Estimates the prior distribution of population parameters by Monte Carlo simulations
Arguments
- dat
Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
- prior_model
A
posologyrprior population pharmacokinetics model, a list of six objects.- n_simul
An integer, the number of simulations to be run. For
n_simul =0, all ETAs are set to 0.- return_model
A boolean. Returns a rxode2 model using the simulated ETAs if set to
TRUE.
Value
If return_model is set to FALSE, a list of one element: a
dataframe $eta of the individual values of ETA.
If return_model is set to TRUE, a list of the dataframe of the
individual values of ETA, and a rxode2 model using the simulated ETAs.
Examples
# model
mod_run001 <- function() {
ini({
THETA_Cl <- 4.0
THETA_Vc <- 70.0
THETA_Ka <- 1.0
ETA_Cl ~ 0.2
ETA_Vc ~ 0.2
ETA_Ka ~ 0.2
prop.sd <- sqrt(0.05)
})
model({
TVCl <- THETA_Cl
TVVc <- THETA_Vc
TVKa <- THETA_Ka
Cl <- TVCl*exp(ETA_Cl)
Vc <- TVVc*exp(ETA_Vc)
Ka <- TVKa*exp(ETA_Ka)
K20 <- Cl/Vc
Cc <- centr/Vc
d/dt(depot) = -Ka*depot
d/dt(centr) = Ka*depot - K20*centr
Cc ~ prop(prop.sd)
})
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
TIME=c(0.0,1.0,14.0),
DV=c(NA,25.0,5.5),
AMT=c(2000,0,0),
EVID=c(1,0,0),
DUR=c(0.5,NA,NA))
# estimate the prior distribution of population parameters
poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100)
#>
#>
#>
#>
#> $eta
#> ETA_Cl ETA_Vc ETA_Ka
#> 1 -0.356283402 -0.718204842 0.262748840
#> 2 0.181760486 0.034134300 -0.148758525
#> 3 0.496977745 0.262578048 -0.590569407
#> 4 -0.050422960 0.022481073 -0.594248849
#> 5 -0.589944445 0.046335613 0.127644452
#> 6 -0.205528681 0.163884445 -0.267323046
#> 7 0.032184701 -0.416066419 -0.061589229
#> 8 0.251984307 -0.108502985 0.434539666
#> 9 0.360014569 -0.197008524 0.006368876
#> 10 0.748638159 -0.619259579 0.598004141
#> 11 -0.261826386 0.787028980 -0.973551016
#> 12 0.257174673 -0.121258353 0.181363803
#> 13 -0.578536335 -0.439782905 0.385652765
#> 14 0.172137688 -0.152223142 -0.067303297
#> 15 0.063690958 -0.019787337 0.192794594
#> 16 -0.270016327 -0.318032798 -0.135251929
#> 17 0.283652846 -0.067420751 0.179364742
#> 18 -0.615626863 0.410535869 -0.278744098
#> 19 -0.670338610 0.165592949 0.080928687
#> 20 0.153057834 0.258758163 -1.022285571
#> 21 0.289820277 -0.388943160 -0.964969935
#> 22 0.362881603 0.661858040 -0.155690410
#> 23 0.074496536 -0.326060005 0.601478754
#> 24 0.520710920 0.327066294 0.204825494
#> 25 -0.706356273 0.505765094 -0.189318499
#> 26 -0.515482693 -0.048629251 -0.129853314
#> 27 -0.198733238 -0.693697977 -0.366329383
#> 28 -0.226003021 -0.225996607 -0.238593855
#> 29 0.257563215 -0.335390402 0.859965579
#> 30 0.401446594 0.291546870 0.117498688
#> 31 -0.416181135 0.164940227 0.511740593
#> 32 0.184609510 0.386991371 -0.023583874
#> 33 -0.507490185 0.529474161 -0.030670061
#> 34 -0.542411337 0.648319889 0.111987580
#> 35 0.339544687 0.183368625 1.091264519
#> 36 -0.435362477 -0.891098172 0.143370918
#> 37 0.567342932 0.602893000 -0.077783101
#> 38 -0.167167359 -0.338809169 0.401328385
#> 39 -0.807048011 -0.171658988 1.057666291
#> 40 0.594122509 -0.258786483 -0.401453300
#> 41 -0.208117834 -0.385938219 -0.346738675
#> 42 -0.276619517 0.012772765 0.114670671
#> 43 0.369521881 0.385444133 -0.210657640
#> 44 -0.073312195 -0.742860877 -0.283443271
#> 45 0.969763134 -0.419709499 -0.609111991
#> 46 -0.507883003 0.384172350 0.450767165
#> 47 -0.755994965 0.114251662 -0.014825607
#> 48 0.093753606 -0.413170767 -0.765581131
#> 49 -0.445696349 -0.361818583 0.696092200
#> 50 -0.335634683 1.323640599 0.187242097
#> 51 0.778177523 -0.518966870 -0.440444092
#> 52 0.544350606 -0.607969397 -0.007389321
#> 53 0.232865308 -0.467590257 0.333609027
#> 54 -0.348279281 -0.431254088 -0.270891636
#> 55 -0.160277742 0.007030329 -0.122509250
#> 56 -0.850417886 -0.293385455 -0.415629169
#> 57 -0.354029626 -0.133159495 -0.283971372
#> 58 -0.349616857 -0.409964992 0.795757060
#> 59 -0.419375870 0.741272382 -0.286953436
#> 60 -0.294976504 -0.226135319 0.598089542
#> 61 0.504095183 -0.217666300 -0.098950498
#> 62 0.055149663 -0.190465975 -0.317856143
#> 63 -0.747853461 -0.177296321 -0.428659762
#> 64 0.290124270 0.037868767 -0.101023298
#> 65 -0.143907184 0.376518121 -0.413290607
#> 66 0.187080412 0.273033490 0.597583891
#> 67 0.222125540 0.067990507 0.056376377
#> 68 -0.164292808 -0.256574612 -0.266408697
#> 69 -0.142379144 0.106768516 0.098376487
#> 70 0.109333802 0.124287641 0.699968412
#> 71 -0.265077525 0.295265122 -0.726738412
#> 72 0.614862243 0.196987493 0.161421640
#> 73 0.001827896 0.584211584 0.641098420
#> 74 0.143967236 -0.718037903 0.167855246
#> 75 -0.400051420 1.363997222 0.237981818
#> 76 -0.568232591 -0.563574801 -0.408365880
#> 77 0.110920764 1.188663369 -0.059583651
#> 78 0.207406708 -0.076469938 -0.329846165
#> 79 -0.460029531 -0.626673370 0.194879502
#> 80 0.180702754 0.112867673 -0.046181755
#> 81 0.351503098 0.213002290 -0.892943673
#> 82 0.044966527 0.176452176 0.538427307
#> 83 0.640070248 0.519929656 -0.629138997
#> 84 0.298601919 0.626826806 -0.620369265
#> 85 0.793940827 -0.034879168 0.449129974
#> 86 -0.278526497 0.133821297 0.365356962
#> 87 0.361916153 -0.197153272 0.050216099
#> 88 -0.017062638 -0.401052752 -0.155132355
#> 89 0.472322104 -0.591024717 -0.147335267
#> 90 -0.551310497 -0.475133531 -0.732751368
#> 91 -0.139192504 0.019537293 0.242074042
#> 92 -0.020565163 -0.004286516 0.059194027
#> 93 0.664191784 -0.710832879 0.341218330
#> 94 -0.221888659 0.055717941 -0.236692343
#> 95 -0.337818681 0.278295703 -0.255029950
#> 96 -0.214647598 0.108692666 -0.256110331
#> 97 0.096231495 -0.365920056 -0.601794884
#> 98 -0.149768457 -0.132618895 -0.171780765
#> 99 0.668386569 0.101813600 -0.852789433
#> 100 -0.380466741 0.090938681 -0.208283767
#>
#> $model
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> # A tibble: 100 × 8
#> sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl ETA_Vc ETA_Ka
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 4 70 1 0.224 -0.356 -0.718 0.263
#> 2 2 4 70 1 0.224 0.182 0.0341 -0.149
#> 3 3 4 70 1 0.224 0.497 0.263 -0.591
#> 4 4 4 70 1 0.224 -0.0504 0.0225 -0.594
#> 5 5 4 70 1 0.224 -0.590 0.0463 0.128
#> 6 6 4 70 1 0.224 -0.206 0.164 -0.267
#> 7 7 4 70 1 0.224 0.0322 -0.416 -0.0616
#> 8 8 4 70 1 0.224 0.252 -0.109 0.435
#> 9 9 4 70 1 0.224 0.360 -0.197 0.00637
#> 10 10 4 70 1 0.224 0.749 -0.619 0.598
#> # ℹ 90 more rows
#> ── Initial Conditions ($inits): ──
#> depot centr AUC
#> 0 0 0
#>
#> Simulation without uncertainty in parameters, omega, or sigma matricies
#>
#> ── First part of data (object): ──
#> # A tibble: 200 × 14
#> sim.id time TVCl TVVc TVKa Cl Vc Ka K20 rxCc Cc depot
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 4 70 1 2.80 34.1 1.30 0.0821 34.8 34.8 7.67e+2
#> 2 1 14 4 70 1 2.80 34.1 1.30 0.0821 20.2 20.2 3.49e-5
#> 3 2 1 4 70 1 4.80 72.4 0.862 0.0662 12.7 12.7 1.06e+3
#> 4 2 14 4 70 1 4.80 72.4 0.862 0.0662 12.0 12.0 1.44e-2
#> 5 3 1 4 70 1 6.57 91.0 0.554 0.0722 7.21 7.21 1.32e+3
#> 6 3 14 4 70 1 6.57 91.0 0.554 0.0722 9.35 9.35 9.87e-1
#> # ℹ 194 more rows
#> # ℹ 2 more variables: centr <dbl>, AUC <dbl>
#>