The question of selecting the "best" amongst different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example: what is the dose that gives me a pre-specified risk of toxicity or which treatment gives the best response rate. Motivated by a recent development in the weighted information measures theory, we propose an experimental design based on a simple and intuitive criterion which governs arm selection in the experiment with multinomial outcomes. The criterion leads to accurate arm selection without any parametric or monotonicity assumption. The asymptotic properties of the design are studied for different allocation rules and the small sample size behaviour is evaluated in simulations in the context of Phase I and Phase II clinical trials with binary endpoints. We compare the proposed design to currently used alternatives and discuss its practical implementation.
Background: Experimental treatments pass through various stages of development. If a treatment passes through early phase experiments, the investigators may want to assess it in a late phase randomised controlled trial. An efficient way to do this is adding it as a new research arm to an ongoing trial. This allows to add the new treatment while the existing arms continue. The familywise type I error rate (FWER) is often a key quantity of interest in any multi-arm trial. We set out to clarify how it should be calculated when new arms are added to a trial some time after it has started. Methods:…
Selective recruitment designs preferentially recruit individuals that are estimated to be statistically informative onto a clinical trial. Individuals that are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information-adaptive design recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values and how information is defined is a critical element of information-adaptive designs. In this paper we define and evaluate four different me…
The use of simulation-based sensitivity analyses is fundamental to evaluate and compare candidate designs for future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics (OCs) with respect to various unknown parameters (UPs). Typical examples of OCs include the likelihood of detecting treatment effects and the average study duration, which depend on UPs that are not known until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial c…
A multi-arm multi-stage trial is a multi-arm trial which includes interim analyses - analysing the data at certain specified points, generally discontinuing treatments which are concluded to not work and proceeding with the remainder. It is possible that the advantages of multi-arm trials over single-arm trials may be enhanced further by considering the allocation ratio, R. For an R:1 allocation ratio, Rn patients are allocated to the control arm and n patients allocated to each active treatment arm. In this study, the optimal allocation ratio will be defined as the allocation ratio which resu…
Toward Valid Generative Clinical Trial Data with Survival Endpoints
Inria（法国国家信息与自动化技术研究院）；Université Paris Cité（巴黎城市大学）；Inserm（法国国家卫生研究院）；HeKA；AP-HP（巴黎大学医院）；Hôpital Européen Georges Pompidou（乔治·蓬皮杜欧洲医院）；Unité de Recherche Clinique APHP（APHP临床研究中心）；CIC1418（临床研究1418中心）；Epidémiologie Clinique Paris France（巴黎流行病学临床研究中心）
We study the design of multi-armed parallel group clinical trials to estimate personalized treatment rules that identify the best treatment for a given patient with given covariates. Assuming that the outcomes in each treatment arm are given by a homoscedastic linear model, with possibly different variances between treatment arms, and that the trial subjects form a random sample from an unselected overall population, we optimize the (possibly randomized) treatment allocation allowing the allocation rates to depend on the covariates. We find that, for the case of two treatments, the approximate…
Clinical trials are an instrument for making informed decisions based on evidence from well-designed experiments. Here we consider adaptive designs mainly from the perspective of multi-arm Phase II clinical trials, in which one or more experimental treatments are compared to a control. Treatment allocation of individual trial participants is assumed to take place according to a fixed block randomization, albeit with an important twist: The performance of each treatment arm is assessed after every measured outcome, in terms of the posterior distribution of a corresponding model parameter. Diffe…
Pathologic complete response (pCR) is a common primary endpoint for a phase II trial or even accelerated approval of neoadjuvant cancer therapy. If granted, a two-arm confirmatory trial is often required to demonstrate the efficacy with a time-to-event outcome such as overall survival. However, the design of a subsequent phase III trial based on prior information on the pCR effect is not straightforward. Aiming at designing such phase III trials with overall survival as primary endpoint using pCR information from previous trials, we consider a mixture model that incorporates both the survival …
Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of new treatments. The design of response adaptive trials, in most cases, requires time consuming simulation studies to describe operating characteristics, such as type I/II error rates, across plausible scenarios. We investigate large sample approximations of pivotal operating characteristics in Bayesian Uncertainty directed trial Designs (BUDs). A BUD trial …
Clinical trials are complex and usually involve multiple objectives such as controlling type I error rate, increasing power to detect treatment difference, assigning more patients to better treatment, and more. In literature, both response-adaptive randomization (RAR) procedures (by changing randomization procedure sequentially) and sequential monitoring (by changing analysis procedure sequentially) have been proposed to achieve these objectives to some degree. In this paper, we propose to sequentially monitor response-adaptive randomized clinical trial and study it's properties. We prove that…
Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds and both sample size calculation and analysis methods to be used should be recon…
In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (1) guaranteed efficiency gain: a model-assisted method should often gai…
In Anscombe's classical model, the objective is to find the optimal sequential rule for learning about the difference between two alternative treatments and subsequently selecting the superior one. The population for which the procedure is optimised has size $N$ and includes both the patients in the trial and those which are treated with the chosen alternative after the trial. We review earlier work on this problem and give a detailed treatment of the problem itself. In particular, while previous work has mainly focused on the case of conjugate normal priors for the incremental effect, we demo…
Group sequential design (GSD) is widely used in clinical trials in which correlated tests of multiple hypotheses are used. Multiple primary objectives resulting in tests with known correlations include evaluating 1) multiple experimental treatment arms, 2) multiple populations, 3) the combination of multiple arms and multiple populations, or 4) any asymptotically multivariate normal tests. In this paper, we focus on the first 3 of these and extend the framework of the weighted parametric multiple test procedure from fixed designs with a single analysis per objective to a GSD setting where diff…
Biased-coin designs are used in clinical trials to allocate treatments with some randomness while maintaining approximately equal allocation. More recent rules are compared with Efron's [Biometrika 58 (1971) 403-417] biased-coin rule and extended to allow balance over covariates. The main properties are loss of information, due to imbalance, and selection bias. Theoretical results, mostly large sample, are assembled and assessed by small-sample simulations. The properties of the rules fall into three clear categories. A Bayesian rule is shown to have appealing properties; at the cost of slight…
In many clinical trials, outcomes of interest include binary-valued endpoints. It is not uncommon that a binary-valued outcome is dichotomized from a continuous outcome at a threshold of clinical interest. To reach the objective, common approaches include (a) fitting the generalized linear mixed model (GLMM) to the dichotomized longitudinal binary outcome and (b) imputation method (MI): imputing the missing values in the continuous outcome, dichotomizing it into a binary outcome, and then fitting the generalized linear model for the "complete" data. We conducted comprehensive simulation studie…
We consider the classical problem of selecting the best of two treatments in clinical trials with binary response. The target is to find the design that maximizes the power of the relevant test. Many papers use a normal approximation to the power function and claim that Neyman allocation that assigns subjects to treatment groups according to the ratio of the responses' standard deviations, should be used. As the standard deviations are unknown, an adaptive design is often recommended. The asymptotic justification of this approach is arguable, since it uses the normal approximation in tails whe…
Response adaptive randomization (RAR) is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. However, applications of RAR in confirmatory drug clinical trials with multiple active arms are limited largely due to its complexity, and lack of control of randomization ratios to different treatment groups. To address the aforementioned issues, we propose a Response Adaptive Block Randomization (RABR) design allowing arbitrarily pre-specified randomization rati…
Broadening eligibility criteria in cancer trials has been advocated to represent the true patient population more accurately. While the advantages are clear in terms of generalizability and recruitment, novel dose-finding designs are needed to ensure patient safety. These designs should be able to recommend precise doses for subpopulations if such subpopulations with different toxicity profiles exist. While dose-finding designs accounting for patient heterogeneity have been proposed, all existing methods assume the source of heterogeneity is known and thus pre-specify the subpopulations or onl…
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease progression in MS clinical trials. A CRBM is capable of generating digital twins, which are simulated subjects having the same baseline data as actual subjects. Digital twins allow for subject-level statistical analyses of disease progression. The CRBM is trained using data from 23…
FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
Owkin, Inc.（Owkin公司）；Georges Pompidou European Hospital（乔治·蓬皮杜欧洲医院）；AP-HP（巴黎公立医院集团）；Centre de Recherche des Cordeliers（科尔得利研究中心）；Inserm（法国国家健康与医学研究院）；Institut du Cancer Paris CARPEM（巴黎CARPEM癌症研究所）；AP-HP Centre（巴黎公立医院集团中心）；Sorbonne University（索邦大学）；Centre de Recherche sur l’Inflammation (CRI)（炎症研究中心）；CNRS（法国国家科学研究中心）；Beaujon Hospital（博容医院）；Catalan Institute of Oncology（加泰罗尼亚肿瘤研究所）；Doctor Josep Trueta University Hospital（约瑟普·特鲁埃塔大学医院）；Pancreatic Cancer Action Network（胰腺癌行动网络）
Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）
The stochastic $K$-armed bandit problem has been studied extensively due to its applications in various domains ranging from online advertising to clinical trials. In practice however, the number of arms can be very large resulting in large memory requirements for simultaneously processing them. In this paper we consider a streaming setting where the arms are presented in a stream and the algorithm uses limited memory to process these arms. Here, the goal is not only to minimize regret, but also to do so in minimal memory. Previous algorithms for this problem operate in one of the two settings…
Identifying All ε-Best Arms in (Misspecified) Linear Bandits
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology（麻省理工学院土木与环境工程系）；School of Operations Research and Information Engineering, Cornell University（康奈尔大学运筹学与信息工程学院）；Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济管理学院）；SC Johnson College of Business, Cornell University（康奈尔大学SC约翰逊商学院）
