Improvement of Statistical Models by Considering Correlations among Parameters: Local Anesthetic Agent Simulator for Pharmacological Education - Toshiaki Ara; Hiroyuki Kitamura (BioMedInformatics)
Background: To elucidate the effects of local anesthetic agents (LAs), guinea pigs are used in pharmacological education. Herein, we aimed to develop a simulator for LAs. Previously, we developed a statistical model to simulate the LAs’ effects, and we estimated their parameters (mean [<semantics>μ</semantics>] and logarithm of standard deviation [<semantics>logσ</semantics>]) based on the results of animal experiments. The results of the Monte Carlo simulation were similar to those from the animal experiments. However, the drug parameter values widely varied among individuals, because this simulation did not consider correlations among parameters. Method: In this study, we set the correlations among these parameters, and we performed simulations using Monte Carlo simulation. Results: Weakly negative correlations were observed between <semantics>μ</semantics> and <semantics>logσ</semantics> (<semantics>rμ−logσ</semantics>). In contrast, weakly positive correlations were observed among <semantics>μ</semantics> (<semantics>rμ</semantics>) and among <semantics>logσ</semantics> (<semantics>rlogσ</semantics>). In the Monte Carlo simulation, the variability in duration was significant for small <semantics>rμ−logσ</semantics> values, and the correlation for the duration between two drugs was significant for large <semantics>rμ</semantics> and <semantics>rlogσ</semantics> values. When parameters were generated considering the correlation among the parameters, the correlation of the duration among the drugs became larger. Conclusions: These results suggest that parameter generation considering the correlation among parameters is important to reproduce the results of animal experiments in simulations.