  Recently, phase II trials with multiple schedules (frequency of
administrations) have become more popular, for instance in the development of
treatments for atopic dermatitis. If the relationship of the dose and response
is described by a parametric model, a simplistic approach is to pool doses from
different schedules. However, this approach ignores the potential heterogeneity
in dose-response curves between schedules. A more reasonable approach is the
partial pooling, i.e. certain parameters of the dose-response curves are
shared, while others are allowed to vary. Rather than using schedule-specific
fixed-effects, we propose a Bayesian hierarchical model with random-effects to
model the between-schedule heterogeneity with regard to certain parameters.
Schedule-specific dose-response relationships can then be estimated using
shrinkage estimation. Considering Emax models, the proposed method displayed
desirable performance in terms of the mean absolute error and the coverage
probabilities for the dose-response curve compared to the complete pooling.
Furthermore, it outperformed the partial pooling with schedule-specific
fixed-effects by producing lower mean absolute error and shorter credible
intervals. The methods are illustrated using simulations and a phase II trial
example in atopic dermatitis. A publicly available R package, \texttt{ModStan},
is developed to automate the implementation of the proposed method
(\href{https://github.com/gunhanb/ModStan}{https://github.com/gunhanb/ModStan}).
