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Including historical control data in simultaneous inference for pre-clinical multi-arm studies

Max Menssen, Carsten Kneuer, Gyamfi Akyianu, Christian Röver, Tim Friede, Frank Schaarschmidt

Abstract

In pre- and non-clinical toxicology, the reduction of animal use is highly desireable. Although approaches for possible sample size reduction in the concurrent control group were suggested previously under the virtual control groups framework for continuous endpoints, methodology that is applicable to binary outcomes that occur in long-term carcinogenicity studies is currently missing. In order to augment animals in the current control group with historical control data, we propose approaches that rely on dynamic Bayesian borrowing and simultaneous credible intervals for risk ratios. Several operation characteristics such as familywise error rate (FWER) and power are assessed via Monte-Carlo simulations and compared to the ones of approaches that rely on pooling of historical and current observations. It turned out that under optimal conditions, Bayesian approaches based on robustified prior distributions enable a substantial reduction of the control groups sample size, while still controlling the FWER up to a satisfactory level. Furthermore, at least to some extend, these approaches were able to protect against possible drift. This hightlights the potential of Bayesian study designs to reduce animal use in toxicology through re-use of the large pool of existing control data.

Including historical control data in simultaneous inference for pre-clinical multi-arm studies

Abstract

In pre- and non-clinical toxicology, the reduction of animal use is highly desireable. Although approaches for possible sample size reduction in the concurrent control group were suggested previously under the virtual control groups framework for continuous endpoints, methodology that is applicable to binary outcomes that occur in long-term carcinogenicity studies is currently missing. In order to augment animals in the current control group with historical control data, we propose approaches that rely on dynamic Bayesian borrowing and simultaneous credible intervals for risk ratios. Several operation characteristics such as familywise error rate (FWER) and power are assessed via Monte-Carlo simulations and compared to the ones of approaches that rely on pooling of historical and current observations. It turned out that under optimal conditions, Bayesian approaches based on robustified prior distributions enable a substantial reduction of the control groups sample size, while still controlling the FWER up to a satisfactory level. Furthermore, at least to some extend, these approaches were able to protect against possible drift. This hightlights the potential of Bayesian study designs to reduce animal use in toxicology through re-use of the large pool of existing control data.
Paper Structure (22 sections, 25 equations, 12 figures, 5 tables)

This paper contains 22 sections, 25 equations, 12 figures, 5 tables.

Figures (12)

  • Figure 1: Simulated family wise error rate (FWER) in the absence of a data-prior conflict ($\mathrm{E}(\pi_h) = \mathrm{E}^(\pi_0) = \pi$).
  • Figure 2: Simulated FWER in the presence of a data-prior conflict. Upper panel: moderate drift ($\mathrm{E}(\pi_0) = \delta \mathrm{E}(\pi_h) = 1.25 \pi$). Lower panel: large drift ($\mathrm{E}(\pi_0) = \delta \mathrm{E}(\pi_h) = 1.50 \pi$).
  • Figure 3: Simulated power to detect at least one increase for moderate treatment effects ($\max(\Delta_m)=1.25$).
  • Figure 4: Simulated power to detect at least one increase for high treatment effects ($\max(\Delta_m)=1.50$).
  • Figure 5: Simulated power to detect at least one increase for severe treatment effects ($\max(\Delta_m)=1.75$).
  • ...and 7 more figures