The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as a discounting parameter. When the discounting parameter is modeled as random, the normalized power prior (NPP) is recommended. When there are multiple historical datasets, there has been limited research on how to choose priors for the multiple discounting parameters of the NPP to induce desirable information borrowing behavior. In this work, we address this question by investigating the analytical relationship between the NPP and the Bayesian hierarchical model (BHM), which is a widely used method for synthesizing information from different sources. We develop the adapted normalized power prior (ANPP), which establishes dependence between the dataset-specific discounting parameters of the NPP, leading to inferences that are identical to the BHM. We establish a direct relationship between the prior for the dataset-specific discounting parameters of the ANPP and the prior for the variance parameter of the BHM. Establishing this relationship not only justifies the NPP from the perspective of hierarchical modeling, but also achieves easy prior elicitation for the NPP for the purpose of dynamic borrowing. We examine the borrowing properties of the ANPP through simulations, and apply it to a case study for a pediatric lupus trial.
Discussion on "Principled type I error rate inflation in two-arm clinical trial designs with external control information borrowing" by Silvia Calderazzo, Manuel Wiesenfarth, Vivienn Weru, and Annette Kopp-Schneider
University Medical Center Göttingen（哥廷根大学医学中心）；Deartment of Medical Statistics（医学统计学系）
Borrowing of information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Many approaches for borrowing from external data have been proposed. Even though these methods are mainly based on Bayesian approaches by incorporating external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are of interest. In particular, type I error and power at a prespecified point…
In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This featur…
Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is how to effectively borrow information from historical data while maintaining a reasonable type I error. We propose the elastic prior approach to address this challenge and achieve dynamic information borrowing. Unlike existing approaches, this method proactively controls the behavior of dynamic information borrowing and type I errors by incorporating a well-known concept of clinically meaningful difference through an elastic function, defined as a monotonic …
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）
Background -- In phase I clinical trials, historical data may be available through multi-regional programs, reformulation of the same drug, or previous trials for a drug under the same class. Statistical designs that borrow information from historical data can reduce cost, speed up drug development, and maintain safety. Purpose -- Based on a hybrid design that partly uses probability models and partly uses algorithmic rules for decision making, we aim to improve the efficiency of the dose-finding trials in the presence of historical data, maintain safety for patients, and achieve a level of si…
In clinical trials, there often exist multiple historical studies for the same or related treatment investigated in the current trial. Incorporating historical data in the analysis of the current study is of great importance, as it can help to gain more information, improve efficiency, and provide a more comprehensive evaluation of treatment. Enlightened by the unit information prior (UIP) concept in the reference Bayesian test, we propose a new informative prior called UIP from an information perspective that can adaptively borrow information from multiple historical datasets. We consider bot…
Most clinical trials involve the comparison of a new treatment to a control arm (e.g., the standard of care) and the estimation of a treatment effect. External data, including historical clinical trial data and real-world observational data, are commonly available for the control arm. Borrowing information from external data holds the promise of improving the estimation of relevant parameters and increasing the power of detecting a treatment effect if it exists. In this paper, we propose to use Bayesian additive regression trees (BART) for incorporating external data into the analysis of clini…
Borrowing from historical control data in a Bayesian time-to-event model with flexible baseline hazard function
AstraZeneca R&D, Cambridge（阿斯利康研发剑桥）；Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK, WC1E 7HT（伦敦卫生与热带医学院医学统计系）
Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomised basket trial design where patients are randomly assigned to the new treatment or a control within each trial subset (`subtrial' for short). Closed-form sample size formulae are derived to ensure each subtrial has a specified chance of correctly deciding whether the new…
The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalisation. Today, the traditional statistical analyses approaches may seem questionable to practitioners in light of ubiquitous historical covariate information. Several methodological developments aim at incorporating historical information in the design and analysis of future clinical trials, most importantly Bayesian information borrowing, propensity score…
Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this paper, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomised, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under …
Use of historical control data to augment a small internal control arm in a randomized control trial (RCT) can lead to significant improvement of the efficiency of the trial. It introduces the risk of potential bias, since the historical control population is often rather different from the RCT. Power prior approaches have been introduced to discount the historical data to mitigate the impact of the population difference. However, even with a Bayesian dynamic borrowing which can discount the historical data based on the outcome similarity of the two populations, a considerable population diffe…
We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local-MEM framework, information borrowing is only allowed to occur locally, i.e., among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two-stage design for phase II basket trials using the proposed strategy. The …
Optimal Biological Dose Combination Finding: A Design Roadmap and Robust Cross-Indication Bayesian Borrowing
Population Health Sciences Institute, Newcastle University（纽卡斯尔大学人口健康科学研究所）；Quantitative Science and Evidence Generation, Astellas Pharma Global Development Inc.（安斯泰来全球开发公司定量科学与证据生成部）
Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early-phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 e…
