RARfreq (version 0.1.4)

SEU_BINARY_raw: 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)

Value

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

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.

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)