Abstract
BackgroundPerforming well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial’s analysis through a prior distribution.MethodsMotivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions.ResultsThe operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data.ConclusionIn the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.
Highlights
Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible
More and more data are being generated: this may be real world evidence or evidence generated from clinical trials conducted by pharmaceutical companies or academic clinical trials units; evidence may be in the form of individual patient data (IPD) or aggregate information; and data may be accessed through repositories or registries [12]
The historical controls were generated by sampling from Weibull distributions; prior-data conflicts arise either due to differences between corresponding parameters of these Weibull distributions or because the treatment effect underlying the new trial differs from the historical estimate
Summary
Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. In the rare disease setting, the standard of care often remains relatively stable over time as treatment options are slow to advance, we can expect some commensurability between the performance of the control therapy in historical studies and the new trial. In 2006, the Food and Drug Administration published a guideline for the use of Bayesian statistics in medical device clinical trials [13] which highlighted the advantages of using historical data to formulate a prior distribution for a parameter of interest, while insisting on the importance of down-weighting or discounting this information. In 2017, they published a guideline for the use of antibacterial therapies for patients with an unmet medical need for the treatment of serious bacterial diseases which encourages the use of historical information as a control for the trial in some particular situations [14]
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