Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples
Source:R/Model-methods.R
biomarker.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Usage
biomarker(xLevel, model, samples, ...)
# S4 method for class 'integer,DualEndpoint,Samples'
biomarker(xLevel, model, samples, ...)Arguments
- xLevel
(
integer)
the levels for the doses the patients have been given w.r.t dose grid. SeeDatafor more details.- model
(
DualEndpoint)
the model.- samples
(
Samples)
the samples of model's parameters that store the value of biomarker levels for all doses on the dose grid.- ...
not used.
Details
This function simply returns a specific columns (with the indices equal
to xLevel) of the biomarker samples matrix, which is included in the the
samples object.
Examples
# Create the data.
my_data <- DataDual(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
ID = 1:17,
cohort = c(1L, 2L, 3L, 4L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L),
w = c(
0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6, 0.52, 0.54,
0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21
),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
mean = c(0, 1),
cov = matrix(c(1, 0, 0, 1), nrow = 2),
sigma2betaW = 0.01,
sigma2W = c(a = 0.1, b = 0.1),
rho = c(a = 1, b = 1),
rw1 = TRUE
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(
burnin = 100,
step = 2,
samples = 500
)
my_samples <- mcmc(my_data, my_model, my_options)
# Obtain the biomarker levels (samples) for the second dose from the dose grid,
# which is 0.5.
biomarker(
xLevel = 2L,
model = my_model,
samples = my_samples
)
#> [1] 0.39308712 0.40654225 0.43370971 0.39276324 0.38863022 0.31367627
#> [7] 0.27558324 0.37032295 0.40616447 0.35839564 0.38668611 0.36246081
#> [13] 0.36724200 0.39998700 0.36807695 0.37361258 0.35693000 0.28145816
#> [19] 0.11883598 0.16207586 0.26626122 0.34350131 0.39292524 0.31491453
#> [25] 0.31826275 0.45435923 0.47902842 0.52378386 0.50427392 0.53625455
#> [31] 0.48021906 0.45111006 0.39350075 0.30015349 0.43219968 0.41358752
#> [37] 0.37373453 0.38352637 0.45866401 0.52034754 0.52576558 0.51977900
#> [43] 0.36479257 0.40305581 0.45609250 0.40714936 0.44317857 0.42815040
#> [49] 0.39874037 0.39206962 0.37855864 0.43186632 0.55175785 0.52588817
#> [55] 0.63539921 0.70049025 0.50327809 0.50969882 0.42797236 0.40807520
#> [61] 0.36694719 0.38568833 0.39416008 0.47404443 0.40408221 0.34839761
#> [67] 0.29640341 0.43004508 0.41390208 0.39977708 0.37845252 0.41643905
#> [73] 0.48646349 0.43532171 0.42731539 0.42533261 0.48729906 0.58426026
#> [79] 0.54797470 0.45827535 0.45351933 0.38573890 0.43744659 0.57507765
#> [85] 0.63243893 0.63044055 0.68485099 0.58005397 0.52562721 0.48618750
#> [91] 0.44662897 0.44819648 0.28928374 0.21620025 0.17259309 -0.11097190
#> [97] 0.05568965 0.16617959 0.48949129 0.45058975 0.44741294 0.47859349
#> [103] 0.52239486 0.53091985 0.42380252 0.45292971 0.39368497 0.27289887
#> [109] 0.33290268 0.28882489 0.37067482 0.27702977 0.22039160 0.21674679
#> [115] 0.31627081 0.22442580 0.28911110 0.40481628 0.50320891 0.51387972
#> [121] 0.54554219 0.43899862 0.35845075 0.35305032 0.41964125 0.41855276
#> [127] 0.46447683 0.49871169 0.55180985 0.57889428 0.60475961 0.53279132
#> [133] 0.48907207 0.56702934 0.53092524 0.51237444 0.55220485 0.35791901
#> [139] 0.42025887 0.49214894 0.54176332 0.52495157 0.50084886 0.45718359
#> [145] 0.32822244 0.42465729 0.46386045 0.48104617 0.45468873 0.49796432
#> [151] 0.45345125 0.36573996 0.31859414 0.37639966 0.38580164 0.36180348
#> [157] 0.47180810 0.46596469 0.51173447 0.39701281 0.46146373 0.47441604
#> [163] 0.44644661 0.49095758 0.48375592 0.50766490 0.46640442 0.50329773
#> [169] 0.44414732 0.41645133 0.38145211 0.44291825 0.31256555 0.45652136
#> [175] 0.46081816 0.37878557 0.33558322 0.31368780 0.45344054 0.49713169
#> [181] 0.47717870 0.50656817 0.56849632 0.47333406 0.58676943 0.61394245
#> [187] 0.64338256 0.48808817 0.45529877 0.40007030 0.34265288 0.28386982
#> [193] 0.35526519 0.43161571 0.43005538 0.42834809 0.41123343 0.53566050
#> [199] 0.55359588 0.61836912 0.60807068 0.58801218 0.44370642 0.61553765
#> [205] 0.49241285 0.39335454 0.45305576 0.51724401 0.51807324 0.48131570
#> [211] 0.41384227 0.50705483 0.40478603 0.44964683 0.37568391 0.40448047
#> [217] 0.37985109 0.35480022 0.37619092 0.31047753 0.49083990 0.46170344
#> [223] 0.50339333 0.39288577 0.44710091 0.49949772 0.46795856 0.44410629
#> [229] 0.38663646 0.45557225 0.45599826 0.50509128 0.56227511 0.67294067
#> [235] 0.54928808 0.55935696 0.44965302 0.39936998 0.36330182 0.22834027
#> [241] 0.30210980 0.42556927 0.41281124 0.36487639 0.36280339 0.28050467
#> [247] 0.23469263 0.24974494 0.27058572 0.39746616 0.42832730 0.39540364
#> [253] 0.66507415 0.47988744 0.56175584 0.49432697 0.49933491 0.51600646
#> [259] 0.60977880 0.51351884 0.54430651 0.30579221 0.24502620 0.35380351
#> [265] 0.36465934 0.37356521 0.45041674 0.41189678 0.42468749 0.42844403
#> [271] 0.45086742 0.44942026 0.50763762 0.47435358 0.40033807 0.36163264
#> [277] 0.32737504 0.33999005 0.31956002 0.34150279 0.25871947 0.42992192
#> [283] 0.57110596 0.46448303 0.45931770 0.52973923 0.40304786 0.26285372
#> [289] 0.34636300 0.44669870 0.42838015 0.42840587 0.39506069 0.45872960
#> [295] 0.50173856 0.55582776 0.59494751 0.48108685 0.35583956 0.40562319
#> [301] 0.46463661 0.51940091 0.42149303 0.41307395 0.44183557 0.52812836
#> [307] 0.53374611 0.53548687 0.41288451 0.49077314 0.46243638 0.58033559
#> [313] 0.54596081 0.52231622 0.41767198 0.44412402 0.30564715 0.31878888
#> [319] 0.53179165 0.45463143 0.38609391 0.35328767 0.29470007 0.32495904
#> [325] 0.39707203 0.41367914 0.46525452 0.48233349 0.48449738 0.43937576
#> [331] 0.40794420 0.37369831 0.35135463 0.43104331 0.48050203 0.42502704
#> [337] 0.44078835 0.34524029 0.25749333 0.32326520 0.36547166 0.40351173
#> [343] 0.42995453 0.36278025 0.36349067 0.42362240 0.40250350 0.33573749
#> [349] 0.45921527 0.42743978 0.40540605 0.40510193 0.38027037 0.39533117
#> [355] 0.36504458 0.34714501 0.34377288 0.34811352 0.29268721 0.18393276
#> [361] 0.07662493 0.09867589 0.18029110 0.28546375 0.39193732 0.35374201
#> [367] 0.47392828 0.52247580 0.50259328 0.37618370 0.34862806 0.30846601
#> [373] 0.39109288 0.44687798 0.31633547 0.42063151 0.41545887 0.42790556
#> [379] 0.45184662 0.41180055 0.48926879 0.52691589 0.49238021 0.45941699
#> [385] 0.49580743 0.40599309 0.43936970 0.48367275 0.36321590 0.28148263
#> [391] 0.24373382 0.40729776 0.37281782 0.38539179 0.33898892 0.35749007
#> [397] 0.33499457 0.23970260 0.34827700 0.36835797 0.38298880 0.45047670
#> [403] 0.55526690 0.46935475 0.50545417 0.51813368 0.44254518 0.40205993
#> [409] 0.44851159 0.41218329 0.43375217 0.31455886 0.44683985 0.36658782
#> [415] 0.36669468 0.33346185 0.39986784 0.32066053 0.36820316 0.35364308
#> [421] 0.43252151 0.46359764 0.47788503 0.46189618 0.44853196 0.46542908
#> [427] 0.47962463 0.41285996 0.37294950 0.34146042 0.31128928 0.26649729
#> [433] 0.25667688 0.29957348 0.32717617 0.33246696 0.38373918 0.42398666
#> [439] 0.42565369 0.45513421 0.46986014 0.45822848 0.44567152 0.45391053
#> [445] 0.48729322 0.44226984 0.41533180 0.42950030 0.57071751 0.44655199
#> [451] 0.46010872 0.17865207 0.35048336 0.35771681 0.40837415 0.35323660
#> [457] 0.49103834 0.47319060 0.49343477 0.40665655 0.48905516 0.57384089
#> [463] 0.57807269 0.57059766 0.52982403 0.53591906 0.40851816 0.31856193
#> [469] 0.39848109 0.33936727 0.50308291 0.46852178 0.31164200 0.50371587
#> [475] 0.55705868 0.40625109 0.35708965 0.35944284 0.40252315 0.39968038
#> [481] 0.38292287 0.44193395 0.48057391 0.56965614 0.51384920 0.51258239
#> [487] 0.51555392 0.38401450 0.33829671 0.43487270 0.54697530 0.56760979
#> [493] 0.65309301 0.41934016 0.24104787 0.29656907 0.29315768 0.25306440
#> [499] 0.40895012 0.51303070