Dynamic Borrowing Through Empirical Power Priors that Control Type I Error

Abstract

In order for historical data to be considered for inclusion in the design and analysis of clinical trials, prospective rules are essential. Incorporation of historical data may be of particular interest in the case of small populations where available data is scarce and heterogeneity is not as well understood, and thus conventional methods for evidence synthesis might fall short. The concept of power priors can be particularly useful for borrowing evidence from a single historical study. Power priors employ a parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>γ</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>[</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo></mml:math> that quantifies the heterogeneity between the historical study and the new study. However, the possibility of borrowing data from a historical trial will usually be associated with an inflation of the type I error. We suggest a new, simple method of estimating the power parameter suitable for the case when only one historical dataset is available. The method is based on predictive distributions and parameterized in such a way that the type I error can be controlled by calibrating to the degree of similarity between the new and historical data. The method is demonstrated for normal responses in a one or two group setting. Generalization to other models is straightforward.

Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control

“…Extreme cases are , when information from d 0 is discarded and , when d 0 is completely taken into account. For developments of the power prior approach, see, for example, Duan, Ye, and Smith (); Gravestock and Held (); Ibrahim and Chen (); Ibrahim, Chen, Gwon, and Chen (); Neuenschwander, Branson, and Spiegelhalter () and Nikolakopoulos, Tweel, and Roes ().…”

Bayesian sample size re-estimation using power priors

“…Or, in other words the ðjD 0 ,Þ is such that the two-sided prior predictive p-value for T is at least c. By choosing c, as shown by Nikolakopoulos et al, 29 one can calibrate the procedure in order for desirable Figure 1. Sample sizes estimated with Bayesian SSR, with their 95% confidence intervals, for different true 's ( R ), for assumed ¼ 1 and…”

“…We employ the power prior approach introduced in Nikolakopoulos et al 29 to synthesize prior and new data in order for operational characteristics (in this case the probability of having a conclusive trial) to be calibrated.…”

“…The adaptive power prior, based on predictive distributions and termed Prior-Data conflict calibrating power prior (PDCCPP) in Nikolakopoulos et al, 29 is then briefly described and applied in the variance re-estimation problem. Subsequently, the proposed approach is demonstrated for a clinical trial in the field of pediatrics.…”

Applicability and added value of novel methods to improve drug development in rare diseases

“… ▪ At least 1 interim analysis ▪ Randomisation ▪ 1 control and 1 experimental arm ▪ Developed for continuous endpoints, transportable to other types of outcomes ▪ More efficient use of available patients for the development programme (i.e. smaller sample size) ▪ Increased precision when optimally using prior knowledge (from historical data or previous trials) to estimate treatment effect size ▪ Control of type I error ▪ Extra patients needed in case of effect size overestimation Dynamic borrowing using power priors that control type I error [ 57 ] In rare diseases, where available data is scarce and heterogeneity between trials is less well understood, the current methods of meta-analysis fall short. The concept of power priors can be useful, particularly for borrowing evidence from a single historical study.…”