Description Usage Arguments Value See Also Examples
Considers MITT data collected through an interim timepoint and generates independent timetoevent datasets, ignoring treatment assignments, to assess the distribution of the number of treatment armpooled endpoints at the end of the followup period. A Bayesian model for the treatment armpooled endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette).
1 2 3 4 5 6 7  completeTrial.pooledArms(interimData, nTrials, N, enrollRate = NULL,
enrollRatePeriod = NULL, eventPriorWeight, eventPriorRate = NULL,
fixedDropOutRate = NULL, ppAnalysis = FALSE, missVaccProb = NULL,
ppAtRiskTimePoint = NULL, fuTime, mixture = FALSE,
mix.weights = NULL, eventPriorWeightRobust = NULL, 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 
N 
the total target number of enrolled participants 
enrollRate 
a treatment armpooled weekly enrollment rate used for completing enrollment if fewer than 
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 armpooled event rate at the time when 50% of the estimated total persontime at risk has been accumulated (see the vignette) 
eventPriorRate 
a numeric value of a treatment armpooled prior mean incidence rate for the endpoint, expressed as the number of events per personyear at risk. If 
fixedDropOutRate 
the pretrial assumed annual 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 
mixture 
a logical value indicating whether to use the robust mixture approach (see the vignette). If equal to 
mix.weights 
a numeric vector of length 2 representing prior weights (values in [0,1]) of the informative and the weakly informative component, respectively, of the prior gammamixture distribution of the treatment armpooled event rate. The two weights must sum up to 1. If 
eventPriorWeightRobust 
a numeric value representing the weight w used to calculate the β parameter of the weakly informative gamma distribution in the mixture prior. If 
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 numeric vector of length nTrials
of the treatment armpooled annual event rates sampled from the posterior distribution
BetaOverBetaPlusTk
: the weight placed on the prior mean event rate
TkOverTstar
: the ratio of the observed persontime at risk to the estimated total persontime at risk, with the event rate set equal to eventPriorRate
in the estimator for the total persontime at risk
randomSeed
: seed of the random number generator for simulation reproducibility
w.post
: the weights, summing up to 1, of the gamma components of the posterior mixture distribution of the treatment armpooled event rate. If mixture=FALSE
, then w.post=NA
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  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.pooledArms(interimData=interimData, nTrials=5, N=1500,
enrollRatePeriod=24, eventPriorWeight=0.5, eventPriorRate=0.001, 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.pooledArms(interimData=interimData, nTrials=5, N=1500,
enrollRatePeriod=24, eventPriorWeight=0.5, eventPriorRate=0.001, 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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