Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships Written by an applied statistician with extensive practical experience in drug development, Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling, from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book describes using measurement error models to treat sequential modeling, fitting models with exposure and response driven by complex dynamics, and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis, causal inference to eliminate confounding biases, and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation. Many examples illustrate the use of exposure-response modeling in experimental toxicology, clinical pharmacology, epidemiology, and drug safety.
Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios. Although application oriented, this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition, students can see how complicated methodology and models are applied to practical situations.
Introduction Multifaceted exposure-response relationships Practical scenarios in ER modeling Models and modeling in exposure-response analysis Model-based decision-making and drug development Drug regulatory guidance for analysis of exposure-response relationship Examples and modeling software Basic exposure and exposure-response models Models based on pharmacological mechanisms Statistical models Transformations Semiparametric and nonparametric models Comments and bibliographic notes Dose-exposure and exposure-response models for longitudinal data Linear mixed models for exposure-response relationships Modeling exposures with linear mixed models Nonlinear mixed ER models Modeling exposure with a population PK model Mixed effect models specified by differential equations Generalized linear mixed model and generalized estimating equation Generalized nonlinear mixed models Testing variance components in mixed models Nonparametric and semiparametric models with random effects On distributions of random effects Bibliographic notes Sequential and simultaneous exposure-response modeling Joint models for exposure and response Simultaneous modeling of exposure and response models Sequential exposure-response modeling Measurement error models and regression calibration Instrumental variable methods Modeling multiple exposure and response Internal validation data and partially observed and surrogate exposure measures Comments and bibliographic notes Exposure-risk modeling for time-to-event data An example Basic concepts and models for time-to-event data Dynamic exposure model as a time varying covariate Multiple TTE and competing risks Models for recurrent events Frailty: Random effects in TTE models Joint modeling of exposure and time to event Interval censored data Model identification and misspecification Random sample simulation from exposure-risk models Comments and bibliographic notes Modeling dynamic exposure-response relationships Effect compartment models Indirect response models Disease process models Fitting dynamic models for longitudinal data Semiparametric and nonparametric approaches Dynamic linear and generalized linear models Testing hysteresis Comments and bibliographic notes Bayesian modeling and model-based decision analysis Bayesian modeling Bayesian decision analysis Decisions under uncertainty and with multiple objectives Evidence synthesis and mixed treatment comparison Comments and bibliographic notes Confounding bias and causal inference in exposure-response modeling Introduction Confounding factors and confounding biases Causal effect and counterfactuals Classical adjustment methods Directional acyclic graphs Bias assessment Instrumental variable Joint modeling of exposure and response Study designs robust to confounding bias or allowing the use of instrument variables Doubly robust estimates Comments and bibliographic notes Dose-response relationship, dose determination, and adjustment Marginal dose-response relationships Dose-response relationship as a combination of dose-exposure and exposure-response relationships Dose determination: Dose-response or dose-exposure-response modeling approaches? Dose adjustment Dose adjustment and causal effect estimation Sequential decision analysis Dose determination: Design issues Comments and bibliographic notes Implementation using software Two key elements: Model and data Linear mixed and generalized linear mixed models Nonlinear mixed models A very quick guide to NONMEM Appendix Bibliography Index