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

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 numeric,LogisticNormal,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticLogNormal,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticLogNormalOrdinal,Samples
dose(x, model, samples, grade)

# S4 method for numeric,LogisticLogNormalSub,Samples
dose(x, model, samples)

# S4 method for numeric,ProbitLogNormal,Samples
dose(x, model, samples)

# S4 method for numeric,ProbitLogNormalRel,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticLogNormalGrouped,Samples
dose(x, model, samples, group)

# S4 method for numeric,LogisticKadane,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticKadaneBetaGamma,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticNormalMixture,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticNormalFixedMixture,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticLogNormalMixture,Samples
dose(x, model, samples)

# S4 method for numeric,DualEndpoint,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticIndepBeta,Samples
dose(x, model, samples)

# S4 method for numeric,LogisticIndepBeta,missing
dose(x, model)

# S4 method for numeric,Effloglog,missing
dose(x, model)

# S4 method for numeric,EffFlexi,Samples
dose(x, model, samples)

# S4 method for numeric,OneParLogNormalPrior,Samples
dose(x, model, samples)

# S4 method for 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.

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]  41.56066  41.56066  41.56066  71.99341 155.02621  92.08929  60.25945
#>  [8]  60.25945  60.25945  37.50733  19.97471  19.97471  20.03481  39.93760
#> [15]  39.93760  39.93760  46.17990  51.42044  51.42044  51.42044

# 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]     10.07613     10.07613     22.15191 850756.88704     48.50700
#>  [6]     48.50700     48.50700     72.06078     72.06078     72.06078
#> [11]   2590.71936    155.17055    155.17055    155.17055    212.47066
#> [16]    156.91934    234.83714    234.83714    196.82793     90.86247
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]    66.07520    61.08300    76.15262    83.47429    60.17843    64.77986
#>    [7]    72.99826    57.73981    58.41763    56.59639    92.75554   253.80308
#>   [13]   240.58223   349.67579    65.83493   144.92998   266.53447    60.37094
#>   [19]    62.16265    58.82040    61.01682    96.77802   114.72857    57.33542
#>   [25]    73.40131    67.21688    71.18746    59.49147    56.85420    90.90860
#>   [31]   163.07651    61.27820    63.83218   109.54538    59.21033    56.05853
#>   [37]    69.25195    59.77095   122.31732    53.46954    68.72654    66.06694
#>   [43]    57.92578    63.07911    79.44305    57.73879    73.92113    66.09630
#>   [49]    68.27778   107.25606    57.20848    70.14217    86.56300    64.42141
#>   [55]    66.47697    60.30376    63.96431    75.65355    54.16039    62.19713
#>   [61]    77.26086    57.18139    70.80352    59.78917    69.80448    69.93424
#>   [67]    61.91771    59.90926    69.93061    65.38790    60.61468    57.02712
#>   [73]   149.01886   169.72957    82.15397    89.33145    80.30771    54.15646
#>   [79]    75.42805    90.36483   267.91144    66.47167    57.04119    68.79336
#>   [85]    66.09059    73.89822   162.35059   109.80887    54.35504    98.42235
#>   [91]   115.26078   149.21183    81.06245    71.25048    60.19240    59.80886
#>   [97]    67.93010    52.12529    63.35995    61.39031    59.86440    68.32096
#>  [103]    90.31110    54.10704    52.66232    58.51778    63.78642    71.52867
#>  [109]   114.84779    91.44784    59.33136    63.15201    60.51489    63.66285
#>  [115]    63.65816    83.22411    56.58357    66.11112    55.79289    53.49290
#>  [121]    64.83649    59.59579    73.72573    54.04188    56.07011    82.81573
#>  [127]    67.37660   106.61631   126.13670    61.64790    58.40008    63.79393
#>  [133]    79.57368   204.90757   501.76857    80.88440    77.91787   293.30471
#>  [139]    79.50777    61.71546    55.98197    63.41835   142.19239    60.84721
#>  [145]    68.84292    91.50405    74.18690    57.51296    69.75180   125.83319
#>  [151]    79.85351    60.26979    58.59972    62.16230    60.48844    59.71869
#>  [157]    63.58365    72.94266    57.74211   166.43858   205.93440  1336.62160
#>  [163]    55.55510   137.88810    93.53126    59.75831    66.92376    64.92147
#>  [169]    58.77459    70.74464    77.80667    60.01265    52.95637    74.47933
#>  [175]    87.73729    57.68402    65.70193    59.82847    61.67907    76.14954
#>  [181]    59.78918    80.05720    61.01249    65.03557    99.01964    58.51246
#>  [187]    78.45733    64.49683    63.71557    67.24439    57.82793    69.81081
#>  [193]    73.99580    65.90770    57.73934    58.99676    83.03459    72.36505
#>  [199]    95.21867    74.62707    56.25393    70.93395    64.09806    71.04354
#>  [205]    79.11518    58.30306    72.69577    62.61934    57.99542    73.84334
#>  [211]    70.02940    62.24713    56.15191    78.43231    57.40697    73.69682
#>  [217]   100.50169    63.25973    68.78595   119.15407    81.37355    81.17526
#>  [223]    67.96611  1084.47837   190.92442    64.28692    67.70391    87.80182
#>  [229]    63.37990    62.93045    74.63468    93.61267    58.86755    63.83991
#>  [235]    60.23793    68.14608    65.79669    55.28118    49.95446    96.56575
#>  [241]    58.87900    62.50019    51.44152    74.30917    58.80982   555.33963
#>  [247]    54.56853   120.14220   131.71704    61.16658    59.89960   119.62210
#>  [253]    88.14017    59.36379    68.99625    63.54986    59.28268    66.30933
#>  [259]   103.72353    62.17441    58.24677    57.89241    74.30192    58.26551
#>  [265]    99.14392    61.25630    64.29739    80.03213    49.95629    56.03161
#>  [271]    88.37938   102.28455    59.90088    57.26105    62.53975    70.71031
#>  [277]    49.39472    69.92938   241.88754   650.30644   150.38083    65.57173
#>  [283]    65.29885    60.60060    59.23170    66.41232  1019.20510    51.14487
#>  [289]    74.88636    61.98775    72.33925    61.98195    66.79417    58.72124
#>  [295]    66.49096    59.53943    64.82151    60.02819   123.65134   105.56820
#>  [301]    89.91657    58.70548   144.81957    63.83949    71.28684    65.15442
#>  [307]    59.79999    61.35026   106.87089    62.89676    61.94574    52.32669
#>  [313]    56.90432    64.34953    59.28864    62.22842   128.16842    88.55196
#>  [319]    59.09323    57.61137    63.70021    55.75829    68.26754    48.79942
#>  [325]    55.51148    66.22252    97.51135    53.37004   146.09994    58.55488
#>  [331]   113.05343    59.71473    61.84893    56.47677    63.31413   106.05441
#>  [337]    62.09883    60.45675    89.84748    64.52817    84.00154    54.62900
#>  [343]    55.92233    77.13786    70.01635    59.50358    55.39838   199.50636
#>  [349]   214.49093    69.51872   107.42400    54.89217    77.69169    73.18161
#>  [355]    59.41595   108.23046    58.09553    57.56903    58.13688    55.87974
#>  [361]    61.12245    57.85546    89.87643    70.67806    80.31927    73.95165
#>  [367]   145.04205   178.94633    56.57177    85.60602    61.41169    70.96007
#>  [373]    74.16591    57.69662    62.70932   119.09367    77.18156    60.68360
#>  [379]    56.97567    56.81385    70.32677   338.53207    67.70863    59.84382
#>  [385]    70.34075    68.71287   183.61815   494.59252   131.54095    64.69081
#>  [391]    55.72149    74.58706    82.86996    67.76467    55.30954    65.55180
#>  [397]    64.34361    62.94428    61.40047    70.49512    57.22741    69.77043
#>  [403]    59.64648    71.61247    85.80182    55.32754    65.19200    59.99446
#>  [409]    59.77707    77.23507    76.77995   121.00048   202.96755    63.21450
#>  [415]    63.83243    70.25740    59.37251   112.87709    60.02814    76.99862
#>  [421]    93.53421    66.47291    88.81575    70.78310    84.50707  2391.49134
#>  [427]   102.92868    64.85589    76.71534    84.12901    59.93401    67.17862
#>  [433]    61.86026    57.76407    60.08138    64.71092    92.54036    55.74813
#>  [439]    72.89777    67.86105    59.49417    72.00953   508.61058    64.61116
#>  [445]    65.31073   177.02469    63.07156    57.05333    64.07833    89.93729
#>  [451]    97.54324    59.00083    60.23209   110.70463    97.36275    65.30837
#>  [457]    75.33278    70.88995    61.18568    62.63206    65.80562    73.25288
#>  [463]    57.29102   116.49838    55.74859   159.38714    57.84735    62.88790
#>  [469]    67.00030    75.89714    66.23468    64.88535   598.17662    68.02526
#>  [475]    57.24255    61.56668    68.29990    67.13373    59.88764    87.94152
#>  [481]    54.69080   101.28851    69.88483    65.53649   146.44207    75.16010
#>  [487]    58.85355    58.06277    55.51338    72.64313    58.09768    56.39200
#>  [493]   158.99461    89.44031    88.58510    66.61334    54.54921   108.82718
#>  [499]    64.49112    74.35716  3501.67158   110.89290   181.86623    56.98284
#>  [505]    63.93664    60.41677    54.08075    87.15187  5393.28675   170.30287
#>  [511] 31901.39568   682.49118    79.28083    85.98816    53.63070    64.51774
#>  [517]   117.44831   127.12609    73.96513   241.12217    81.34348    67.04785
#>  [523]    73.76605    78.39764    83.66275    55.28614    59.93011    61.03606
#>  [529]    84.20291   152.99654    72.53878    41.94963    61.68296    68.13065
#>  [535]    69.43050    72.92778    58.04889    62.49362    79.23209    57.55147
#>  [541]    56.17844    52.79249    62.18176    91.31923    58.51250    62.45930
#>  [547]    84.90566    60.23316    68.40809  9469.34227    68.29812    92.22432
#>  [553]    59.88389    87.78886    84.52926    60.79037    61.25118    64.63687
#>  [559]    82.16562   100.44388    56.95006    65.04107   125.41708   122.12359
#>  [565]    80.53909    87.26375    70.18771    66.08485    55.59482    79.49064
#>  [571]   198.94407    77.50294   152.49729   121.23691   251.78523   118.47988
#>  [577]    54.06208    63.35871    61.11754    88.68571   105.57909    95.23491
#>  [583]    61.73542    63.27881    95.52518   113.09539   124.13287    97.40608
#>  [589]    55.60131    75.29585    63.26746    57.32862    70.22594    73.49275
#>  [595]   177.16466   139.83845   921.56779  1442.68636    58.19714    60.79425
#>  [601]    56.92430    57.79703    84.16198   263.63408   202.26722    78.62526
#>  [607]    57.69704    65.95459    86.37350    58.59737    60.62321    59.82175
#>  [613]    71.40231   210.55298    97.52138    67.28172    62.08389    59.17367
#>  [619]    66.58042    99.48285    81.88583    54.04377    77.80113    70.83389
#>  [625]    94.50517   202.31324  2141.09766    83.16301   122.45662    56.49211
#>  [631]    63.60239    54.69072    60.57524    60.57841   119.51698    78.26182
#>  [637]    58.15718    55.59131    57.99808   259.91544    60.92896    56.44035
#>  [643]    59.93401   230.26231    60.72489    59.64239   112.04373   134.10354
#>  [649]    61.69851    56.93391    53.23962    62.29205   131.77925    81.34570
#>  [655]    54.63316    60.56425    58.55654    63.49116    70.17445   309.91411
#>  [661]   991.53268    66.52219    63.63528    62.80000    66.85555    58.43228
#>  [667]   151.01201    66.48049    89.16396   170.61453   141.24629    75.09816
#>  [673]    86.85418    78.58872    62.01993    62.42726    65.78144   156.62804
#>  [679]    58.50840    85.29922    87.28206    61.90737    58.00194    79.27219
#>  [685]    75.10377    53.51461   104.03589    54.49345    55.98998    65.27136
#>  [691]   104.60986   104.03580    66.24396    58.81110    70.85218    60.86713
#>  [697]    55.23185    57.74325    74.87498    97.15822   102.67595   117.46278
#>  [703]    61.69167   120.41804    62.14557    64.84509    78.22917    56.09853
#>  [709]    61.11440    71.83895    69.31492    65.02808    58.76659    65.54994
#>  [715]    81.72544    92.46902    95.87010    66.71112    92.69534    60.18555
#>  [721]    63.44475    68.42171    63.53734    56.00148    93.11214    62.60792
#>  [727]    61.42634    60.15825   127.91178    72.78889    60.35132    58.87335
#>  [733]    78.38057    88.06446    80.80705    53.58566    58.27767    66.87579
#>  [739]    61.05356    75.42859    57.36241   172.68151   101.95209    61.47146
#>  [745]    68.66093    58.05448    59.43166    68.76280   103.14476   114.63595
#>  [751]   622.25362   222.94308   135.79194    64.27772    60.69795    91.23301
#>  [757]    48.61924    80.75222    67.75902    82.28554    65.55032    68.13425
#>  [763]    59.12799    62.03315    81.40562    64.36386    57.12992    66.86147
#>  [769]    70.47417    79.64640   128.98429    72.04402    59.08023    79.50329
#>  [775]    61.83102    60.69912    78.25068    65.89107    63.96416    56.29142
#>  [781]    82.02093    56.31948    88.59021   249.69694    80.28229   141.91326
#>  [787]    55.34266    87.34149    63.39857    77.15425    65.66089    62.43839
#>  [793]   128.45258    61.38677    55.91381    62.35237    68.51142    90.25432
#>  [799]    58.93906    64.34589    63.18804    60.60582   148.51455    75.26704
#>  [805]    58.33938    72.08102   152.47315    57.57289    62.53949    59.12800
#>  [811]    59.68089    62.91155    56.11251    66.84659    69.25661    74.67568
#>  [817]    57.87790    61.01823    62.71737    60.88661    81.00258    96.65311
#>  [823]    53.82560    68.11794    75.37969    59.47665    81.00025   854.77425
#>  [829]  6827.19360   214.07965    57.26199    59.97581    66.63219    65.88275
#>  [835]    64.32640    65.15567    61.59271    67.49849   130.55662   131.24085
#>  [841]    99.89431    97.65870    53.94421   107.40847    60.92546    53.22494
#>  [847]    59.78243    87.19408    59.95249    54.29132    63.27307    86.02191
#>  [853]    69.86104    61.06065    54.78935    57.02174    61.09832   129.75891
#>  [859]    54.04988    63.34186    60.41542    63.00762    62.21385    87.72803
#>  [865]    63.42007    61.76385    56.41218    85.49442    52.91071    60.07299
#>  [871]    59.80442    71.45256    64.49521    85.73424    66.83804    89.19792
#>  [877]    68.73213    59.88153    70.20294    57.93322    56.07507    63.11063
#>  [883]    57.22701    71.98468    67.74792    64.32974    56.76483    98.30032
#>  [889]    56.65536    63.82937    59.61358    70.36302    56.75255   404.63390
#>  [895]   526.65840   239.71642   102.90413    59.05425    63.35038    54.71617
#>  [901]    60.84603   144.62554   105.70448    68.60926    64.31514    61.69997
#>  [907]    64.22001    67.57138    70.37638    60.52100    69.88037    69.21348
#>  [913]    62.08789   142.42013   133.73320    89.74602    63.07910    74.89157
#>  [919]    54.73143    64.58380    57.79274    66.93055    60.06419    64.42766
#>  [925]    66.55770    79.41137   133.21290  2161.75599    74.77025    51.99680
#>  [931]    59.05751    72.70578    56.80211    76.03514    63.91793    67.17709
#>  [937]    60.15831    71.06421    46.06837   221.48864    57.50227    61.63267
#>  [943]    59.07225   227.96803    67.94468    60.20115    69.63022    65.42689
#>  [949]    66.63143    61.46353    76.10689    96.67576    80.48124   134.77705
#>  [955]    71.62172    76.66603    88.73064    69.17014    54.52583    90.63418
#>  [961]    68.51733    52.00745    64.76441    66.97515    64.48971    64.32554
#>  [967]   139.31996    52.43975    55.44642   658.00033    62.43100    57.98204
#>  [973]   149.08745    57.50710    84.27164    65.18285    59.76262    86.15766
#>  [979]    62.98944    70.17326   102.86325    60.31634    61.18076    83.78990
#>  [985]    58.57603   100.56923    94.67110    54.26612    85.62390    56.05818
#>  [991]    59.02113    64.07938    72.45904    67.90281   144.73576 15251.35398
#>  [997]   290.74519    72.90779   267.87813   209.44560