normaldata

A dataset containing the results of 300 patients with continuous (normal) outcome,
the dataset is filled with loss to follow up.

dataset

Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types,
incorporates historical data and allows early stopping for futility or early success. The package uses novel
and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up,
and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data
into the analysis via the power prior or non-informative priors.

Thevaa Chandereng

bayesCT

Simulation and Analysis of Adaptive Bayesian Clinical Trials

Donald Musgrove

Tarek Haddad

Graeme Hickey

Timothy Hanson

Theodore Lystig

normaldata function

A data frame with 300 rows and 4 variables:<dl class="dl-horizontal">
 <dt>id</dt><dd>Patient ID in the trial</dd>
 <dt>treatment</dt><dd>treatment assignment for patients, 1 for treatment group
 0 for control group</dd>
 <dt>outcome</dt><dd>continuous outcome of the trial (gaussian distribution)</dd>
 <dt>complete</dt><dd>1 for complete outcome, 0 for loss to follow up</dd>
</dl>

Format

Gaussian dataset for analyzing adaptive Bayesian trials — normaldata

A data frame with 300 rows and 4 variables:<dl class='dl-horizontal'>
 <dt>id</dt><dd>Patient ID in the trial</dd>
 <dt>treatment</dt><dd>treatment assignment for patients, 1 for treatment group
 0 for control group</dd>
 <dt>outcome</dt><dd>continuous outcome of the trial (gaussian distribution)</dd>
 <dt>complete</dt><dd>1 for complete outcome, 0 for loss to follow up</dd>
</dl>

normaldata: Gaussian dataset for analyzing adaptive Bayesian trials

Description

Usage

Arguments

Format

Examples