves, wit…
Aims: Combinations of treatments can offer additional benefit over the treatments individually. However, trials of these combinations are lower priority than the development of novel therapies, which can restrict funding, timelines and patient availability. This paper develops a novel trial design to facilitate the evaluation of novel combination therapies that combines elements of phase II and phase III trials. Methods: This trial uses response adaptive randomisation to increase the information collected about successful novel drug combinations and Bayesian dose-response modelling to undertak…
In a stratified clinical trial design with time to event end points, stratification factors are often accounted for the log-rank test and the Cox regression analyses. In this work, we have evaluated the impact of inclusion of stratification factors on the power of the stratified log-rank test and have compared the bias and standard error in HR estimate between multivariate and stratified Cox regression analyses through simulation. Results from our investigation suggests that both failing to consider stratification factor in presence of their prognostic effect and stratification with smaller nu…
In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. The traditional RAR strategy alters the randomization ratio on a patient-by-patient basis; this has been heavily criticized for bias due to time-trends. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; the final analysis is then stratified by block. However, the typical blocked RAR design divides patients into equal-sized blocks, which is…
The primary endpoint in oncology is usually overall survival, where differences between therapies may only be observable after many years. To avoid withholding of a promising therapy, preliminary approval based on a surrogate endpoint is possible. The approval can be confirmed later by assessing overall survival within the same study. In these trials, the correlation between surrogate endpoint and overall survival has to be taken into account for sample size calculation and analysis. For a binary surrogate endpoint, this relation can be modeled by means of the responder stratified exponential …
A robust score test in g-computation for covariate adjustment in randomized clinical trials leveraging different variance estimators via influence functions
Data Sciences and Analytics, Pfizer Inc（辉瑞公司数据科学与分析部）；Division of Biostatistics and Health Data Science, University of Minnesota（明尼苏达大学生物统计学与健康数据科学系）；Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University（上海交通大学自然科学研究院、教育部拉曼森数学中心、数学科学学院、上海数学中心及上交-耶鲁生物统计与数据科学联合中心）
A Novel Stratified Analysis Method for Testing and Estimating Overall Treatment Effects on Time-to-Event Outcomes Using Average Hazard with Survival Weight
Many phase II clinical trials have used survival outcomes as the primary endpoints in recent decades. Suppose the radiotherapy is evaluated in a phase II trial using survival outcomes. In that case, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, much literature has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-dose-fit-all" strategy often fails, and it is m…
Erasmus MC, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟医学中心）；Delft University of Technology（代尔夫特理工大学）
Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different st…
The RobinCar Family: R Tools for Robust Covariate Adjustment in Randomized Clinical Trials
University of Washington（华盛顿大学）；Global Statistical Sciences, Eli Lilly and Company（礼来公司全球统计科学部）；BARDS, MSD（默沙东 BARDS）；AstraZeneca（阿斯利康）；RCONIS；Gilead Sciences（吉利德科学）
Nesting a Target Study within a Target Trial: A Framework for Evaluating Intervention Effects on Disparities
Johns Hopkins Bloomberg School of Public Health（约翰斯·霍普金斯大学布隆伯格公共卫生学院）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
In the presence of prognostic covariates, inference about the treatment effect with time-to-event endpoints is mostly conducted via the stratified log-rank test or the score test based on the Cox proportional hazards model. In their ground-breaking work Ye and Shao (2020) have demonstrated theoretically that when the model is misspecified, the robust score test (Wei and Lin, 1989) as well as the unstratified log-rank test are conservative in trials with stratified randomization. This fact, however, was not established for the Pocock and Simon covariate-adaptive allocation other than through si…
There is no doubt on the importance of randomisation in agricultural experiments by agronomists and biometricians. Even when agronomists extend the experimentation from small trials to large on-farm trials, randomised designs predominate over systematic designs. However, the situation may change depending on the objective of the on-farm experiments (OFE). If the goal of OFE is obtaining a smooth map showing the optimal level of a controllable input across a grid made by rows and columns covering the whole field, a systematic design should be preferred over a randomised design in terms of robus…
Nonparametric covariate adjustment is considered for log-rank type tests of treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization and all commonly used covariate-adaptive randomization schemes such as the stratified permuted block and P…
Design and inference for multi-arm clinical trials with informational borrowing: the interacting urns design
Università degli Studi di Milano（米兰大学）；University of Bologna（博洛尼亚大学）；IMT School for Advanced Studies Lucca（卢卡高等研究学院）；Università degli Studi di Milano-Bicocca（米兰比可卡大学）
Two-Phase Sampling Designs and Analysis Approaches for Ordinal Outcomes
Vanderbilt University Medical Center（范德堡大学医学中心）；Beth Israel Deaconess Medical Center and Harvard Medical School（贝斯以色列女执事医疗中心和哈佛医学院）；Mass General Brigham（麻省总医院布里格姆）；Vanderbilt Institute for Clinical and Translational Research（范德堡临床与转化研究研究所）；Vanderbilt Genetics Institute（范德堡遗传学研究所）
The design and statistical aspects of VIETNARMS: a strategic post-licensing trial of multiple oral direct acting antiviral Hepatitis C treatment strategies in Vietnam
实现丙型肝炎的消除受到治疗费用和治疗难以接触人群的需要的阻碍。通过缩短治疗时间可以扩大治疗范围，但有限的研究意味着尚不清楚哪些策略可以达到足够高的治愈率。我们介绍了一项多组试验的统计学方面，该试验旨在通过监测机制同时测试多种策略，以快速检测和阻止那些治愈率低得令人无法接受的策略。方法VIETNARMS试验将患者随机分为三组。我们将在中期分析中使用贝叶斯监测来检测和停止招募不成功的策略，定义为真实治愈率<90%的后验概率>0.95。在这里，我们测试了停止指南的操作特征，计划了中期分析的时间，并探索了最终分析的功效。结果A β（4.5，0.5）先验为真治愈率产生<0.05 probability of incorrectly stopping a group with true cure rate >90%。治愈率非常低（<60%）的组很可能（>0.9概率）在招募约25%的患者后停止。治愈率较低（80%）的组可能在招募结束前停止（概率为0.7）。招募开始后7、10、13和18个月的中期分析提供了停止劣效组的良好概率。对于95%的总体真实治愈率，分别使用5%和10%的界值检测方案和策略比较的非劣效性的把握度>90%，而不考虑对照治愈率，并检测利巴韦林比较的5%绝对差异。结论：停止指南的操作特征是适当的，中期分析可以定时检测在不同阶段的失败组。
