Historical Control Data Borrowing for Noninferiority Assessment

Quan, Hui; Xu, Zhixing; Cho, Meehyung; Dong, Yan; Jia, Nan

doi:10.1080/19466315.2021.1994458

Cited by 2 publications

(2 citation statements)

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“…There are many methods for dynamic data borrowing for univariate data analysis. 10,11,16 For a bivariate data analysis, we can consider the extension of a power prior as an example. 17 For borrowing (1) to combine with (3) to form (4), the power parameter can be derived as…”

Section: Dynamic Historical Data Borrowingmentioning

confidence: 99%

“…This dynamic approach will determine or discount the amount of data borrowing from the historical source based on the observed level of heterogeneity using the phase II data as the reference. There are many methods for dynamic data borrowing for univariate data analysis 10,11,16 . For a bivariate data analysis, we can consider the extension of a power prior as an example 17 .…”

Section: Dynamic Historical Data Borrowingmentioning

confidence: 99%

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Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint

Quan

Luo

et al. 2023

Pharmaceutical Statistics

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To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.

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