Description Usage Arguments Value See Also Examples
Considers MITT data collected through an interim timepoint and generates independent timetoevent datasets, by treatment arm, to assess the distribution of the number of treatment armspecific endpoints at the end of the followup period. A Bayesian model for treatment armspecific endpoint rates is used for generating future data (see the vignette).
1 2 3 4 5 6  completeTrial.byArm(interimData, nTrials, trtNames, N, enrollRate = NULL,
enrollRatePeriod, eventPriorWeight, eventPriorRate,
fixedDropOutRate = NULL, ppAnalysis = FALSE, missVaccProb = NULL,
ppAtRiskTimePoint = NULL, fuTime, visitSchedule,
visitSchedule2 = NULL, saveFile = NULL, saveDir = NULL,
randomSeed = NULL)

interimData 
a data frame capturing observed MITT data at an interim timepoint that contains one row per enrolled participant in the MITT cohort and the following variables: 
nTrials 
the number of trials to be simulated 
trtNames 
a character vector of treatment labels as specified in 
N 
a numeric vector specifying the target number of enrolled participants in each treatment arm, with the arms in the same order as in 
enrollRate 
a treatment armpooled weekly enrollment rate used for completing enrollment if 
enrollRatePeriod 
the length (in weeks) of the time period preceding the time of the last enrolled participant in 
eventPriorWeight 
a numeric value in [0,1] representing a weight assigned to the prior gamma distribution of the treatment armspecific event rates at the time when 50% of the estimated persontime at risk in each arm has been accumulated (see the vignette) 
eventPriorRate 
a numeric vector of treatment armspecific prior mean incidence rates for the endpoint, expressed as numbers of events per personyear at risk, with the arms in the same order as in 
fixedDropOutRate 
the pretrial assumed annual treatment armpooled dropout rate. If 
ppAnalysis 
a logical value ( 
missVaccProb 
a probability that a participant misses at least one vaccination. If 
ppAtRiskTimePoint 
a minimal followup time (in weeks) for a participant to qualify for inclusion in the perprotocol cohort ( 
fuTime 
a followup time (in weeks) of each participant 
visitSchedule 
a numeric vector of visit weeks at which testing for the endpoint is conducted 
visitSchedule2 
a numeric vector of visit weeks at which testing for the endpoint is conducted in a subset of participants (e.g., those who discontinue administration of the study product but remain in followup). If 
saveFile 
a character string specifying an 
saveDir 
a character string specifying a path for the output directory. If supplied, the output is saved as an 
randomSeed 
seed of the random number generator for simulation reproducibility 
If saveDir
is specified, the output list (named trialObj
) is saved as an .RData
file; otherwise it is returned. The output object is a list with the following components:
trialData
: a list with nTrials
components each of which is a data.frame
with the variables arm
, entry
, exit
, event
, and dropout
storing the treatment assignments, enrollment times, study exit times, event indicators, and dropout indicators, respectively. The observed followup times can be recovered as exit
 entry
. If ppAnalysis=TRUE
, then the indicators of belonging to the perprotocol cohort (named pp
) are included.
nTrials
: the number of simulated trials
N
: the total number of enrolled trial participants
rates
: a list with three components:
enrollRate
: the treatment armpooled weekly enrollment rate
dropRate
: fixedDropOutRate
, or, if NULL
, the annual treatment armpooled dropout rate in interimData
eventPostRate
: a list with length(trtNames)
components (labeled by the levels of the arm
variable in interimData
) each of which is a numeric vector of length nTrials
of the sampled treatment armspecific posterior annual event rates
BetaOverBetaPlusTk
: a list with length(trtNames)
components (labeled by the levels of the arm
variable in interimData
) each of which is the armspecific weight placed on the prior mean event rate
TkOverTstar
: a list with length(trtNames)
components (labeled by the levels of the arm
variable in interimData
) each of which is the ratio of the observed armspecific persontime at risk to the estimated total armspecific persontime at risk, with the armspecific event rates set equal to the components of eventPriorRate
in the estimator for the total armspecific persontime at risk
randomSeed
: seed of the random number generator for simulation reproducibility
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  arm < rep(c("C3","T1","T2"), each=250)
schedule < rbinom(length(arm), 1, 0.01)
entry < rpois(length(arm), lambda=60)
entry < entry  min(entry)
last_visit_dt < entry + runif(length(arm), min=0, max=80)
event < rbinom(length(arm), 1, 0.01)
dropout < rbinom(length(arm), 1, 0.02)
dropout[event==1] < 0
exit < rep(NA, length(arm))
exit[event==1] < last_visit_dt[event==1] + 5
exit[dropout==1] < last_visit_dt[dropout==1] + 5
followup < ifelse(event==1  dropout==1, 0, 1)
interimData < data.frame(arm=arm, schedule2=schedule, entry=entry, exit=exit,
last_visit_dt=last_visit_dt, event=event, dropout=dropout, complete=0, followup=followup)
completeData < completeTrial.byArm(interimData=interimData, nTrials=5,
trtNames=c("C3","T1","T2"), N=c(500,500,500), enrollRatePeriod=24, eventPriorWeight=0.5,
eventPriorRate=c(0.001,0.0004,0.0004), fuTime=80, visitSchedule=seq(0, 80, by=4),
visitSchedule2=c(0,seq(from=8,to=80,by=12)), randomSeed=9)
### alternatively, to save the .RData output file (no '<' needed):
completeTrial.byArm(interimData=interimData, nTrials=5, trtNames=c("C3","T1","T2"),
N=c(500,500,500), enrollRatePeriod=24, eventPriorWeight=0.5,
eventPriorRate=c(0.001,0.0004,0.0004), fuTime=80, visitSchedule=seq(0, 80, by=4),
visitSchedule2=c(0,seq(from=8,to=80,by=12)), saveDir="./", randomSeed=9)

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