R: Implement a Multi-Stage Phase I Dose-Finding Design to...

RunPRMD {phase1PRMD}

R Documentation

Implement a Multi-Stage Phase I Dose-Finding Design to recommend dosage selection based on the data collected in the available patient cohorts

Description

A function to implement a Multi-Stage Phase I Dose-Finding Design to recommend dosage selection based on the data collected in the available patient cohorts. The available models include 1-stage model with/without individualized dose modification, 3-stage model with/without individualized dose modification, 3-stage model with individualized dose modification on stage II and 3-stage model with individualized dose modification on stage I and dose modification on stage II.

Usage

RunPRMD(
  seed = 1234,
  patlist,
  patID_act = NULL,
  cycle_act = NULL,
  dose_act = NULL,
  dlt_act = NULL,
  doses = 1:6,
  cycles = 1:6,
  tox.target = 0.28,
  p_tox1 = 0.2,
  p_tox2 = 0.2,
  trialSize = 36,
  chSize = 3,
  thrd1 = 0.28,
  thrd2 = 0.28,
  proxy.thrd = 0.1,
  param.ctrl = list(),
  n.iters = 10000,
  burn.in = 5000,
  thin = 2,
  n.chains = 1,
  effcy.flag = T,
  ICD.flag = T,
  DLT.drop.flag = T,
  testedD = T,
  IED.flag = T,
  ICD_thrd = 0.3
)

Arguments

`seed`	The seed of R's random number generator. Default is 1234
`patlist`	A list of the patient treatment records, which must contains the following variables: PatID denotes the patient ID where the elements are specified by cohort and subject number. For example, "cohort2subject3" denotes the third subject in the second cohort dose records the dose level assigned for each patient through the whole treatment cycle shows the treatment cycle information of each record nTTP records the corresponding nTTP score. dlt indicates whether a DLT event is observed or not? efficacy provides the continuous efficacy for each cycle. Required when `effcy.flag == T`. The range of efficacy is (0, 1), use -1 for missing efficacy response. See `patlist_sim` for an example.
`patID_act`	A vector recording the patients' ID who need dose recommendation for next cycle. Default is `NULL`
`cycle_act`	A vector recording the current cycle of patID_act. Default is `NULL`
`dose_act`	A vector recording the current dose level of patID_act. Default is `NULL`
`dlt_act`	A vector indicating whether a dlt is observed in current cycle for current patients. Default is `NULL`
`doses`	A vector of doses that users are going to explore. Default is 1:6, where dose 1 through dose 6 are being tested.
`cycles`	A vector of cycles that the treatment plans to go through. Default is 1:6, where patients will experience up to 6 cycles of the treatment
`tox.target`	The target toxicity of the treatment. Default is 0.28. See details below.
`p_tox1`	The probability cutoff for cycle 1 toxicity. Default is 0.2. See details below.
`p_tox2`	The probability cutoff for later cycles toxicity beyond cycle 1. Default is 0.2. See Details below.
`trialSize`	The maximum sample size for trial simulation. Default is 36. Must be the multiple of cohort size (`chSize`).
`chSize`	The cohort size of patients recruited. Default is 3.
`thrd1`	An upper bound of toxicity for cycle 1 of the treatment. Default is 0.28. See Details below.
`thrd2`	An upper bound of toxicity for late cycles of the treatment, beyond cycle 1. Default is 0.28. See Details below
`proxy.thrd`	A distance parameter to define efficacious doses. Any dose whose predicted efficacy is within proxy.thrd away from the largest one among the safe doses will be declared an efficacious dose.
`param.ctrl`	A list specifying the prior distribution for the parameters. p1_beta_intercept the prior mean of intercept of toxicity model assuming a normal prior p2_beta_intercept the precision (inverse of variance) of intercept of toxicity model assuming a normal prior p1_beta_cycle the prior mean of cycle effect of toxicity model assuming a normal prior p2_beta_cycle the precision (inverse of variance) of cycle effect of toxicity model assuming a normal prior p1_beta_dose the prior minimum of dose effect of toxicity model assuming a uniform prior p2_beta_dose the prior maximum of dose effect of toxicity model assuming a uniform prior p1_alpha the prior mean vector of the parameters from efficacy model assuming a multivariate normal prior p2_alpha the prior precision matrix (inverse of covariance matrix) of the parameters from efficacy model assuming a multivariate normal prior p1_gamma0 the prior mean of association parameter `\gamma` (See Du et al(2017)) of two submodels of the joint model assuming a normal prior p2_gamma0 the prior precision (inverse of variance) of association parameter `\gamma` of two submodels of the joint model assuming a normal prior. Default is non-informative priors.
`n.iters`	Total number of MCMC simulations. Default is 10,000.
`burn.in`	Number of burn=ins in the MCMC simulation. Default is 5,000.
`thin`	Thinning parameter. Default is 2.
`n.chains`	No. of MCMC chains in Bayesian model fitting. Default is 1. Will check the convergence of MCMC chains by the potential scale reduction factor (PSRF) when `n.chains` > 1.
`effcy.flag`	Whether efficacy data is modeled in the model fitting or not. Default is TRUE.
`ICD.flag`	Whether we allow individualized dose modification in stage 1 model or not? Default is TRUE. See details below
`DLT.drop.flag`	Whether the patients should suspend the treatment when observing DLT. Default is TRUE.
`testedD`	Default is TRUE. Whether we only allow ICD or IED to be less than or equal to the maximum dose tested in first cycle.
`IED.flag`	Default is TRUE. Whether we allow dose changing for cycle > 1 in stage 2 model or not?
`ICD_thrd`	The cut-off point of the posterior toxicity probability in defining ICD. Default is 0.3. See details below.

Details

The RunPRMD function implement a Multi-Stage Phase I Dose–Finding Design to recommend dosage selection based on the data collected in the available patient cohorts. The function will automatically identify the model and the stage based on all flags and the records. For the details of argument tox.target, p_tox1, p_tox2, thrd1, thrd2 and ICD_thrd, please check the help document of SimPRMD.

Value

`patlist`	The input data `patlist`
`doseA`	The recommended dose level for cycle 1 for new cohorts
`pat_rec`	The recommended dose for current patients for next cycle
`effcy.flag`	The input argument `effcy.flag`
`doses`	The input argument `doses`
`cycles`	The input argument `cycles`

Examples

data("patlist_sim")
# check the whole dataset by function patlist.display
patlist.display(patlist_sim, n.dose = 6, n.cycle = 6)

# When we pick the records before 6th cohort enrolled in the study
L <- length(patlist_sim$PatID)
patlist <- lapply(patlist_sim, function(a){a <- a[-(44:L)]})
patlist.display(patlist, n.dose = 6, n.cycle = 6)

#The table shows the current patient in the trial. Now record the active
#patient ID and records as follows

patID_act <- c("cohort1subject1", "cohort1subject2", "cohort1subject3",
               "cohort2subject1", "cohort2subject2", "cohort2subject3",
               "cohort3subject2", "cohort3subject3",
               "cohort4subject1", "cohort4subject2", "cohort4subject3",
               "cohort5subject1", "cohort5subject2", "cohort5subject3")
cycle_act <- c(5, 5, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1 ,1)
dose_act <- c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4)
dlt_act <- c(0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0)


test <- RunPRMD(patlist = patlist, patID_act = patID_act,
                cycle_act = cycle_act, dose_act = dose_act,
                dlt_act = dlt_act, trialSize = 36, chSize = 3,
                effcy.flag = TRUE, ICD.flag = TRUE, DLT.drop.flag = TRUE,
                IED.flag = TRUE, ICD_thrd = 0.3)

summary(test)
plot(test)
plot(test, select_cycle = 1:2)

[Package phase1PRMD version 1.0.2 Index]