sim_flgi_known_var {RARtrials}

Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Known Variances

Description

Function for simulating a trial using the forward-looking Gittins Index rule and the controlled forward-looking Gittins Index rule for continuous outcomes with known variances in trials with 2-5 arms. The conjugate prior distributions follow Normal (N(mean,sd)) distributions and should be the same for each arm.

Usage

sim_flgi_known_var(
  Gittinstype,
  df,
  gittins = NULL,
  Pats,
  nMax,
  TimeToOutcome,
  enrollrate,
  K,
  noRuns2,
  Tsize,
  block,
  rule,
  prior_n,
  prior_mean,
  stopbound,
  mean,
  sd,
  side
)

Arguments

Gittinstype

type of Gittins indices, should be set to 'KV' in this function.

df

discount factor which is the multiplier for loss at each additional patient in the future. Available values are 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99 and 0.995. The maximal sample size can be up to 10000.

gittins

user specified Gittins indices for calculation in this function. If gittins is provided, Gittinstype and df should be NULL.

Pats

the number of patients accrued within a certain time frame indicates the count of individuals who have been affected by the disease during that specific period, for example, a month or a day. If this number is 10, it represents that 10 people have got the disease within the specified time frame.

nMax

the assumed maximum accrued number of patients with the disease in the population, this number should be chosen carefully to ensure a sufficient number of patients are simulated, especially when considering the delay mechanism.

TimeToOutcome

the distribution of delayed response times or a fixed delay time for responses. The delayed time could be a month, a week or any other time frame. When the unit changes, the number of TimeToOutcome should also change. It can be in the format of expression(rnorm( length( vStartTime ),30, 3)), representing delayed responses with a normal distribution, where the mean is 30 days and the standard deviation is 3 days.

enrollrate

probability that patients in the population can enroll in the trial. This parameter is related to the number of people who have been affected by the disease in the population, following an exponential distribution.

K

number of total arms in the trial.

noRuns2

number of simulations for simulated allocation probabilities within each block. Default value is set to 100, which is recommended in (Villar et al. 2015).

Tsize

maximal sample size for the trial.

block

block size.

rule

rules can be used in this function, with values 'FLGI PM', 'FLGI PD' or 'CFLGI'. 'FLGI PM' stands for making decision based on posterior mean; 'FLGI PD' stands for making decision based on posterior distribution; 'CFLGI' stands for controlled forward-looking Gittins Index.

prior_n

a vector representing the number of observations assumed in prior distributions, eg: c(1,1) for a two-armed trial.

prior_mean

a vector representing mean of observations assumed in prior distributions, eg: c(0,0,0) for a three-armed trial, rep(0,K) can be used to simplify the process. If a negative effect is expected, adjust the mean to a negative value.

stopbound

the cut-off value for Z test statistics, which is simulated under the null hypothesis.

mean

a vector of mean hypotheses, for example, c(0.1,0.1) where 0.1 stands for the mean for both groups. Another example is c(0.1,0.3) where 0.1 and 0.3 stand for the mean for the control and a treatment group, respectively.

sd

a vector of standard deviation in hypotheses, for example, as c(0.64,0.64) where 0.64 stands for the standard deviation for both groups. Another example is c(0.64,0.4) where 0.64 and 0.4 stand for the standard deviation for the control and a treatment group, respectively.

side

direction of a one-sided test, with values 'upper' or 'lower'.

Details

This function simulates a trial using the forward-looking Gittins Index rule or the controlled forward-looking Gittins Index rule under both no delay and delayed scenarios. The cut-off value used for stopbound is obtained by simulations using flgi_stop_bound_flgi_known_var. Considering the delay mechanism, Pats (the number of patients accrued within a certain time frame), nMax (the assumed maximum accrued number of patients with the disease in the population) and TimeToOutcome (the distribution of delayed response times or a fixed delay time for responses) are parameters in the functions adapted from https://github.com/kwathen/IntroBayesianSimulation. Refer to the website for more details.

Value

sim_flgi_known_var returns an object of class "flgi". An object of class "flgi" is a list containing final decision based on the Z test statistics with 1 stands for selected and 0 stands for not selected, final decision based on the maximal Gittins Index value at the final stage, Z test statistics, the simulated data set and participants accrued for each arm at the time of termination of that group in one trial. The simulated data set includes 5 columns: participant ID number, enrollment time, observed time of results, allocated arm, and participants' result.

References

Williamson SF, Villar S (2019). “A Response‐Adaptive Randomization Procedure for Multi‐Armed Clinical Trials with Normally Distributed Outcomes.” Biometrics, 76. doi:10.1111/biom.13119.

Examples

#The forward-looking Gittins Index rule with delayed responses follow a normal distribution
#with a mean of 30 days and a standard deviation of 3 days

sim_flgi_known_var(Gittinstype='KV',df=0.995,Pats=10,nMax=50000,
TimeToOutcome=expression(rnorm( length( vStartTime ),30, 3)),enrollrate=0.5,
K=3,noRuns2=100,Tsize=852,block=20,rule='FLGI PM',prior_n=rep(1,3),
prior_mean=rep(9/100,3),stopbound=(-2.1725),mean=c(9.1/100,8.83/100,8.83/100),
sd=c(0.009,0.009,0.009),side='lower')

#The controlled forward-looking Gittins Index rule with delayed responses follow a 
#normal distribution with a mean of 30 days and a standard deviation of 3 days

sim_flgi_known_var(Gittinstype='KV',df=0.995,Pats=10,nMax=50000,
TimeToOutcome=expression(rnorm( length( vStartTime ),30, 3)),enrollrate=0.1,
K=3,noRuns2=100,Tsize=852,block=20,rule='CFLGI',prior_n=rep(1,3),
prior_mean=rep(9/100,3),stopbound=(-2.075),mean=c(9.1/100,8.83/100,8.83/100),
sd=c(0.009,0.009,0.009),side='lower')