Design of randomized controlled confirmatory trials using historical control data to augment sample size for concurrent controls

Considerations for master protocols using external controls

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different commensurate priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and the potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings, we recommend more simulation studies.

Hybrid Controlled Clinical Trials Using Concurrent Registries in Amyotrophic Lateral Sclerosis: A Feasibility Study

Hybrid designs with both randomized arms and an external control cohort preserve key features of randomization and utilize external information to augment clinical trials. In this study, we propose to leverage high-quality, patient-level concurrent registries to enhance clinical trials and illustrate the impact on trial design for amyotrophic lateral sclerosis. The proposed methodology was evaluated in a randomized, placebo-controlled clinical trial. We used patient-level information from a well-defined, population-based registry, that was running parallel to the randomized clinical trial, to identify concurrently nonparticipating, eligible patients who could be matched with trial participants, and integrate them into the statistical analysis. We assessed the impact of the addition of the external controls on the treatment effect estimate, precision, and time to reach a conclusion. During the runtime of the trial, a total of 1,141 registry patients were alive; 473 (41.5%) of them fulfilled the eligibility criteria and 133 (11.7%) were enrolled in the study. A matched control population could be identified among the nonparticipating patients. Augmenting the randomized controls with matched external controls could have avoided unnecessary randomization of 17 patients (-12.8%) as well as reducing the study duration from 30.1 months to 22.6 months (-25.0%). Matching eligible external controls from a different calendar period led to bias in the treatment effect estimate. Hybrid trial designs utilizing a concurrent registry with rigorous matching can minimize bias due to a mismatch in calendar time and differences in standard of care, and may accelerate the development of new treatments.

Leveraging external evidence using Bayesian hierarchical model and propensity score in the presence of covariates

In recent decades, there has been growing interest in leveraging external data information for clinical development as it improves the efficiency of the design and inference of clinical trials when utilized properly and more importantly, alleviates potential ethical and recruitment challenges. When it is of interest to augment the concurrent study's control arm using external control data, the potential outcome heterogeneity across data sources, also known as prior-data conflict, should be accounted for. In addition, in the outcome modeling, inclusion of prognostic covariates that may have impact on the outcome can avoid efficiency loss or potential bias. In this paper, we propose a Bayesian hierarchical modeling strategy incorporating covariate-adjusted meta-analytic predictive approach (cMAP) and also introduce a propensity score (PS) based sequential procedure that integrates the cMAP. In the simulation study, the proposed methods are found to have advantages in the estimation, power, and type I error control over the standard methods such as PS matching alone and hierarchical modeling that ignores the covariates. An illustrative example is used to illustrate the procedure.

On the use of non-concurrent controls in platform trials: a scoping review

BACKGROUND

Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met.

METHODS

We conducted two reviews on the use of non-concurrent controls in platform trials: one on statistical methodology and one on regulatory guidance. We broadened our searches to the use of external and historical control data. We conducted our review on the statistical methodology in 43 articles identified through a systematic search in PubMed and performed a review on regulatory guidance on the use of non-concurrent controls in 37 guidelines published on the EMA and FDA websites.

RESULTS

Only 7/43 of the methodological articles and 4/37 guidelines focused on platform trials. With respect to the statistical methodology, in 28/43 articles, a Bayesian approach was used to incorporate external/non-concurrent controls while 7/43 used a frequentist approach and 8/43 considered both. The majority of the articles considered a method that downweights the non-concurrent control in favour of concurrent control data (34/43), using for instance meta-analytic or propensity score approaches, and 11/43 considered a modelling-based approach, using regression models to incorporate non-concurrent control data. In regulatory guidelines, the use of non-concurrent control data was considered critical but was deemed acceptable for rare diseases in 12/37 guidelines or was accepted in specific indications (12/37). Non-comparability (30/37) and bias (16/37) were raised most often as the general concerns with non-concurrent controls. Indication specific guidelines were found to be most instructive.

CONCLUSIONS

Statistical methods for incorporating non-concurrent controls are available in the literature, either by means of methods originally proposed for the incorporation of external controls or non-concurrent controls in platform trials. Methods mainly differ with respect to how the concurrent and non-concurrent data are combined and temporary changes handled. Regulatory guidance for non-concurrent controls in platform trials are currently still limited.

Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial

BACKGROUND

Randomized controlled trials offer the best design for eliminating bias in estimating treatment effects but can be slow and costly in rare disease research. Additionally, an equal randomization approach may not be optimal in studies in which prior evidence of superiority of one or more treatments exist. Supplementing prospectively enrolled, concurrent controls with historical controls can reduce recruitment requirements and provide patients a higher likelihood of enrolling in a new and possibly superior treatment arm. Appropriate methods need to be employed to ensure comparability of concurrent and historical controls to minimize bias and variability in the treatment effect estimates and reduce the chances of drawing incorrect conclusions regarding treatment benefit.

METHODS

MILES was a phase III placebo-controlled trial employing 1:1 randomization that led to US Food and Drug Administration approval of sirolimus for treating patients with lymphangioleiomyomatosis. We re-analyzed the MILES trial data to learn whether substituting concurrent controls with controls from a historical registry could have accelerated subject enrollment while leading to similar study conclusions. We used propensity score matching to identify exchangeable historical controls from a registry balancing the baseline characteristics of the two control groups. This allowed more new patients to be assigned to the sirolimus arm. We used trial data and simulations to estimate key outcomes under an array of alternative designs.

RESULTS

Borrowing information from historical controls would have allowed the trial to enroll fewer concurrent controls while leading to the same conclusion reached in the trial. Simulations showed similar statistical performance for borrowing as for the actual trial design without producing type I error inflation and preserving power for the same study size when concurrent and historical controls are comparable.

CONCLUSION

Substituting concurrent controls with propensity score-matched historical controls can allow more prospectively enrolled patients to be assigned to the active treatment and enable the trial to be conducted with smaller overall sample size, while maintaining covariate balance and study power and minimizing bias in response estimation. This approach does not fully eliminate the concern that introducing non-randomized historical controls in a trial may lead to bias in estimating treatment effects, and should be carefully considered on a case-by-case basis. Borrowing historical controls is best suited when conducting randomized controlled trials with conventional designs is challenging, as in rare disease research. High-quality data on covariates and outcomes must be available for candidate historical controls to ensure the validity of these designs. Additional precautions are needed to maintain blinding of the treatment assignment and to ensure comparability in the assessment of treatment safety.MILES ClinicalTrials.gov Number: NCT00414648.

An Improved Matching Practice for Augmenting a Randomized Clinical Trial with External Control

The use of information from real world to assess the effectiveness of medical products is becoming increasingly popular and more acceptable by regulatory agencies. According to a strategic real-world evidence framework published by U.S. Food and Drug Administration, a hybrid randomized controlled trial that augments internal control arm with real-world data is a pragmatic approach worth more attention. In this paper, we aim to improve on existing matching designs for such a hybrid randomized controlled trial. In particular, we propose to match the entire concurrent randomized clinical trial (RCT) such that (1) the matched external control subjects used to augment the internal control arm are as comparable as possible to the RCT population, (2) every active treatment arm in an RCT with multiple treatments is compared with the same control group, and (3) matching can be conducted and the matched set locked before treatment unblinding to better maintain the data integrity and increase the credibility of the analysis. Besides a weighted estimator, we also introduce a bootstrap method to obtain its variance estimation. The finite sample performance of the proposed method is evaluated by simulations based on data from a real clinical trial.

Thermoreversible Reverse-Phase-Shift Foam for Treatment of Noncompressible Torso Hemorrhage

BACKGROUND

Noncompressible torso hemorrhage (NCTH) is a leading cause of traumatic exsanguination, requiring emergent damage control surgery performed by a highly trained surgeon in a sterile operating environment. A self-expanding, intraabdominally deployed, thermoreversible foam is one proposed method to potentially task shift temporizing hemostasis to earlier providers and additional settings. The purpose of this study was to assess the feasibility of using Fast Onset Abdominal Management (FOAM) in a lethal swine model of NCTH.

METHODS

This was a proof-of-concept study comparing FOAM intervention in large Yorkshire swine to historical control animals in the established Ross-Burns model of NCTH. After animal preparation, a Grade IV liver laceration was surgically induced, followed by a free bleed period of 10 min. FOAM was then deployed to a goal intraabdominal pressure of 60 mm Hg for 5 min, followed by a total 60-min observation period following injury.

RESULTS

At the end of the experiment, the FOAM agent was found to be distributed throughout the peritoneal cavity in all animals, without signs of iatrogenic injury. The FOAM group demonstrated a significantly higher mean arterial pressure compared with historical controls and a trend toward improved survival: 82% (9/11) compared with 50% for controls (7/14; P = 0.082).

CONCLUSIONS

This is the first study to describe the use of a thermoresponsive foam to manage NCTH and successfully demonstrated proof-of-concept feasibility of FOAM deployment. These results provide strong support for future, higher-powered studies to confirm improved survival with this novel intervention.