SEU_GAUSSIAN_raw

Allocates patients to one of treatments based on sequential
estimation-adjusted urn model (SEU) on summarized data.

Provides functions and command-line user interface to generate allocation sequence
by response-adaptive randomization for clinical trials. The package currently supports
two families of frequentist response-adaptive randomization procedures, Doubly Adaptive
Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for
binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square
(or 'ANOVA') hypothesis testing methods are also available in the package to facilitate
the inference for treatment effect. Additionally, the package provides comprehensive and
efficient tools to allow one to evaluate and compare the performance of randomization
procedures and tests based on various criteria. For example, plots for relationship among
assumed treatment effects, sample size, and power are provided. Five allocation functions
for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations
such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001)
<doi:10.1111/j.0006-341X.2001.00909.x> and 'Urn' allocations.

Xiu Huang

RARfreq

Response Adaptive Randomization with 'Frequentist' Approaches

Mengjia Yu

Feifang Hu

Li Wang

Hongjian Zhu

SEU_GAUSSIAN_raw function

<dl><dt>x.df</dt>
<dd>A data frame of two columns: treatment arm and response value.
treatment arm.</dd>
<dt>urn_comp</dt>
<dd>A vector of current urn composition.</dd>
<dt>arms</dt>
<dd>A vector of arm names. If it is not provided, the arms occurred
in x.df will be assumed as all possible arms. Suggest to always assign arms.</dd>
<dt>group_allo</dt>
<dd>An integer of the size of group allocation. The default is
1.</dd>
<dt>add_rule_index</dt>
<dd>Supply a number of 1 or 2 indicting the
addition rules to target allocation functions.
1 = the SEU model targeting Neyman allocation;
2 = the SEU model that assigns probability of 0.6+1/K to winner at each step.
The default is 1.</dd>
<dt>add_rule</dt>
<dd>Supply a user-specified addition rules function of x.df and
arms when add_rule_index is NULL. Default is NULL.</dd></dl>

Arguments

Sequential Estimation-adjusted Urn Model (Gaussian Responses) — SEU_GAUSSIAN_raw

<dl>

<dt>x.df</dt>
<dd>A data frame of two columns: treatment arm and response value.
treatment arm.</dd>


<dt>urn_comp</dt>
<dd>A vector of current urn composition.</dd>


<dt>arms</dt>
<dd>A vector of arm names. If it is not provided, the arms occurred
in x.df will be assumed as all possible arms. Suggest to always assign arms.</dd>


<dt>group_allo</dt>
<dd>An integer of the size of group allocation. The default is
1.</dd>


<dt>add_rule_index</dt>
<dd>Supply a number of 1 or 2 indicting the
addition rules to target allocation functions.
1 = the SEU model targeting Neyman allocation;
2 = the SEU model that assigns probability of 0.6+1/K to winner at each step.
The default is 1.</dd>


<dt>add_rule</dt>
<dd>Supply a user-specified addition rules function of x.df and
arms when add_rule_index is NULL. Default is NULL.</dd>

</dl>

SEU_GAUSSIAN_raw: Sequential Estimation-adjusted Urn Model (Gaussian Responses)

Description

Usage

Value

Arguments

Details

Examples