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NextBestMaxGainSamples

Source: R/Rules-class.R
NextBestMaxGainSamples-class.Rd

[Stable]

NextBestMaxGainSamples is the class to find a next best dose with maximum gain value based on a pseudo DLT and efficacy models and DLT and efficacy samples. There are two target probabilities of the occurrence of a DLT that must be specified: target probability to be used during the trial and target probability to be used at the end of the trial. It is suitable to use it only with the ModelTox model and ModelEff classes.

Usage

NextBestMaxGainSamples(prob_target_drt, prob_target_eot, derive, mg_derive)

.DefaultNextBestMaxGainSamples()

Arguments

prob_target_drt

(proportion)
see slot definition in NextBestMaxGain.

prob_target_eot

(proportion)
see slot definition in NextBestMaxGain.

derive

(function)
see slot definition.

mg_derive

(function)
see slot definition.

Slots

derive

(function)
derives, based on a vector of posterior dose samples, the target dose that has the probability of the occurrence of DLT equals to either the prob_target_drt or prob_target_eot. It must therefore accept one and only one argument, which is a numeric vector, and return a number.

mg_derive

(function)
derives, based on a vector of posterior dose samples that give the maximum gain value, the final next best estimate of the dose that gives the maximum gain value. It must therefore accept one and only one argument, which is a numeric vector, and return a number.

Note

Typically, end users will not use the .DefaultNextBestMaxGainSamples() function.

Examples

# Target probability of the occurrence of a DLT during the trial is set to 0.35.
# Target probability of the occurrence of a DLT at the end of the trial is set to 0.3.
# We want the use the 30% posterior quantile (the 30th percentile) of the TD35
# (the dose level with probability of the DLT equals 0.35) and TD30 samples.
# For `mg_derive` function (which takes the sample of doses which give the maximum
# gain), we will use the 50% posterior quantile (the median or th 50th percentile)
# of the sample.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)