dosing_schedule_df

A data frame with the following columns:<dl>
<dt><code>kit</code></dt>
<dd>The numeric code of the kit type.</dd><dt><code>target_days</code></dt>
<dd>Number of days per treatment cycle.</dd><dt><code>target_dose</code></dt>
<dd>Dose per treatment cycle.</dd><dt><code>max_cycles</code></dt>
<dd>Maximum number of treatment cycles.</dd>
</dl>

datasets

Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Kaifeng Lu

drugDemand

Drug Demand Forecasting

dosing_schedule_df function

An object of class <code>tbl_df</code> (inherits from <code>tbl</code>, <code>data.frame</code>) with 4 rows and 4 columns.

Format

A data frame with the following columns:<dl>
<dt><code>kit</code></dt>
<dd>The numeric code of the kit type.</dd>

<dt><code>target_days</code></dt>
<dd>Number of days per treatment cycle.</dd>

<dt><code>target_dose</code></dt>
<dd>Dose per treatment cycle.</dd>

<dt><code>max_cycles</code></dt>
<dd>Maximum number of treatment cycles.</dd>


</dl>

dosing_schedule_df: The dosing schedule data frame.

Description

Usage

Arguments

Format