Robust meta‐analytic‐predictive priors in clinical trials with historical control information

Abstract

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Bayesian selective response‐adaptive design using the historical control

“…Other priors that address the potential conflict such as meta‐analytic‐predictive priors11 exist and can be used instead. Similarly, other response‐adaptive randomization schemes can be used if they can be appropriately modified.…”

“…Despite careful selection of historical trials, study design, conduct, or subject population may differ so past information may not be relevant for the proposed trial. Bayesian designs were introduced to address such prior‐data conflict by data adaptively determining the degree of borrowing on the control arm using power priors,5, 6, 7 commensurate priors,8, 9, 10 or meta‐analytic‐predictive priors 11. All this work, however, focused on reducing the number of study subjects assigned to the control group without taking into account the true relative treatment effects.…”

Meta-analysis of aggregate data on medical events

“…While prior information about a new test treatment may be limited, several previous trials studying the control group may be available to provide information on the nuisance parameters describing the distribution of control group outcomes (Schmidli et al, 2014). However, while an exchangeability of the parameters in historical and new trials may often be plausible, there will inevitably be concerns that there could be systematic differences that could bias posterior inference (Schmidli et al, 2014). In this section we review a number of the existing proposals for making posterior inference more robust against such prior-data conflicts.…”

“…Because these approaches make non-randomized comparisons to control groups that were not concurrently recruited, the reliability of such comparisons and to what extent such data should be discounted based on potential between-trial differences are key questions (Schmidli et al, 2014). …”

“…the posterior from fitting a hierarchical meta-analysis model to the historical data Schmidli et al, 2014). Secondly, the marginal posterior of the hyperparameters given D is used as the hyperprior for a new analysis (Schmidli et al, 2014), i.e.…”