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Analysis of dose–response in flexible dose titration clinical studies
Author(s) -
Xu Xu Steven,
Yuan Min,
Nandy Partha
Publication year - 2012
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1498
Subject(s) - covariate , estimator , titration , statistics , clinical trial , selection (genetic algorithm) , medicine , computer science , econometrics , mathematics , chemistry , machine learning , inorganic chemistry
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. Copyright © 2012 John Wiley & Sons, Ltd.