Simulation-based Estimation of Relative Risk in Pharmacometric Analyses: How Much Do We Know About My Virtual Twin?

Music City Center 

Pharmacometric "population simulations" are often used to determine whether special subpopulations, such as the renally or hepatically impaired, have elevated risk of adverse events as a result of elevated pharmacokinetic exposure to a drug. When such simulations are summarized using non-comparative statistics, e.g. P( AE if given high dose ) and P( AE if given low dose ), this simulation-based methodology conforms to the logic of "standardization" / g-formula, and therefore results in valid estimates if the outcome models are correct (1). On the other hand, when such simulations are used to obtain estimates of the causal relative risk P( AE if given high dose ) / P( AE if given low dose ), the path forward for the pharmacometric analyst is less clear. We consider two methods of summarizing population simulations in terms of relative risk and evaluate the degree to which each conforms to the principles of subgroup mixable estimation (2). Enhanced dialogue between the pharmacometric, biostatistics, and machine learning communities requires an aligned understanding of this issue as all three communities progress toward increased usage of "virtual twins".


(1) Rogers, James A., Hugo Maas, and Alejandro Pérez Pitarch. 2023. "An Introduction to Causal Inference for Pharmacometricians." CPT: Pharmacometrics & Systems Pharmacology 12 (1): 27–40.

(2) Liu, Yi, Bushi Wang, Miao Yang, Jianan Hui, Heng Xu, Siyoen Kil, and Jason C. Hsu. 2022. "Correct and Logical Causal Inference for Binary and Time-to-Event Outcomes in Randomized Controlled Trials." Biometrical Journal 64 (2): 198–224.