SEU_BINARY_raw: Sequential Estimation-adjusted Urn Model (Binary Data)

In RARfreq: Response Adaptive Randomization with 'Frequentist' Approaches

Sequential Estimation-adjusted Urn Model (Binary Data)

Description

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

Usage

SEU_BINARY_raw(x.df, urn_comp, arms, group_allo, add_rule_index,
add_rule)

Arguments

x.df	A data frame of two columns: treatment arm and response value.
urn_comp	A vector of current urn composition.
arms	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.
group_allo	An integer of the size of group allocation. The default is 1.
add_rule_index	Supply a number of 1, 2 or 3 indicting the addition rules to target allocation functions. 1 = randomized play-the-winner (RPW) rule that targets the urn allocation 2 = the SEU model that targets Neyman allocation; 3 = the SEU model that targets Rosenberger allocation;' 4 = the SEU model that assigns probability of 0.6+1/K to winner at each step. The default is 1.
add_rule	Supply a user-specified addition rules function of x.df and arms when add_rule_index is NULL. Default is NULL.

Details

'SEU_BINARY_raw' assigns the next subject to a group given the observed data, current urn composition, full list of arm codes, number of group allocation and addition rule function.

Value

Code of arms that the next group of subjects assigned to and the updated urn composition.

Examples

x.df = data.frame(
ARM = sample(LETTERS[1:3],50,replace = TRUE),
RESPONSE = sample(c(0,1),50,replace = TRUE)
)
SEU_BINARY_raw(x.df, urn_comp=c(0,0,0), arms=c("A","B","C"))

x.df = data.frame(
ARM = sample(LETTERS[1:2],40,replace = TRUE),
RESPONSE = sample(c(0,1),40,replace = TRUE)
)
SEU_BINARY_raw(x.df,
urn_comp=c(0,0),
arms=c("A","B"),
group_allo = 1,
add_rule_index = 3)

RARfreq documentation built on May 29, 2024, 5:52 a.m.