crm_codified_dose_logistic

Calculate the codified CRM doses that map to probability of
toxicity <code>prob_tox</code> in a logistic model with expected values for
intercept and gradient. I.e. find \(x[i]\) such that
\(logit(p[i]) = \alpha + \beta x[i]\), were \(p\) is
<code>prob_tox</code>.

A collection of clinical trial designs and methods, implemented in
'rstan' and R, including: the Continual Reassessment Method by O'Quigley et
al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004)
<doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of
Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the
Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and
more. We provide functions to aid model-fitting and analysis.
The 'rstan' implementations may also serve as a cookbook to anyone looking
to extend or embellish these models. We hope that this package encourages
the use of Bayesian methods in clinical trials. There is a preponderance of
early phase trial designs because this is where Bayesian methods are used
most. If there is a method you would like implemented, please get in touch.

Kristian Brock

trialr

Clinical Trial Designs in 'rstan'

Trustees of Columbia University

crm_codified_dose_logistic function

<dl><dt>prob_tox</dt>
<dd>Numeric vector, seek codified doses that yield these
probabilities of toxicity.</dd>
<dt>alpha_mean</dt>
<dd>Numeric, expected value of intercept.</dd>
<dt>beta_mean</dt>
<dd>Numeric, expected value of gradient with respect to dose.</dd></dl>

Arguments

Calculate codified CRM doses. — crm_codified_dose_logistic

<dl>

<dt>prob_tox</dt>
<dd>Numeric vector, seek codified doses that yield these
probabilities of toxicity.</dd>


<dt>alpha_mean</dt>
<dd>Numeric, expected value of intercept.</dd>


<dt>beta_mean</dt>
<dd>Numeric, expected value of gradient with respect to dose.</dd>

</dl>

crm_codified_dose_logistic: Calculate codified CRM doses.

Description

Usage

Value

Arguments

Examples