dabcd_max_power

<code>dabcd_max_power</code> can be used for doubly adaptive biased coin design with maximal power
strategy for binary outcomes, targeting generalized Neyman allocation and generalized RSIHR allocation. The return 
of this function is a vector of allocation probabilities to each arm, with the pre-specified number of participants in the trial.

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

Chuyao Xu

RARtrials

Response-Adaptive Randomization in Clinical Trials

Thomas Lumley

Alain Vandal

Sofia S. Villar

Kyle J. Wathen

dabcd_max_power function

<dl><dt>NN</dt>
<dd>a vector representing the number of participants with success results for each arm
estimated from the current data.</dd>
<dt>Ntotal1</dt>
<dd>a vector representing the total number of participants for each arm
estimated from the current data.</dd>
<dt>armn</dt>
<dd>number of total arms in the trial.</dd>
<dt>BB</dt>
<dd>the minimal allocation probability for each arm, which is within the
range of \([0,1/armn]\).</dd>
<dt>type</dt>
<dd>allocation type, with choices from 'RSIHR' and 'Neyman'.</dd>
<dt>dabcd</dt>
<dd>an indicator of whether to apply Hu &amp; Zhang's formula (Hu2004RARtrials), with choices from FALSE and TRUE.
TRUE represents allocation probabilities calculated using Hu &amp; Zhang's formula;
FALSE represents allocation probabilities calculated before applying Hu &amp; Zhang's formula.
Default value is set to FALSE.</dd>
<dt>gamma</dt>
<dd>tuning parameter in Hu &amp; Zhang's formula (Hu2004RARtrials). When <code>dabcd</code>=FALSE, this parameter does not need
to be specified. Default value is set to 2.</dd></dl>

Arguments

Chuyao Xu, Thomas Lumley, Alain Vandal

Author

Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint — dabcd_max_power

<dl>

<dt>NN</dt>
<dd>a vector representing the number of participants with success results for each arm
estimated from the current data.</dd>


<dt>Ntotal1</dt>
<dd>a vector representing the total number of participants for each arm
estimated from the current data.</dd>


<dt>armn</dt>
<dd>number of total arms in the trial.</dd>


<dt>BB</dt>
<dd>the minimal allocation probability for each arm, which is within the
range of \([0,1/armn]\).</dd>


<dt>type</dt>
<dd>allocation type, with choices from 'RSIHR' and 'Neyman'.</dd>


<dt>dabcd</dt>
<dd>an indicator of whether to apply Hu &amp; Zhang's formula (Hu2004RARtrials), with choices from FALSE and TRUE.
TRUE represents allocation probabilities calculated using Hu &amp; Zhang's formula;
FALSE represents allocation probabilities calculated before applying Hu &amp; Zhang's formula.
Default value is set to FALSE.</dd>


<dt>gamma</dt>
<dd>tuning parameter in Hu &amp; Zhang's formula (Hu2004RARtrials). When <code>dabcd</code>=FALSE, this parameter does not need
to be specified. Default value is set to 2.</dd>

</dl>

dabcd_max_power: Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint

Description

Usage

Value

Arguments

Author

Details

References

Examples