Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls

A Bayesian model with application for adaptive platform trials having temporal changes

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.

Elastic priors to dynamically borrow information from historical data in clinical trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.

Augmenting control arms with real-world data for cancer trials: Hybrid control arm methods and considerations

Background

Hybrid controlled trials with real-world data (RWD), where the control arm is composed of both trial and real-world patients, could facilitate research when the feasibility of randomized controlled trials (RCTs) is challenging and single-arm trials would provide insufficient information.

Methods

We propose a frequentist two-step borrowing method to construct hybrid control arms. We use parameters informed by a completed randomized trial in metastatic triple-negative breast cancer to simulate the operating characteristics of dynamic and static borrowing methods, highlighting key trade-offs and analytic decisions in the design of hybrid studies.

Results

Simulated data were generated under varying residual-bias assumptions (no bias: HR_RWD = 1) and experimental treatment effects (target trial scenario: HR_Exp = 0.78). Under the target scenario with no residual bias, all borrowing methods achieved the desired 88% power, an improvement over the reference model (74% power) that does not borrow information externally. The effective number of external events tended to decrease with higher bias between RWD and RCT (i.e. HR_RWD away from 1), and with weaker experimental treatment effects (i.e. HR_Exp closer to 1). All dynamic borrowing methods illustrated (but not the static power prior) cap the maximum Type 1 error over the residual-bias range considered. Our two-step model achieved comparable results for power, type 1 error, and effective number of external events borrowed compared to other borrowing methodologies.

Conclusion

By pairing high-quality external data with rigorous simulations, researchers have the potential to design hybrid controlled trials that better meet the needs of patients and drug development.

Bayesian and frequentist approaches to sequential monitoring for futility in oncology basket trials: A comparison of Simon's two-stage design and Bayesian predictive probability monitoring with information sharing across baskets

This article discusses and compares statistical designs of basket trial, from both frequentist and Bayesian perspectives. Baskets trials are used in oncology to study interventions that are developed to target a specific feature (often genetic alteration or immune phenotype) that is observed across multiple tissue types and/or tumor histologies. Patient heterogeneity has become pivotal to the development of non-cytotoxic treatment strategies. Treatment targets are often rare and exist among several histologies, making prospective clinical inquiry challenging for individual tumor types. More generally, basket trials are a type of master protocol often used for label expansion. Master protocol is used to refer to designs that accommodates multiple targets, multiple treatments, or both within one overarching protocol. For the purpose of making sequential decisions about treatment futility, Simon's two-stage design is often embedded within master protocols. In basket trials, this frequentist design is often applied to independent evaluations of tumor histologies and/or indications. In the tumor agnostic setting, rarer indications may fail to reach the sample size needed for even the first evaluation for futility. With recent innovations in Bayesian methods, it is possible to evaluate for futility with smaller sample sizes, even for rarer indications. Novel Bayesian methodology for a sequential basket trial design based on predictive probability is introduced. The Bayesian predictive probability designs allow interim analyses with any desired frequency, including continual assessments after each patient observed. The sequential design is compared with and without Bayesian methods for sharing information among a collection of discrete, and potentially non-exchangeable tumor types. Bayesian designs are compared with Simon's two-stage minimax design.

Optimal Sequential Predictive Probability Designs for Early-Phase Oncology Expansion Cohorts

PURPOSE

The customary approach to early-phase clinical trial design, where the focus is on identification of the maximum tolerated dose, is not always suitable for noncytotoxic or other targeted therapies. Many trials have continued to follow the 3 + 3 dose-escalation design, but with the addition of phase I dose-expansion cohorts to further characterize safety and assess efficacy. Dose-expansion cohorts are not always planned in advance nor rigorously designed. We introduce an approach to the design of phase I expansion cohorts on the basis of sequential predictive probability monitoring.

METHODS

Two optimization criteria are proposed that allow investigators to stop for futility to preserve limited resources while maintaining traditional control of type I and type II errors. We demonstrate the use of these designs through simulation, and we elucidate their implementation with a redesign of the phase I expansion cohort for atezolizumab in metastatic urothelial carcinoma.

RESULTS

A sequential predictive probability design outperforms Simon's two-stage designs and posterior probability monitoring with respect to both proposed optimization criteria. The Bayesian sequential predictive probability design yields increased power while significantly reducing the average sample size under the null hypothesis in the context of the case study, whereas the original study design yields too low type I error and power. The optimal efficiency design tended to have more desirable properties, subject to constraints on type I error and power, compared with the optimal accuracy design.

CONCLUSION

The optimal efficiency design allows investigators to preserve limited financial resources and to maintain ethical standards by halting potentially large dose-expansion cohorts early in the absence of promising efficacy results, while maintaining traditional control of type I and II error rates.

Bayesian adaptive randomization design incorporating propensity score-matched historical controls

Incorporating historical control data to augment the control arm in randomized controlled trials (RCTs) is one way of increasing their efficiency and feasibility when adequate RCTs cannot be conducted. In recent work, a Bayesian adaptive randomization design incorporating historical control data has been proposed to reduce sample size according to the amount of information that could be borrowed, assessed at interim assessment in respect to prior-data conflict. However, the approach does not distinguish between the two sources of prior-data conflict: (1) imbalance in measured covariates, and (2) imbalance in unmeasured covariates. In this paper, we propose an extension of the Bayesian adaptive randomization design to incorporate propensity score-matched historical controls. At interim assessment, historical controls similar to the concurrent controls in terms of measured covariates are selected using propensity score matching. Then, final sample size of the control arm is adjusted according to the extent of borrowing from the matched historical controls quantified by effective historical sample size. The conditional power prior approach and commensurate prior approach are adopted for designing the prior, and addressing prior-data conflict due to unmeasured covariate imbalance. Simulation results show that the proposed method yields reduced bias in treatment effect estimates, type I error at the nominal level, and reduced sample size while maintaining statistical power. Even when residual imbalance exists due to unmeasured covariates, the proposed method borrowed more information without risking substantially inflated type I error and bias, providing meaningful implications for use of historical controls to facilitate the conduct of adequate RCTs.

A group-sequential randomized trial design utilizing supplemental trial data

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

Quantification of prior impact in terms of effective current sample size

Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, this measure is independent of newly observed data, and thus would not capture an actual "loss of information" induced by the prior in case of prior-data conflict. We build on a recent work to relate prior impact to the number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power, and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcome lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper.

Borrowing from Historical Control Data in Cancer Drug Development: A Cautionary Tale and Practical Guidelines

Some clinical trialists, especially those working in rare or pediatric disease, have suggested borrowing information from similar but already-completed clinical trials. This paper begins with a case study in which relying solely on historical control information would have erroneously resulted in concluding a significant treatment effect. We then attempt to catalog situations where borrowing historical information may or may not be advisable using a series of carefully designed simulation studies. We use an MCMC-driven Bayesian hierarchical parametric survival modeling approach to analyze data from a sponsor's colorectal cancer study. We also apply these same models to simulated data comparing the effective historical sample size, bias, 95% credible interval widths, and empirical coverage probabilities across the simulated cases. We find that even after accounting for variations in study design, baseline characteristics, and standard-of-care improvement, our approach consistently identifies Bayesianly significant differences between the historical and concurrent controls under a range of priors on the degree of historical data borrowing. Our simulation studies are far from exhaustive, but inform the design of future trials. When the historical and current controls are not dissimilar, Bayesian methods can still moderate borrowing to a more appropriate level by adjusting for important covariates and adopting sensible priors.