Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Smith, Claire; Thomas, Zachary M.; Enas, Nathan; Thorn, Katharine; Lahn, Michael; Benhadji, Karim A.; Cleverly, Ann

doi:10.1002/pst.1990

Cited by 5 publications

(8 citation statements)

References 19 publications

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“…In these studies, data on members of the control group are borrowed from other trials with similar demographics and disease characteristics. Ultimately, this method allowed for a new trial to use 15–20% fewer participants than would be required for a standalone clinical trial with a full, concurrent control group 17 . This same approach could be used in phase III trials to create an even larger impact on the efficiency of clinical drug development 18 , including borrowing control data from studies in other therapeutic areas.…”

Section: Examples Of How Bayesian Methods Are Being Used Effectivelymentioning

confidence: 99%

Application of Bayesian approaches in drug development: starting a virtuous cycle

Ruberg¹,

Beckers

Hemmings³

et al. 2023

Nat Rev Drug Discov

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The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.

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Section: Examples Of How Bayesian Methods Are Being Used Effectivelymentioning

confidence: 99%

Application of Bayesian approaches in drug development: starting a virtuous cycle

Ruberg¹,

Beckers

Hemmings³

et al. 2023

Nat Rev Drug Discov

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“…Compared to continuous and binary endpoints, historical data borrowing with TTE endpoint has received less attention until recently 15,18 . Let

h = 1, \dots, H

index the historical trials.…”

Section: Methodsmentioning

confidence: 99%

“…Compared to continuous and binary endpoints, historical data borrowing with TTE endpoint has received less attention until recently. 15,18 Let h ¼ 1, ÁÁÁ,H index the historical trials. The historical TTE data are summarized in two quantities: number of events and total at-risk time (exposure), denoted respectively by r h and exp h .…”

Section: Eb-rmap Prior With Time-to-event Endpointmentioning

confidence: 99%

Adaptively leveraging external data with robust meta‐analytical‐predictive prior using empirical Bayes

Zhang

Shen

et al. 2023

Pharmaceutical Statistics

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The robust meta‐analytical‐predictive (rMAP) prior is a popular method to robustly leverage external data. However, a mixture coefficient would need to be pre‐specified based on the anticipated level of prior‐data conflict. This can be very challenging at the study design stage. We propose a novel empirical Bayes robust MAP (EB‐rMAP) prior to address this practical need and adaptively leverage external/historical data. Built on Box's prior predictive p‐value, the EB‐rMAP prior framework balances between model parsimony and flexibility through a tuning parameter. The proposed framework can be applied to binomial, normal, and time‐to‐event endpoints. Implementation of the EB‐rMAP prior is also computationally efficient. Simulation results demonstrate that the EB‐rMAP prior is robust in the presence of prior‐data conflict while preserving statistical power. The proposed EB‐rMAP prior is then applied to a clinical dataset that comprises 10 oncology clinical trials, including the prospective study.

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“…We use indicator variables I i CC , I i h to distinguish between the hth historical control group ( I i CC = 0 and I i h = 1), current control group ( I i CC = 1 and I i h = 0), and current treatment group ( I i CC = 0 and I i h = 0). Using these indicator variables, we assume the proportional hazards model 11,12 , λ i = λ CT exp 0.2em false( θ CC I i CC + ∑ h = 1 H θ h I i h false), where λ i is the hazard for each patient, …”

Section: Existing Methodsmentioning

confidence: 99%

“…We use indicator variables I CC i , I h i to distinguish between the hth historical control group (I CC i = 0 and I h i = 1), current control group (I CC i = 1 and I h i = 0), and current treatment group (I CC i = 0 and I h i = 0). Using these indicator variables, we assume the proportional hazards model 11,12 ,…”

Section: Meta-analytic Approachmentioning

confidence: 99%

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

Ohigashi

Maruo

Sozu

et al. 2022

Stat Methods Med Res

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Meta-analytic approaches and power priors are often used to incorporate historical controls into the analysis of a current randomized controlled trial. In this study, we propose a method for incorporating multiple historical controls based on a horseshoe prior, which is a type of global–local shrinkage prior. The method assumes that historical controls follow the same distribution as the current control. In the case in which only a few historical controls are heterogeneous, we consider them to follow a potentially biased distribution from the distribution of the current control. We analyze two clinical trial examples with binary and time-to-event endpoints and conduct simulation studies to compare the performance of the proposed and existing methods. In the analysis of the clinical trial example, the posterior standard deviation of the treatment effect is decreased by the proposed method by considering the bias between the current control and heterogeneous historical control. In the scenarios in which the current and historical controls follow the same distribution, the statistical power using the proposed method is higher than that using existing methods. The proposed method is advantageous when few or no heterogeneous historical controls are expected.

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Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Cited by 5 publications

References 19 publications

Application of Bayesian approaches in drug development: starting a virtuous cycle

Application of Bayesian approaches in drug development: starting a virtuous cycle

Adaptively leveraging external data with robust meta‐analytical‐predictive prior using empirical Bayes

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

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