Abstract
We consider optimal design problems for dose-finding studies with censored Weibull time-to-event outcomes. Locally D-optimal designs are investigated for a quadratic dose–response model for log-transformed data subject to right censoring. Two-stage adaptive D-optimal designs using maximum likelihood estimation (MLE) model updating are explored through simulation for a range of different dose–response scenarios and different amounts of censoring in the model. The adaptive optimal designs are found to be nearly as efficient as the locally D-optimal designs. A popular equal allocation design can be highly inefficient when the amount of censored data is high and when the Weibull model hazard is increasing. The issues of sample size planning/early stopping for an adaptive trial are investigated as well. The adaptive D-optimal design with early stopping can potentially reduce study size while achieving similar estimation precision as the fixed allocation design.
Highlights
Dose–response studies play an important role in clinical drug development
We report results of a simulation study to investigate operating characteristics of two non-adaptive designs and a two-stage adaptive D-optimal design using maximum likelihood estimation (MLE) updating under 24 different dose–response scenarios and different amount of censoring in the model
We present simulation results in a more complex setting, assuming the trial has pre-specified criteria which can potentially enable stopping of the trial once model parameters have been estimated with due precision, thereby potentially reducing the total sample size in the study
Summary
Dose–response studies play an important role in clinical drug development. Such studies are typically randomized, multi-armed, placebo-controlled parallel group designs involving several dose levels of an investigational drug. The study goals may be to estimate the drug’s dose–response profile with respect to some primary outcome measure and to identify a dose or doses to be tested in subsequent confirmatory phase III trials. Optimization of a trial design can allow an experimenter to achieve study objectives most efficiently with a given sample size. Many clinical trials use time-to-event outcomes as primary study endpoints. The outcome could be, for example, progression-free survival in oncology, duration of viral shredding in virology, time from treatment administration until pain symptoms disappear in studies of migraine, time to onset/duration of anesthesia in dentistry, or time to first relapse in multiple sclerosis.
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