Application of machine learning based methods in exposure-response analysis

Robust estimation of exposure response analysis relies on correct specification of the model structure with traditional parametric approach. However, the assumptions of the handcrafted model may not always hold or verifiable. Here, we conducted a simulation study to assess the performance of machine learning-based techniques in exposure-response (E-R) analysis where data were generated by a complicated nonlinear system under one dose level. Two analysis options involving machine learning were evaluated. The first option was based on marginal structural model with inverse probability weighting, where machine learning (ML) was employed to improve the performance of propensity score estimation. The simulation results showed that propensity score predicted by ML was more robust than traditional multinomial logistic regression in terms of adjusting the confounding effects and unbiasedly estimating the E-R relationship. The second option estimated the E-R relationship by employing artificial neural network as a universal function approximator to the data generating mechanism, without the requirement of accurately hand-crafting the whole simulation system. The results demonstrated that the trained network was able to correctly predict the treatment effects across a certain range of adjacent dose levels. In contrast, traditional regression provided biased predictions, even when all confounders were included in the model. Our study demonstrated that ML may serve as a powerful tool for pharmacometrics analysis with its prediction flexibility in a nonlinear system and its capacity of approximating the ground truth.