
Computing the Doses for a given independent variable, Model and Samples
Source:R/Model-methods.R
dose.Rd
A function that computes the dose reaching a specific target value of a given variable that dose depends on. The meaning of this variable depends on the type of the model. For instance, for single agent dose escalation model or pseudo DLE (dose-limiting events)/toxicity model, this variable represents the a probability of the occurrence of a DLE. For efficacy models, it represents expected efficacy. The doses are computed based on the samples of the model parameters (samples).
Usage
dose(x, model, samples, ...)
# S4 method for class 'numeric,LogisticNormal,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticLogNormal,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticLogNormalOrdinal,Samples'
dose(x, model, samples, grade)
# S4 method for class 'numeric,LogisticLogNormalSub,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,ProbitLogNormal,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,ProbitLogNormalRel,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticLogNormalGrouped,Samples'
dose(x, model, samples, group)
# S4 method for class 'numeric,LogisticKadane,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticKadaneBetaGamma,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticNormalMixture,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticNormalFixedMixture,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticLogNormalMixture,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,DualEndpoint,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticIndepBeta,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,LogisticIndepBeta,missing'
dose(x, model)
# S4 method for class 'numeric,Effloglog,missing'
dose(x, model)
# S4 method for class 'numeric,EffFlexi,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,OneParLogNormalPrior,Samples'
dose(x, model, samples)
# S4 method for class 'numeric,OneParExpPrior,Samples'
dose(x, model, samples)
Arguments
- x
(
proportion
ornumeric
)
a value of an independent variable on which dose depends. The following recycling rule applies whensamples
is not missing: vectors of size 1 will be recycled to the size of the sample (i.e.size(samples)
). Otherwise,x
must have the same size as the sample.- model
(
GeneralModel
orModelPseudo
)
the model.- samples
(
Samples
)
the samples of model's parameters that will be used to compute the resulting doses. Can also be missing for some models.- ...
model specific parameters when
samples
are not used.- grade
(
integer
)
The toxicity grade for which probabilities are required- group
(
character
orfactor
)
forLogisticLogNormalGrouped
, indicating whether to calculate the dose for themono
or for thecombo
arm.
Value
A number
or numeric
vector with the doses.
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 prob
.
Details
The dose()
function computes the doses corresponding to a value of
a given independent variable, using samples of the model parameter(s).
If you work with multivariate model parameters, then assume that your model
specific dose()
method receives a samples matrix where the rows
correspond to the sampling index, i.e. the layout is then
nSamples x dimParameter
.
Functions
dose(x = numeric, model = LogisticNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).-
dose(x = numeric, model = LogisticLogNormalOrdinal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).In the case of a
LogisticLogNormalOrdinal
model,dose
returns only the probability of toxicity at the given grade or higher dose(x = numeric, model = LogisticLogNormalSub, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = ProbitLogNormal, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = ProbitLogNormalRel, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormalGrouped, samples = Samples)
: method forLogisticLogNormalGrouped
which needsgroup
argument in addition.dose(x = numeric, model = LogisticKadane, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticKadaneBetaGamma, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticNormalMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticNormalFixedMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticLogNormalMixture, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = DualEndpoint, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticIndepBeta, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
).dose(x = numeric, model = LogisticIndepBeta, samples = missing)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). All model parameters (exceptx
) should be present in themodel
object.dose(x = numeric, model = Effloglog, samples = missing)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). All model parameters (exceptx
) should be present in themodel
object.dose(x = numeric, model = EffFlexi, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLE (x
). For this methodx
must be a scalar.dose(x = numeric, model = OneParLogNormalPrior, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLT (x
).dose(x = numeric, model = OneParExpPrior, samples = Samples)
: compute the dose level reaching a specific target probability of the occurrence of a DLT (x
).
Note
The dose()
and prob()
methods are the inverse of each other, for
all dose()
methods for which its first argument, i.e. a given independent
variable that dose depends on, represents toxicity probability.
Examples
# Create some data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
#> Used default patient IDs!
# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = my_model, samples = my_samples)
#> [1] 34.32397 32.44449 32.44449 32.44449 32.44449 47.58871 47.58871
#> [8] 36.65422 36.65422 36.65422 36.65422 36.65422 228.32111 228.32111
#> [15] 52.78286 52.78286 74.75020 74.75020 74.75020 74.75020
# Create data from the 'Data' (or 'DataDual') class.
dlt_data <- Data(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
doseGrid = seq(from = 25, to = 300, by = 25)
)
#> Used default patient IDs!
#> Used best guess cohort indices!
# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = dlt_data
)
# Get samples from posterior.
dlt_sample <- mcmc(data = dlt_data, model = dlt_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = dlt_model, samples = dlt_sample)
#> [1] 5.394717e+07 2.079593e-131 2.079593e-131 2.079593e-131 2.079593e-131
#> [6] 2.103099e+01 2.103099e+01 1.875484e+02 3.718879e+01 3.718879e+01
#> [11] 3.718879e+01 3.718879e+01 3.718879e+01 3.718879e+01 3.718879e+01
#> [16] 3.718879e+01 3.718879e+01 3.718879e+01 3.718879e+01 6.519215e+01
dose(x = c(0.45, 0.6), model = dlt_model)
#> [1] 144.6624 247.7348
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data_ordinal, model, options)
#> Warning: Unused variable "y" in data
dose(0.25, model, samples, grade = 2L)
#> [1] 6.143309e+01 5.621558e+01 5.919920e+01 9.688731e+01 8.094201e+01
#> [6] 5.912687e+01 5.767324e+01 5.901114e+01 7.979451e+01 7.209331e+01
#> [11] 5.695632e+01 1.530745e+02 5.270801e+01 6.459545e+01 1.047867e+02
#> [16] 7.676280e+01 5.287074e+01 6.860839e+01 1.142494e+02 6.153054e+01
#> [21] 5.912012e+01 1.300338e+02 8.335518e+01 5.694274e+01 1.624171e+02
#> [26] 7.956292e+01 6.651356e+01 1.164615e+02 2.087459e+02 6.159164e+01
#> [31] 7.429127e+01 5.096064e+01 3.601983e+02 5.567597e+01 6.629201e+01
#> [36] 5.922856e+01 6.292168e+01 5.995181e+01 6.725049e+01 8.189472e+01
#> [41] 8.127360e+02 1.266473e+02 8.485608e+01 6.105929e+01 1.057628e+02
#> [46] 7.003773e+01 6.171185e+01 5.692344e+01 6.253722e+01 6.196618e+01
#> [51] 6.796740e+01 6.408205e+01 1.738016e+02 2.445249e+02 1.255957e+02
#> [56] 5.185251e+01 5.548772e+01 5.279641e+01 6.455075e+01 8.748391e+01
#> [61] 1.137566e+02 6.492647e+01 6.040143e+01 6.870302e+01 6.220419e+01
#> [66] 8.719384e+01 8.945984e+01 6.468577e+01 6.386693e+01 1.060209e+02
#> [71] 6.792783e+01 1.663792e+02 5.948240e+01 9.772456e+01 2.212480e+02
#> [76] 6.910329e+01 6.545490e+01 5.938807e+01 1.680617e+02 6.135043e+01
#> [81] 7.169622e+01 6.186813e+01 7.188134e+01 6.318856e+01 5.459078e+01
#> [86] 2.615278e+02 1.613685e+02 6.926496e+01 6.683470e+01 1.324967e+02
#> [91] 9.614562e+01 7.062401e+01 9.661597e+01 6.497108e+01 6.207426e+01
#> [96] 6.046574e+01 5.710497e+01 5.998059e+01 5.694297e+01 5.704682e+01
#> [101] 6.683446e+01 5.758483e+01 6.795229e+01 2.816207e+02 7.546679e+01
#> [106] 9.337239e+01 8.549630e+01 5.794475e+01 8.041519e+01 8.281017e+01
#> [111] 5.363528e+01 7.930436e+01 5.641537e+01 8.654195e+01 6.899151e+01
#> [116] 3.934060e+02 5.466330e+01 8.665859e+01 1.965127e+02 5.787870e+01
#> [121] 5.367958e+01 7.348346e+01 1.181353e+02 6.783968e+01 5.894728e+01
#> [126] 6.209768e+01 5.621979e+01 8.823655e+01 6.588494e+01 5.741273e+01
#> [131] 6.289304e+01 7.395270e+01 8.120308e+01 5.966688e+01 6.074955e+01
#> [136] 6.183457e+01 5.584855e+01 5.989392e+01 6.261407e+01 5.522284e+01
#> [141] 6.311659e+01 7.416020e+01 5.711017e+01 6.421441e+01 5.904214e+01
#> [146] 5.976761e+01 8.199754e+01 1.891733e+03 7.355327e+01 5.903847e+01
#> [151] 6.335306e+01 6.300611e+01 6.105783e+01 5.856372e+01 6.544896e+01
#> [156] 6.015598e+01 6.321851e+01 5.778277e+01 5.944782e+01 8.175275e+01
#> [161] 6.558136e+01 7.213807e+01 6.037840e+01 7.114297e+01 1.059862e+02
#> [166] 6.954356e+01 6.420329e+01 9.147878e+01 6.551175e+01 5.530961e+01
#> [171] 7.723379e+01 8.810676e+01 7.241498e+01 5.744060e+01 7.984898e+01
#> [176] 7.840635e+01 1.376972e+02 5.135312e+01 1.605597e+02 7.265208e+01
#> [181] 9.701081e+01 6.622866e+01 4.771995e+01 7.422205e+01 6.151410e+01
#> [186] 7.035734e+01 6.904846e+01 7.874076e+01 5.953903e+01 5.666141e+01
#> [191] 6.085354e+01 9.220689e+01 5.419134e+01 8.018565e+01 7.068443e+01
#> [196] 6.257913e+01 4.982649e+01 6.761159e+01 6.606346e+01 6.772696e+01
#> [201] 6.453496e+01 6.104016e+01 5.693013e+01 5.709661e+01 7.677891e+01
#> [206] 6.527770e+01 6.868196e+01 2.042203e+02 1.320556e+02 9.774695e+01
#> [211] 7.534633e+01 5.607050e+01 1.092649e+02 7.103824e+01 6.833489e+01
#> [216] 6.135848e+01 6.063580e+01 6.062645e+01 1.089487e+02 6.831772e+01
#> [221] 8.398203e+01 5.088375e+01 7.106577e+01 7.758399e+01 1.122676e+02
#> [226] 7.979163e+01 6.171330e+01 6.992044e+01 8.774749e+01 6.103130e+01
#> [231] 7.228163e+01 5.779101e+01 6.288314e+01 5.794882e+01 9.684187e+01
#> [236] 5.569886e+01 6.567849e+01 6.411405e+01 5.021021e+02 2.950190e+02
#> [241] 3.889601e+03 1.456182e+02 5.892812e+01 8.780746e+01 5.617028e+01
#> [246] 6.730022e+01 5.498517e+01 5.611746e+01 6.377532e+01 1.445556e+02
#> [251] 6.047453e+01 6.657196e+01 5.885844e+01 9.643397e+01 1.948129e+02
#> [256] 5.820147e+01 5.596503e+01 7.699104e+01 9.820225e+01 6.701427e+01
#> [261] 7.330719e+01 6.055689e+01 6.754543e+01 5.768205e+01 8.499778e+01
#> [266] 9.112789e+01 1.485489e+02 7.818161e+01 6.671379e+01 1.156690e+02
#> [271] 6.443738e+01 1.485913e+02 5.932840e+01 8.995831e+01 1.216033e+02
#> [276] 5.978377e+01 6.072458e+01 5.990803e+01 1.069536e+02 5.845682e+01
#> [281] 5.813360e+01 5.940731e+01 7.916207e+01 5.457381e+01 6.116907e+01
#> [286] 5.733789e+01 8.010754e+01 5.709443e+01 6.169197e+01 6.769532e+01
#> [291] 6.527106e+01 1.488939e+02 7.245299e+01 5.491037e+01 6.418574e+01
#> [296] 5.888271e+01 8.587106e+01 6.761772e+01 5.631055e+01 5.785560e+01
#> [301] 7.095438e+01 6.560097e+01 5.708145e+01 8.286082e+01 6.868083e+01
#> [306] 1.650169e+02 1.406217e+02 3.401515e+02 2.007634e+02 6.092818e+01
#> [311] 5.645507e+01 1.693627e+02 2.553516e+02 1.623208e+02 5.499289e+01
#> [316] 5.656192e+01 6.829367e+01 1.302465e+02 6.622792e+01 6.098134e+01
#> [321] 7.392480e+01 7.885353e+01 7.326961e+01 7.861960e+01 6.071314e+01
#> [326] 6.147993e+01 6.307063e+01 6.783321e+01 5.890818e+01 7.203129e+01
#> [331] 5.458472e+01 9.438705e+01 6.495885e+01 8.225696e+01 5.760172e+01
#> [336] 6.607408e+01 7.836149e+01 7.357640e+01 1.856492e+02 4.628423e+01
#> [341] 3.145820e+02 6.795618e+01 1.219852e+02 5.582286e+01 7.167798e+01
#> [346] 6.395704e+01 6.544260e+01 1.037171e+02 6.491211e+01 6.552010e+01
#> [351] 6.966145e+01 6.052666e+01 5.449596e+01 6.092105e+01 1.655003e+02
#> [356] 6.042553e+01 6.115742e+01 5.678060e+01 9.858694e+01 5.777454e+01
#> [361] 6.561652e+01 9.695624e+01 8.727379e+01 6.209937e+01 1.017824e+02
#> [366] 5.956568e+01 5.755198e+01 5.748429e+01 5.424585e+04 3.571983e+03
#> [371] 5.448587e+01 1.542905e+02 7.170077e+01 6.661756e+01 1.199764e+03
#> [376] 5.934105e+01 5.976604e+01 5.490208e+01 6.093195e+01 1.690616e+02
#> [381] 1.201782e+02 1.718065e+02 9.846521e+01 9.350460e+01 6.391032e+01
#> [386] 7.050070e+01 9.163032e+01 2.149354e+02 5.628441e+01 1.802125e+02
#> [391] 1.001878e+03 6.125616e+01 6.570323e+01 9.012679e+01 7.020633e+01
#> [396] 1.087388e+02 9.832515e+01 6.530612e+01 7.019868e+01 1.070100e+02
#> [401] 6.160535e+01 6.123654e+01 6.946275e+01 5.835913e+01 5.794321e+01
#> [406] 6.109971e+01 6.213601e+01 5.665650e+01 5.558633e+01 7.908882e+01
#> [411] 6.348513e+01 1.225439e+02 5.306023e+01 4.050594e+02 7.688307e+01
#> [416] 6.039235e+01 7.542933e+01 5.774235e+01 6.509491e+01 6.588021e+01
#> [421] 5.901056e+01 6.004951e+01 5.689006e+01 8.463488e+01 6.931679e+01
#> [426] 6.764007e+01 6.403672e+01 5.750561e+01 1.381201e+02 1.619899e+02
#> [431] 8.504640e+01 6.257226e+01 3.665718e+02 8.066175e+01 7.813492e+01
#> [436] 8.033393e+01 6.094000e+01 6.499481e+01 7.302318e+01 7.138902e+01
#> [441] 5.823885e+01 1.151201e+02 2.056050e+02 6.479399e+01 2.401583e+02
#> [446] 5.389543e+01 5.978827e+01 2.065568e+02 1.491429e+02 8.741972e+01
#> [451] 1.275840e+02 9.979951e+01 5.746655e+01 6.241006e+01 6.687112e+01
#> [456] 6.405510e+01 1.730462e+02 6.488548e+01 5.834375e+01 6.038896e+01
#> [461] 6.027819e+01 9.879844e+01 6.970165e+01 5.094164e+01 5.657558e+01
#> [466] 8.889499e+01 6.225867e+01 7.169324e+01 5.902575e+01 9.857677e+01
#> [471] 5.581659e+01 6.641922e+01 6.315269e+01 9.392975e+01 2.806591e+02
#> [476] 6.357503e+01 5.741466e+01 8.764243e+01 2.428344e+02 6.395171e+01
#> [481] 6.507620e+01 6.328845e+01 6.111027e+01 5.683020e+01 1.059849e+02
#> [486] 7.778013e+01 8.665805e+01 8.499451e+01 7.555382e+01 6.279321e+01
#> [491] 5.938769e+01 9.543545e+01 6.477655e+01 6.345164e+01 6.231118e+01
#> [496] 7.588919e+01 5.891312e+01 9.316272e+01 7.421706e+01 6.766569e+01
#> [501] 8.873406e+01 6.648786e+01 6.187440e+01 1.003530e+02 6.047540e+01
#> [506] 8.817512e+01 6.091776e+01 7.601802e+01 4.990808e+01 7.331862e+01
#> [511] 6.240353e+01 5.374979e+01 9.998251e+01 7.997897e+01 1.218086e+02
#> [516] 7.470727e+01 6.501572e+01 8.368522e+01 1.174520e+02 7.077379e+01
#> [521] 7.082377e+01 8.509023e+01 5.353805e+01 6.149411e+01 8.238545e+01
#> [526] 8.989982e+01 5.514730e+01 6.761603e+01 6.193617e+01 8.181140e+01
#> [531] 6.774654e+01 8.530817e+01 6.942349e+01 5.560535e+01 8.811386e+01
#> [536] 5.496713e+01 2.294375e+02 3.546907e+02 6.262826e+01 1.500263e+02
#> [541] 1.037372e+02 6.446014e+01 7.662137e+01 5.919194e+01 5.768472e+01
#> [546] 7.634305e+01 6.228389e+01 9.427555e+01 3.398332e+02 1.282377e+02
#> [551] 6.468170e+01 6.119968e+01 1.082640e+02 6.370941e+01 9.562448e+01
#> [556] 9.561148e+01 1.134357e+02 9.146350e+01 6.195166e+01 6.050521e+01
#> [561] 6.417554e+01 5.802383e+01 6.090847e+01 9.449470e+01 9.392480e+01
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#> [951] 5.473559e+01 6.909302e+01 6.092668e+01 5.240085e+01 3.083624e+02
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#> [991] 8.534167e+01 9.950927e+02 1.056958e+02 1.220820e+02 7.424642e+01
#> [996] 6.694089e+01 6.987872e+01 6.255202e+01 5.925544e+01 8.302260e+01