dose_paths

<p>A dose-escalation design exists to select doses in response to observed
outcomes. The entire space of possible responses can be calculated to show
the behaviour of a design in response to all feasible outcomes. The
<code>get_dose_paths</code> function performs that task and returns an
instance of this object.</p>

Methods for working with dose-finding clinical trials. We provide
implementations of many dose-finding clinical trial designs, including the
continual reassessment method (CRM) by O'Quigley et al. (1990)
<doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design
by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal
interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of
Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3
described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
are implemented with a common interface. We also offer optional additional
classes to tailor the behaviour of all designs, including avoiding skipping
doses, stopping after n patients have been treated at the recommended dose,
stopping when a toxicity condition is met, or demanding that n patients are
treated before stopping is allowed. By daisy-chaining together these classes
using the pipe operator from 'magrittr', it is simple to tailor the
behaviour of a dose-finding design so it behaves how the trialist wants.
Having provided a flexible interface for specifying designs, we then provide
functions to run simulations and calculate dose-paths for future cohorts of
patients.

dose_paths: Dose pathways

Description

Usage

Arguments

See Also

Examples