binomialfreq: Block Design for Response-Adaptive Randomization for Binomial...

Description Usage Arguments Value Examples

View source: R/binomialfreq.R

Description

Simulation for binomial counts for block design for response-adaptive randomization with time as a confounding

Usage

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binomialfreq(p_control, p_treatment, N_total, block_number = 4,
  drift = 0, simulation = 10000, conf_int = 0.95,
  alternative = "greater", correct = FALSE, replace = TRUE,
  early_stop = FALSE, size_equal_randomization = 20,
  min_patient_earlystop = 20, max_prob = 0.8)

Arguments

p_control

scalar. Proportion of events under the control arm.

p_treatment

scalar. Proportion of events under the treatment arm.

N_total

scalar. Total sample size.

block_number

scalar. Number of blocks or time levels. The default is set to 4. If block_number is set to 1. This is a traditional RCT design.

drift

scalar. The increase or decrease in proportion of event over time. In this case, the proportion of failure changes in each block by the number of patient accured over the total sample size. The full drift effect is seen in the final block.

simulation

scalar. Number of simulation to be ran. The default is set to 10000.

conf_int

scalar. Confidence level of the interval.

alternative

character. A string specifying the alternative hypothesis, must be one of "less" or "greater" (default).

correct

logical. A logical indicating whether to apply continuity correction when computing the test statistic: one half is subtracted from all |O - E| differences; however, the correction will not be bigger than the differences themselves.

replace

logical. should sampling be with replacement? If replace is set to FALSE (default), the 0 for control, 1 for treatment is replicated to the closest integer and this vector is sampled with no replacement. If replace is set to TRUE, the sampling is done based on randomization ratio provided with replacement.

early_stop

logical. A logical indicating whether the trials are stopped early for success or futility.

size_equal_randomization

scalar. The number of run in patients because adaptive randomization is applied.

min_patient_earlystop

scalar. Minimum number of patients before early stopping rule is applied.

max_prob

scalar. The maximum probability for assigning to treatment/control group is 0.8.

Value

a list with details on the simulation.

power

scalar. The power of the trial, ie. the proportion of success over the number of simulation ran.

p_control_estimate

scalar. The estimated proportion of events under the control group.

p_treatment_estimate

scalar. The estimated proportion of events under the treatment group.

N_enrolled

vector. The number of patients enrolled in the trial (sum of control and experimental group for each simulation. )

N_control

vector. The number of patients enrolled in the control group for each simulation.

N_control

vector. The number of patients enrolled in the experimental group for each simulation.

randomization_ratio

matrix. The randomization ratio allocated for each block.

Examples

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binomialfreq(p_control = 0.7, p_treatment = 0.65, N_total = 200,
            block_number = 2, simulation = 100)
binomialfreq(p_control = 0.5, p_treatment = 0.40, N_total = 200,
            block_number = 2, simulation = 100, drift = -0.15)

blockRAR documentation built on March 26, 2020, 5:52 p.m.