Modified power prior with multiple historical trials for binary endpoints

Banbeta, Akalu; Rosmalen, Joost van; Dejardin, David; Lesaffre, Emmanuel

doi:10.1002/sim.8019

Cited by 38 publications

(72 citation statements)

References 29 publications

(51 reference statements)

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“…29 However, none of these methods are guaranteed to control the type I error rate at the nominal 5% level. 30 While these techniques are aimed at accounting for between-study heterogeneity (which is unobserved), in the case of observed differences, for example, patient characteristics, these methods may need to be augmented either with adjustment techniques or by including the relevant patient characteristics as covariates in the statistical model.…”

Section: Appropriate Statistical Methodsmentioning

confidence: 99%

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

et al. 2020

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Background While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. Methods We discuss the original ‘Pocock’ criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. Results A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. Conclusion Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

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Section: Appropriate Statistical Methodsmentioning

confidence: 99%

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

et al. 2020

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“…This modification introduces the normalizing constant dependent on the weight parameter γ, 6,7 resulting in the following joint prior distribution:

p_{c}, γ ∣ H \propto \frac{Bin {(y_{C_{H}}, n_{C_{H}}, p_{c})}^{γ} π false(p_{c} false) π false(γ false)}{\int Bin {(y_{C_{H}}, n_{C_{H}}, p_{c})}^{γ} π false(p_{c} false) d p_{c}} .

The denominator in Equation () is available in closed form when the prior placed on the control response rate is a Beta distribution. However, when the normalizing constant is not available in closed form it can be approximated, though this adds additional computational complexity 8 .…”

Section: Methods For Historical Control Borrowingmentioning

confidence: 99%

“…Incorporating the historical control data as a prior on the current control data allows for borrowing, and the various methods allow for different ways of introducing and modeling the possible sources of heterogeneity. The previously proposed power prior 5 , modified power prior 6,7 , and dependent modified power prior (DMPP) 8 are likelihood‐based methods that discount the historical data to account for differences between the pool of historical data and the current control data. The meta‐analytic‐predictive (MAP) prior 9 accounts for heterogeneity by assuming exchangeability among the historic and current control parameters and explicitly modeling the between‐trial variation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian semiparametricmeta‐analytic‐predictiveprior for historical control borrowing in clinical trials

Hupf

Bunn

Lin

et al. 2021

Statistics in Medicine

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When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta‐analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta‐analytic‐predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof‐of‐concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta‐analytic‐predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.

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“…The resulting posterior is analogous to a weighted average of the historic and current data. This has been extended to the use of multiple historical trials 87 …”

Section: Current Statistical Methods For Combining Current Controls Amentioning

confidence: 99%

“…This has been extended to the use of multiple historical trials. 87 Alternatively, hierarchical models explicitly model the similarity between current and historic data. For example, a meta-analysis could be used to estimate the outcome parameter and the between-study variability in the meta-analytic-predictive approach (MAP).…”

Section: Reviewmentioning

confidence: 99%

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges

Hall

Vase

Tobias

et al. 2020

Clin Pharma and Therapeutics

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Randomized control trials (RCTs) with placebo are the gold standard for determining efficacy of novel pharmaceutical treatments. Since their inception, over 75 years ago, researchers have amassed a large body of underutilized data on outcomes in the placebo control arms of these trials. Although rare disease indications have used these historical placebo data as synthetic controls to reduce burden on patients and accelerate drug discovery, broad use of historical controls is in its infancy. Large‐scale historical placebo data could be leveraged to benefit both drug developers and patients if warehoused and made more available to guide trial design and analysis. Here, we examine challenges in utilizing historical controls related to heterogeneity in trial design, outcome ascertainment, patient characteristics, and unmeasured pharmacogenomic effects. We then discuss the advantages and disadvantages of current approaches and propose a path forward to broader use of historical controls in RCTs.

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Modified power prior with multiple historical trials for binary endpoints

Cited by 38 publications

References 29 publications

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

Bayesian semiparametricmeta‐analytic‐predictiveprior for historical control borrowing in clinical trials

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges

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