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Computing the Doses for a given independent variable, Model and Samples

Source: R/Model-methods.R
dose.Rd

[Stable]

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 or numeric)
a value of an independent variable on which dose depends. 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, x must have the same size as the sample.

model

(GeneralModel or ModelPseudo)
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 or factor)
for LogisticLogNormalGrouped, indicating whether to calculate the dose for the mono or for the combo 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 for LogisticLogNormalGrouped which needs group 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 (except x) should be present in the model 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 (except x) should be present in the model 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 method x 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.

See also

doseFunction(), prob(), efficacy().

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]  36.91561  36.91561  36.91561  83.06868  81.74463  81.74463  81.74463
#>  [8] 292.77530 292.77530  29.95102  13.52425  13.52425  13.52425  19.34449
#> [15]  12.66570  12.66570  12.66570  12.66570  27.51774  21.48722

# 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)

dose(0.25, model, samples, grade = 2L)
#>    [1]    72.47746    65.17403    54.27231    77.42856    63.76975   142.67795
#>    [7]    71.14320    69.89031    61.92357    57.49384    96.13066    58.57596
#>   [13]    60.69700    61.37585  1041.90485    54.94550    78.76719    47.76051
#>   [19]    56.01666    90.90026    61.23543    52.90334   160.54427    95.94700
#>   [25]    58.50428    65.08488    58.86201   557.92565    64.21315    81.76832
#>   [31]    69.56500    50.26312    79.78290    61.84144    60.79889    56.95084
#>   [37]    80.87188    63.18636    64.56878    61.63364    92.64552    57.40045
#>   [43]    55.81177    68.86447    56.74283    61.34920    58.85388    58.54395
#>   [49]    65.42377   303.58155    80.40427    64.75968    56.13431    97.88851
#>   [55]    68.77163    63.94454    72.08930    65.27471    74.15749    59.55670
#>   [61]    64.12533    56.45803   179.01489    90.91265    61.38797    73.35031
#>   [67]   310.79118    69.05570    78.58979    98.59081    62.66914    91.48976
#>   [73]   197.41627    70.82797    68.49423    66.98428   131.22610    73.57773
#>   [79]    60.02673    66.80932    59.16430    55.18270    62.12290    72.92188
#>   [85]    68.74191    72.84113    66.29995    63.56323    72.69410    77.41839
#>   [91]    56.88294    60.41878    70.85992   111.88877    80.73845    59.78275
#>   [97]    79.30307    61.21656    56.44386    82.03401    59.89526    64.07053
#>  [103]    50.30517    70.52626    82.70590    64.82193    58.19662    60.83389
#>  [109]    58.38708    70.58685   133.84493    57.28425    69.15694   128.50454
#>  [115]    69.24796    60.24991    60.55437    55.32461    62.68127    74.25014
#>  [121]    68.27326    66.76783    79.05004   133.81587    75.87381    75.27274
#>  [127]    76.97916   270.99449    52.98816    58.05892    63.10647    80.04102
#>  [133]    57.99900    60.74214    60.77519    61.37916    66.40360    66.22140
#>  [139]    64.28327    55.87684    57.22461    61.08190    54.63922  2562.15257
#>  [145]    57.85656    65.30345    53.42308    67.36040    70.19767    55.40576
#>  [151]    59.52350    70.00156    68.57674   103.25521    59.65985   150.58431
#>  [157]    85.78704  3037.05804   152.88741    72.91020    54.63452   101.29887
#>  [163]    76.84381    89.11886    54.71055    77.43162    69.78703    64.25686
#>  [169]    64.36876    82.72691    81.11847    55.64070    61.40204    87.24369
#>  [175]   219.49182    25.66019    59.89425    52.81841    63.58887    67.62620
#>  [181]   154.00414    68.76974    65.99804    69.62877    63.70402    96.68744
#>  [187]    56.64088    66.65735   319.57608    55.01036    69.67437    77.23182
#>  [193]    77.25169    55.65600    67.18370    69.24390    54.30804    73.17705
#>  [199]   120.07341    63.46445    87.96693    84.97247    73.02449    92.97745
#>  [205]    58.96867    53.11622    52.75754    63.83534    67.75007   432.29692
#>  [211]    63.28913    58.69849    63.80015   107.97874    75.54880   189.28702
#>  [217]   294.03936    62.45236    58.46256   334.13273    65.96781    63.53641
#>  [223]    57.26073    90.51519    62.60834   157.22269    76.07241   136.16364
#>  [229]   102.11310    86.84516    51.84056   116.33875    73.23845    56.36604
#>  [235]    82.26947    58.45390    96.42409    65.56695    56.89048    64.69570
#>  [241]    77.70183    60.00241    89.97844    59.69616    71.09190    53.85908
#>  [247]    66.92672    72.14202   127.85264   173.21065    63.76974   132.24510
#>  [253]    52.41502    55.50147    55.41775    84.59835    63.10140    60.47545
#>  [259]    59.07970    59.82287    64.30599    68.37885    58.55752    78.90631
#>  [265]    69.66001    62.17231    73.21222    60.86761    89.51922    65.09227
#>  [271]   148.35757    52.56185    63.16260    69.80006    62.56231    61.36129
#>  [277]    82.48648    61.87929    84.78903    57.66517    61.81411    56.45673
#>  [283]    57.51447    64.09783    66.08321    55.35148    61.30998    79.37164
#>  [289]    65.31163   101.48257    50.41208    98.71869    61.86429    63.27163
#>  [295]    61.87066    83.08845    61.36678    62.12486   105.25191    59.30089
#>  [301]   315.96941   317.14954    64.05701    66.24221    60.65661    61.83946
#>  [307]    54.80195    66.21158    62.93323    67.54353    68.00466    68.08516
#>  [313]    94.05830    57.32752   168.61515    59.90303   149.45102    98.29392
#>  [319]    65.55362    61.04322    82.23338    65.18793    55.88713    55.97975
#>  [325]    58.90256    61.45418    66.93188    72.30769    68.50040    55.90294
#>  [331]    71.84110    86.09133   298.77599    75.76745    55.25970    63.13197
#>  [337]    76.02615    61.03122    57.29976    74.69561    64.08120  1783.89820
#>  [343]    73.15027    64.62861    53.52700    55.72504    63.69880    57.52579
#>  [349]    70.87719    60.03783    57.83862    65.29078    56.27762    60.22328
#>  [355]    61.24531    66.72724    72.06635    60.90200    63.97798    62.08609
#>  [361]    59.13002    83.87802    76.19450   104.23076    84.07776    56.91650
#>  [367]    57.17495    53.47664    47.16755    79.68183   210.19951    82.28137
#>  [373]    68.89088    76.84827    63.48353    57.28319    58.86062    52.28731
#>  [379]    52.53996    77.53562    80.95418    84.26145    60.33078    75.22153
#>  [385]    91.22479    59.91351   101.68007   112.66846    62.48794    60.81564
#>  [391]    57.16150    71.11305    64.44640    58.14866    87.49670    57.35677
#>  [397]    76.39962    63.90447    83.19386    59.44430    57.93208    57.68506
#>  [403]    64.74626    57.40666    59.56894   214.04268    66.59969    58.02624
#>  [409]    63.01951    64.13942    58.17284    56.82393    74.80843    67.30135
#>  [415]    61.39526    90.84092    57.85172   127.84880    54.07995    62.78352
#>  [421]    70.20323    76.45416    64.33751    83.22221   114.82964    55.17234
#>  [427]    60.12557    60.92367    56.91524    60.78539    89.64844   489.02375
#>  [433]   778.95646   480.22481    61.60709    63.50879    97.10167    89.06848
#>  [439]    60.10830    55.53835    57.99579    55.09034    61.84919    55.24523
#>  [445]    54.58668    57.99840    79.04813    64.37349    71.87755    66.02513
#>  [451]    62.39320    73.30701    87.38129   132.36239    63.50459    61.02606
#>  [457]   158.20569    56.69928    92.30656   210.38325    55.19411    56.91705
#>  [463]    51.69089    59.63136    61.14405   116.04583    63.70227    61.52539
#>  [469]    68.49983    69.41883    62.10026    72.02868    74.90263    60.00982
#>  [475]    80.04744    67.91641    55.81460    53.27009    60.26851    54.36453
#>  [481]    69.96666    94.01740    66.98748    64.61913   152.26079    72.93100
#>  [487]   131.86553   288.87783   105.74974    86.97719    77.86408    59.45620
#>  [493]    55.67929    64.07600   126.91942    62.45026    63.69523    58.98870
#>  [499]    66.35133    62.65966   195.89056    56.98399    63.36591    57.14892
#>  [505]    74.37743    62.31551    55.79124    55.28378    74.04527   109.43586
#>  [511]   121.34508    76.42839    96.34600    55.00079    53.99352    55.05392
#>  [517]    63.19541    55.84946    59.81126    60.97699   135.18515    82.58759
#>  [523]    64.32438    64.39135    97.04390   149.14249    65.84545    59.91750
#>  [529]    62.13170    57.85187    65.40683    76.68599    60.78817    63.88713
#>  [535]    63.56030    79.60889   104.62378    53.18109    60.16862    58.86502
#>  [541]    58.46220    59.82993    69.41451   100.69088    54.52510    63.05584
#>  [547]    89.21555    62.01153   230.87508    46.56338    89.44366    61.84937
#>  [553]    92.15142    58.70062   109.26189    70.99542    66.63975   110.79010
#>  [559]    56.71468    57.64338    66.01795   124.72958    79.77632    63.15269
#>  [565]    61.16777    57.19446    56.79694    58.44610    67.34329    69.54786
#>  [571] 52364.83794    82.25752    61.17826    75.69031    75.26617    58.51771
#>  [577]    58.15236    66.74044    76.62120    85.41755    53.43237    55.74542
#>  [583]    66.30000    56.46177    76.28796    69.78887    59.84070    70.49540
#>  [589]    64.59286   204.70172    60.94164    61.47299    59.81856    74.72879
#>  [595]    68.12934    60.44035    58.90909    74.59055   188.51998    55.53343
#>  [601]    89.19669    85.27778    84.46176    87.63530    65.22773    62.70180
#>  [607]    80.08091   109.98870   399.94354   435.39350    60.96300    58.77998
#>  [613]    60.24314    63.04105    62.65398    62.78057    77.64279    56.49718
#>  [619]    56.85881    62.14617    56.56812    60.46962    61.07966    63.04813
#>  [625]    72.10005    64.14363    64.38936    68.82844   249.21312  7416.58151
#>  [631]    58.36427    61.06209   102.27198    63.93846   112.43365    57.80001
#>  [637]    63.18814    58.82842    59.59133   136.41878    58.16017    78.44026
#>  [643]    57.28774    62.76170   101.25537    73.63165    82.63500   298.09888
#>  [649]   288.76201    59.41153    65.04712   152.21904    91.46410    61.59577
#>  [655]    95.03222    58.34084   213.02168    73.47733    65.36127    73.51421
#>  [661]    62.85026    60.45691    68.38619    70.93936    59.21126    70.56260
#>  [667]    92.94995    53.25452    85.25268    43.25278    75.71672   304.07294
#>  [673]    53.64884    55.54770    73.70919    79.65202    61.83957    62.17561
#>  [679]    65.79119    63.51405    59.98343    72.71919    52.64327    68.34919
#>  [685]    57.21087    66.11008    65.07074    70.13095    61.70998    74.41656
#>  [691]    71.15305    65.08786    87.55508    61.57940    57.47126    59.21748
#>  [697]    62.93341    65.18160    70.62096    88.34287    65.36152    77.06478
#>  [703]    60.46092    64.38563    64.08733   208.54655    57.87223    62.52863
#>  [709]   275.01814    35.40349    43.61151    73.31663    71.90424   133.60490
#>  [715]    53.33076    59.32313   105.26899    57.56666    54.91297    60.49107
#>  [721]    67.36168    62.60703    87.09867    57.67583    70.80833    66.20506
#>  [727]    46.69329    60.20980    64.70313    54.17607    85.48409    61.34168
#>  [733]    59.19077    56.03376    63.28361    59.68801    81.27601    54.84665
#>  [739]    63.92480   267.89164    58.98336    57.94133    67.38500    60.15652
#>  [745]    89.91295    57.37143    63.94399   115.17497    57.32844    79.71163
#>  [751]    72.65442    58.85730    59.53654    63.71520    60.57425    79.82173
#>  [757]    60.34567    58.40566   106.75403    57.34852    61.13865    61.00939
#>  [763]   233.94245   168.56804   211.34195    58.22756    54.35426    62.86928
#>  [769]    61.03493    59.02373    71.72041    62.26296   344.80530   160.15391
#>  [775]    58.40619    67.19291    63.98154    70.73797    61.56614    59.02322
#>  [781]    61.49345    61.45264    55.26963    74.82794    97.60053    63.92591
#>  [787]    57.70032    62.39988    58.74591    68.54569    89.67726    67.95100
#>  [793]    60.97306    66.95835    65.60247    73.38682   101.77342    56.18120
#>  [799]    71.87958    81.48767    58.09980    66.39223    67.94919   177.20573
#>  [805]    64.22334    58.28844    64.05211    57.49498    81.13313   116.54610
#>  [811]    56.83220    59.28011    58.30953    56.32513    58.90756   591.82081
#>  [817]    56.16306    59.83971    64.55094    66.40700    54.89392   146.24803
#>  [823]    59.09332    70.27256    63.41424    58.19752    58.17467   117.89871
#>  [829]    77.90299   148.26677   157.41109    61.24350   126.87431    75.15153
#>  [835]    56.72160    67.92497    80.48197    71.89652    71.59985    60.63827
#>  [841]    68.40715    55.92620    69.50500    56.88471   102.84712    60.33992
#>  [847]    92.00918    70.30740    67.08452   425.67152   158.32007    55.21472
#>  [853]   110.42737    62.17097    79.05518    81.60784   202.04721    67.17935
#>  [859]   109.95577    55.58085    57.07482    62.39225    59.00816    57.58876
#>  [865]    63.06522    76.22922    67.91011    85.23868    59.51374    70.97540
#>  [871]    60.16346    56.54539    55.16518    57.27252    66.43744    67.26465
#>  [877]    79.41475    53.91513    77.71533    73.85845    73.47545    66.32753
#>  [883]    77.16034    70.96472   114.18587    70.60801    64.74184    76.07700
#>  [889]    63.78135    69.37633    75.83579   332.61987    59.45247    64.86964
#>  [895]    58.88873    71.63832    67.06841    54.70824    59.24875    61.32379
#>  [901]    66.47885    64.29638    56.12279    63.95390    58.23461    58.72955
#>  [907]    65.96896    76.59404    91.17241    70.46634    70.58724    69.80445
#>  [913]    67.59147    59.65607    66.82101    63.09175    60.52452    58.57492
#>  [919]    60.24913    81.68737    57.53502    54.46614    72.87159   101.88432
#>  [925]    73.33981    60.91934    57.44801    61.89693    92.62149    70.86598
#>  [931]    58.22340    59.08515    61.91151    65.30457    72.41474    79.34893
#>  [937]    80.41686    61.59357    59.36574   150.18328    59.42585    64.78514
#>  [943]    59.32869    84.31632    61.85145   197.11635    52.37588   105.79926
#>  [949]    65.37708    66.81400    69.58975    68.55866    76.97007    78.92570
#>  [955]    63.47514    58.50557   128.18260    86.02547    89.75399    61.54890
#>  [961]    64.31046    54.21539    58.79107    73.65771    59.56362    61.49225
#>  [967]    57.19386    91.04455    61.43665    56.42349    58.66009   452.27072
#>  [973]    65.25078    69.14946    63.16558   310.69259   111.71563    71.02682
#>  [979]    73.05996    57.78368    93.07819    63.19112    67.65063   289.14706
#>  [985]    62.84201    63.15687    67.80693    58.71267    51.21988    77.82833
#>  [991]    59.72167    73.71505    60.11947    59.55909    62.56227   113.87678
#>  [997]    57.70198   103.90671    62.22289    67.75824