  Statistical methodology for the design and analysis of clinical Phase II dose
response studies, with related software implementation, are well developed for
the case of a normally distributed, homoscedastic response considered for a
single timepoint in parallel group study designs. In practice, however, binary,
count, or time-to-event endpoints are often used, typically measured repeatedly
over time and sometimes in more complex settings like crossover study designs.
In this paper we develop an overarching methodology to perform efficient
multiple comparisons and modeling for dose finding, under uncertainty about the
dose-response shape, using general parametric models. The framework described
here is quite general and covers dose finding using generalized non-linear
models, linear and non-linear mixed effects models, Cox proportional hazards
(PH) models, etc. In addition to the core framework, we also develop a general
purpose methodology to fit dose response data in a computationally and
statistically efficient way. Several examples, using a variety of different
statistical models, illustrate the breadth of applicability of the results. For
the analyses we developed the R add-on package DoseFinding, which provides a
convenient interface to the general approach adopted here.
