Computing Expected Efficacy for a Given Dose, Model and Samples

A function that computes the value of expected efficacy at a specified dose level, based on the model specific parameters. The model parameters (samples) are obtained based on prior specified in form of pseudo data combined with observed responses (if any).

Usage

efficacy(dose, model, samples, ...)

# S4 method for class 'numeric,Effloglog,Samples'
efficacy(dose, model, samples)

# S4 method for class 'numeric,Effloglog,missing'
efficacy(dose, model)

# S4 method for class 'numeric,EffFlexi,Samples'
efficacy(dose, model, samples)

Arguments

dose: (numeric)
the dose which is targeted. The following recycling rule applies when samples is not missing: vectors of size 1 will be recycled to the size of the sample (i.e. size(samples)). Otherwise, dose must have the same size as the sample.
model: (ModelEff)
the efficacy model with pseudo data prior.
samples: (Samples)
samples of model's parameters that will be used to compute expected efficacy values. Can also be missing for some models.
...: model specific parameters when samples are not used.

Value

A numeric vector with the values of expected efficacy. If non-scalar samples were used, then every element in the returned vector corresponds to one element of a sample. Hence, in this case, the output vector is of the same length as the sample vector. If scalar samples were used or no samples were used, e.g. for pseudo DLE/toxicity model, then the output is of the same length as the length of the dose.

Details

The efficacy() function computes the expected efficacy for given doses, using samples of the model parameter(s). If you work with multivariate model parameters, then assume that your model specific efficacy() method receives a samples matrix where the rows correspond to the sampling index, i.e. the layout is then nSamples x dimParameter.

Functions

efficacy(dose = numeric, model = Effloglog, samples = Samples): compute the expected efficacy at a specified dose level, based on the samples of Effloglog model parameters.
efficacy(dose = numeric, model = Effloglog, samples = missing): compute the expected efficacy at a specified dose level, based on the Effloglog model parameters. All model parameters (except dose) should be present in the model object.
efficacy(dose = numeric, model = EffFlexi, samples = Samples): compute the expected efficacy at a specified dose level, based on the samples of EffFlexi model parameters. If a given dose in the dose vector is from outside of the dose grid range, the NA_real is returned for this dose and the warning is thrown.

Examples

# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (in flexible form for prior) and efficacy samples.

# Empty data (i.e. no observed data), dose grid only.
my_data <- DataDual(doseGrid = seq(25, 300, 25))

my_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = my_data
)

my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 200,
  rng_kind = "Mersenne-Twister",
  rng_seed = 94
)

my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Efficacy for dose 75.
efficacy(dose = 75, model = my_model, samples = my_samples)
#>   [1]   1.53309381  -0.36750631   7.55527614   5.25469404   3.66693278
#>   [6]   8.70838981   4.90252140  -0.69061135   1.34203932  -7.43545911
#>  [11]  -2.94369030  -0.31605961   0.50693514  -9.26862484   1.46306208
#>  [16]  -1.14134478   7.32450512  -1.62809243  -2.49305731  -1.66356028
#>  [21]   0.14878359  -0.49947849   4.42304913   4.56649426  -3.57361192
#>  [26]  -5.63080113  -1.94459230  14.44628993   7.35954897   3.02553881
#>  [31]  11.10188815  11.22388514   6.80186903   0.36884435  -2.82813022
#>  [36]  -4.73915711   9.09265745   0.45171075   7.47281283  -3.04597000
#>  [41]  12.38787082   2.42193236   0.61608253  -0.13622731  -6.08412025
#>  [46]  -1.86297634   3.28013838  -2.48647780   1.42981578  -6.81130220
#>  [51]  -5.26399353   2.63505491  -5.57528885  -1.18080239   0.68363630
#>  [56]   0.80957042   1.66747143   1.90085264   0.31491916  -2.97512796
#>  [61]   6.73581440   0.80529855  10.99746433  -4.50426438   2.35555834
#>  [66]  -2.03180783   3.97573796   4.27632858  -9.42646554  12.55749274
#>  [71]  -0.80789290  -1.90257411  -2.49043558   5.63952769   3.45659353
#>  [76]   0.47354025   2.52357271  -1.38263265  -7.15088137  -1.94147061
#>  [81]  -1.49545417  -4.77468458  -2.72741400   9.03347724   2.29069087
#>  [86]   3.92829723   2.63532137   1.82836285   0.58927326   4.21572326
#>  [91]  -2.45214763   7.88420978   0.19709801   0.89906020   4.88309101
#>  [96]   3.04825966  -1.81374639   1.45541231  14.61364519  10.31328290
#> [101]  -7.21042554   2.98659493   8.94809729  -0.36850893   0.77558282
#> [106]   3.24200951  -9.74784292  12.41222304   1.69010933  -1.74952128
#> [111]   5.69098809   2.62087863  -4.88471879  -1.90574868  -0.46153796
#> [116]   2.26830157  -1.42972655  10.97802797  -4.39581100   0.51699864
#> [121]   3.48569230   0.79299301  -6.87113975   3.24208183   6.76958848
#> [126]  -1.49208773   3.78647974   6.56550883   4.58980593  -0.41956371
#> [131]   3.92797953  -0.25794403   3.79244917  -3.41126635  -2.31361622
#> [136]   0.68751868   1.86167000   3.36000659   0.42099810   4.07698962
#> [141]   4.50480555   6.29500656  -4.07015627   6.86309949   4.04608404
#> [146]   1.83641311  -5.48623907  -4.74101984  -5.79376521   1.15017134
#> [151]  -2.50564693  -3.48767730   3.83157946   2.09528864  -1.72330218
#> [156]   8.21151143   6.95861675   5.57982266   8.51214305  -2.05622819
#> [161]   9.80142831  -5.12209892  -6.73822609   2.30227490   1.75097164
#> [166]   1.44273838   2.96978667  10.76845222   4.07874269   7.10792088
#> [171]  -2.05910186   7.30915648   2.61942655  -3.28100602  -0.59016549
#> [176]  -0.08740698   9.11846595  -2.74656639   2.79106308 -10.22195117
#> [181]  -1.57917435  -0.75159739  -0.42006406   5.53433055  -0.76741452
#> [186]  -4.14286245   1.27944063   3.12421033  -3.08574641  10.18627697
#> [191]   9.74964035  -1.02936258   4.68939972   1.95388350   0.99957216
#> [196]  -0.74528033  -0.29508297   2.96868789  14.94223266   4.32409978

# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (linear log-log efficacy) and no samples.
my_model_ll <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data,
  const = 0
)

efficacy(dose = 75, model = my_model_ll)
#> [1] 1.885121