title: Analysis of dose-response in flexible dose titration clinical studies.
abstract: Assessing dose-response from flexible-dose clinical trials (e.g., titration or dose escalation studies) is challenging and often problematic due to the selection bias caused by 'titration-to-response'. We investigate the performance of a dynamic linear mixed-effects (DLME) model and marginal structural model (MSM) in evaluating dose-response from flexible-dose titration clinical trials via simulations. The simulation results demonstrated that DLME models with previous exposure as a time-varying covariate may provide an unbiased and efficient estimator to recover exposure-response relationship from flexible-dose clinical trials. Although the MSM models with independent and exchangeable working correlations appeared to be able to recover the right direction of the dose-response relationship, it tended to over-correct selection bias and overestimated the underlying true dose-response. The MSM estimators were also associated with large variability in the parameter estimates. Therefore, DLME may be an appropriate modeling option in identifying dose-response when data from fixed-dose studies are absent or a fixed-dose design is unethical to be implemented.
authors.affiliation: Advanced PK-PD Modeling and Simulation, Clinical Pharmacology, Janssen Research & Development, Titusville, NJ, USA. sxu26@its.jnj.com
authors.forename: Xu Steven
authors.initials: XS
authors.lastname: Xu
authors.forename: Min
authors.initials: M
authors.lastname: Yuan
authors.forename: Partha
authors.initials: P
authors.lastname: Nandy
mesh_terms: D003198:Computer Simulation; D018849:Controlled Clinical Trials as Topic / Q000379:methods*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D015983:Selection Bias; D013997:Time Factors
chemical_list: D004364:Pharmaceutical Preparations