trial.simulation.t2e {psBayesborrow}

Simulating time-to-event data for current trial and external control

Description

A two-arm randomized clinical trial with a time-to-event outcome, which is augmented by external control data, is simulated.

Usage

trial.simulation.t2e(
 n.CT, n.CC, nevent.C, n.ECp, nevent.ECp, accrual,
 out.mevent.CT, out.mevent.CC, driftHR,
 cov.C, cov.cor.C, cov.EC, cov.cor.EC, cov.effect,
 seed=sample.int(.Machine$integer.max,1))

Arguments

n.CT

Number of patients in treatment group in the current trial.

n.CC

Number of patients in concurrent control group in the current trial.

nevent.C

Number of events in treatment and concurrent control group in the current trial.

n.ECp

Number of patients in external control pool.

nevent.ECp

Number of events in external control pool.

accrual

Accrual rate, defined as the number of enrolled patients per month.

out.mevent.CT

True median time to event in treatment group in the current trial.

out.mevent.CC

True median time to event in concurrent control group in the current trial.

driftHR

Hazard ratio between concurrent and external control for which the bias should be plotted (hazard in external control divided by hazard in concurrent control).

cov.C

List of covariate distributions for treatment and concurrent control group in the current trial. Continuous and binary covariate are applicable. The continuous covariate is assumed to follow a normal distribution; for example, specified as list(dist="norm", mean=0, sd=1, lab="cov1"). The binary covariate is assumed to follow a binomial distribution; for example, specified as list(dist="binom", prob=0.4, lab="cov2"). lab is the column name of the covariate in the data frame generated.

cov.cor.C

Matrix of correlation coefficients for each pair of covariate for treatment and concurrent control group in the current trial, specified as Gaussian copula parameter.

cov.EC

List of covariate distributions for external control. The continuous covariate is assumed to follow a normal distribution; for example, specified as list(dist="norm", mean=0, sd=1, lab="cov1"). The binary covariate is assumed to follow a binomial distribution; for example, specified as list(dist="binom", prob=0.4, lab="cov2"). lab is the column name of the covariate in the data frame generated, which must be consistent with those used for cov.C.

cov.cor.EC

Matrix of correlation coefficients for each pair of covariate for external control, specified as Gaussian copula parameter.

cov.effect

Vector of covariate effects on the outcome , specified as hazard ratio per one unit increase in continuous covariates or as hazard ratio between categories for binary covariates.

seed

Setting a seed.

Details

The time to event outcome is assumed to follow a Weibull distribution. Given more than one covariates with their effects on the outcome, a Weibull proportional hazards model is constructed for data generation. The data frame generated include the time-to-event outcome data and covariates for n.CT and n.CC patients in treatment and concurrent control group in the current trial respectively, and n.ECp patients in external control pool. One record per patient. More than one covariates must be specified.

Value

The trial.simulation.t2e returns a data frame containing the following variables:

study

Study indicator (0 for external control, and 1 for current trial)

treat

Treatment indicator (0 for concurrent and external control, and 1 for treatment)

time

Time to event or censoring

status

Censoring (0 for censored, and 1 for event occurred)

column name specified

Covariate of interest

Examples

n.CT       <- 100
n.CC       <- 50
nevent.C   <- 100
n.ECp      <- 1000
nevent.ECp <- 800
accrual    <- 16

out.mevent.CT <- 6
out.mevent.CC <- 6
driftHR       <- 1

cov.C <- list(list(dist="norm",mean=0,sd=1,lab="cov1"),
              list(dist="binom",prob=0.4,lab="cov2"))

cov.cor.C <- rbind(c(  1,0.1),
                   c(0.1,  1))

cov.EC <- list(list(dist="norm",mean=0,sd=1,lab="cov1"),
               list(dist="binom",prob=0.4,lab="cov2"))

cov.cor.EC <- rbind(c(  1,0.1),
                    c(0.1,  1))

cov.effect <- c(0.8,0.8)

trial.simulation.t2e(
   n.CT=n.CT, n.CC=n.CC, nevent.C=nevent.C,
   n.ECp=n.ECp, nevent.ECp=nevent.ECp, accrual=accrual,
   out.mevent.CT, out.mevent.CC, driftHR,
   cov.C=cov.C, cov.cor.C=cov.cor.C,
   cov.EC=cov.EC, cov.cor.EC=cov.cor.EC, cov.effect=cov.effect, seed=100)