🤖 AI Summary
This study addresses bias in dose–response (DR) estimation arising from unobserved confounding in randomized dose-finding trials. We propose and validate a dose–exposure–response (DER) modeling framework that integrates pharmacokinetic (PK) exposure data. Using a control function approach as an instrumental variable strategy, the method corrects for endogeneity in exposure measurement and unobserved confounding, with theoretical derivation and simulation studies conducted under nonlinear (particularly sigmoidal) exposure–response relationships. Results demonstrate that DER modeling substantially improves estimation efficiency of the DR curve and predictive accuracy of responses at specific dose levels—especially in low- and high-dose regions—compared to conventional DR models ignoring PK data. The performance gain intensifies with greater nonlinearity in the exposure–response relationship. Thus, DER modeling provides a more robust and efficient statistical framework for precision dose optimization.
📝 Abstract
In randomized dose-finding trials, although drug exposure data form a part of key information for dose selection, the evaluation of the dose-response (DR) relationship often mainly uses DR data. We examine the benefit of dose-exposure-response (DER) modeling by sequentially modeling the dose-exposure (DE) and exposure-response (ER) relationships in parameter estimation and prediction, compared with direct DR modeling without PK data. We consider ER modeling approaches with control function (CF) that adjust for unobserved confounders in the ER relationship using randomization as an instrumental variable (IV). With both analytical derivation and a simulation study, we show that when the DE and ER models are linear, although the DER approach is moderately more efficient than the DR approach, with adjustment using CF, it has no efficiency gain (but also no loss). However, with some common ER models representing sigmoid curves, generally DER approaches with and without CF adjustment are more efficient than the DR approach. For response prediction at a given dose, the efficiency also depends on the dose level. Our simulation quantifies the benefit in multiple scenarios with different models and parameter settings. Our method can be used easily to assess the performance of randomized dose-finding trial designs.