library(chlorpromazineR)For a walkthrough on this package’s functions and how to use them, see the walkthrough vignette. The purpose of this vignette is to exhibit the antipsychotics included by the conversion keys and their dose equivalents.
For the reference, see help(gardner2010).
data_gardner_oral_names <- c("Amisulpride", "Aripiprazole", "Benperidol", "Chlorpromazine",
"Clopenthixol", "Clorprothixene", "Clotiapine", "Clozapine",
"Droperidol", "Flupenthixol", "Fluphenazine", "Haloperidol",
"Levomepromazine", "Loxapine", "Mesoridazine",
"Methotrimeprazine", "Molindone", "Olanzapine", "Oxypertine",
"Paliperidone", "Pericyazine", "Perphenazine", "Pimozide",
"Prochlorperazine", "Quetiapine", "Remoxipride", "Risperidone",
"Sertindole", "Sulpiride", "Thioridazine", "Thiothixene",
"Trifluoperazine", "Trifluperidol", "Triflupromazine",
"Ziprasidone", "Zotepine", "Zuclopenthixol")
data_gardner_oral_median <- c(700, 30, 5, 600, 60, 500, 100, 400, 10, 10, 12, 10, 400,
60, 300, 300, 100, 20, 240, 9, 50, 30, 8, 88, 750, 212, 6,
20, 800, 500, 30, 20, 2, 100, 160, 300, 50)
data_gardner_oral <- data.frame(ap = data_gardner_oral_names,
dose = data_gardner_oral_median)to_ap(data_gardner_oral, convert_to_ap = "olanzapine", ap_label = "ap",
dose_label = "dose", route = "oral")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 amisulpride 700 0.8571429 600 20
#> 2 aripiprazole 30 20.0000000 600 20
#> 3 benperidol 5 120.0000000 600 20
#> 4 chlorpromazine 600 1.0000000 600 20
#> 5 clopenthixol 60 10.0000000 600 20
#> 6 clorprothixene 500 1.2000000 600 20
#> 7 clotiapine 100 6.0000000 600 20
#> 8 clozapine 400 1.5000000 600 20
#> 9 droperidol 10 60.0000000 600 20
#> 10 flupenthixol 10 60.0000000 600 20
#> 11 fluphenazine 12 50.0000000 600 20
#> 12 haloperidol 10 60.0000000 600 20
#> 13 levomepromazine 400 1.5000000 600 20
#> 14 loxapine 60 10.0000000 600 20
#> 15 mesoridazine 300 2.0000000 600 20
#> 16 methotrimeprazine 300 2.0000000 600 20
#> 17 molindone 100 6.0000000 600 20
#> 18 olanzapine 20 30.0000000 600 20
#> 19 oxypertine 240 2.5000000 600 20
#> 20 paliperidone 9 66.6666667 600 20
#> 21 pericyazine 50 12.0000000 600 20
#> 22 perphenazine 30 20.0000000 600 20
#> 23 pimozide 8 75.0000000 600 20
#> 24 prochlorperazine 88 6.8181818 600 20
#> 25 quetiapine 750 0.8000000 600 20
#> 26 remoxipride 212 2.8301887 600 20
#> 27 risperidone 6 100.0000000 600 20
#> 28 sertindole 20 30.0000000 600 20
#> 29 sulpiride 800 0.7500000 600 20
#> 30 thioridazine 500 1.2000000 600 20
#> 31 thiothixene 30 20.0000000 600 20
#> 32 trifluoperazine 20 30.0000000 600 20
#> 33 trifluperidol 2 300.0000000 600 20
#> 34 triflupromazine 100 6.0000000 600 20
#> 35 ziprasidone 160 3.7500000 600 20
#> 36 zotepine 300 2.0000000 600 20
#> 37 zuclopenthixol 50 12.0000000 600 20data_gardner_oral_median_cpz100 <- data_gardner_oral_median / 6
data_gardner_oral_cpz100 <- data.frame(ap = data_gardner_oral_names,
dose=data_gardner_oral_median_cpz100)
to_ap(data_gardner_oral_cpz100, convert_to_ap = "olanzapine",
ap_label = "ap", dose_label = "dose", route = "oral")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 amisulpride 116.6666667 0.8571429 100 3.333333
#> 2 aripiprazole 5.0000000 20.0000000 100 3.333333
#> 3 benperidol 0.8333333 120.0000000 100 3.333333
#> 4 chlorpromazine 100.0000000 1.0000000 100 3.333333
#> 5 clopenthixol 10.0000000 10.0000000 100 3.333333
#> 6 clorprothixene 83.3333333 1.2000000 100 3.333333
#> 7 clotiapine 16.6666667 6.0000000 100 3.333333
#> 8 clozapine 66.6666667 1.5000000 100 3.333333
#> 9 droperidol 1.6666667 60.0000000 100 3.333333
#> 10 flupenthixol 1.6666667 60.0000000 100 3.333333
#> 11 fluphenazine 2.0000000 50.0000000 100 3.333333
#> 12 haloperidol 1.6666667 60.0000000 100 3.333333
#> 13 levomepromazine 66.6666667 1.5000000 100 3.333333
#> 14 loxapine 10.0000000 10.0000000 100 3.333333
#> 15 mesoridazine 50.0000000 2.0000000 100 3.333333
#> 16 methotrimeprazine 50.0000000 2.0000000 100 3.333333
#> 17 molindone 16.6666667 6.0000000 100 3.333333
#> 18 olanzapine 3.3333333 30.0000000 100 3.333333
#> 19 oxypertine 40.0000000 2.5000000 100 3.333333
#> 20 paliperidone 1.5000000 66.6666667 100 3.333333
#> 21 pericyazine 8.3333333 12.0000000 100 3.333333
#> 22 perphenazine 5.0000000 20.0000000 100 3.333333
#> 23 pimozide 1.3333333 75.0000000 100 3.333333
#> 24 prochlorperazine 14.6666667 6.8181818 100 3.333333
#> 25 quetiapine 125.0000000 0.8000000 100 3.333333
#> 26 remoxipride 35.3333333 2.8301887 100 3.333333
#> 27 risperidone 1.0000000 100.0000000 100 3.333333
#> 28 sertindole 3.3333333 30.0000000 100 3.333333
#> 29 sulpiride 133.3333333 0.7500000 100 3.333333
#> 30 thioridazine 83.3333333 1.2000000 100 3.333333
#> 31 thiothixene 5.0000000 20.0000000 100 3.333333
#> 32 trifluoperazine 3.3333333 30.0000000 100 3.333333
#> 33 trifluperidol 0.3333333 300.0000000 100 3.333333
#> 34 triflupromazine 16.6666667 6.0000000 100 3.333333
#> 35 ziprasidone 26.6666667 3.7500000 100 3.333333
#> 36 zotepine 50.0000000 2.0000000 100 3.333333
#> 37 zuclopenthixol 8.3333333 12.0000000 100 3.333333data_gardner_sai_names <- c("Chlorpromazine HCl", "Clotiapine injectable",
"Fluphenazine HCl", "Haloperidol lactate",
"Loxapine HCl", "Mesoridazine besylate",
"Olanzapine tartrate", "Perphenazine USP",
"Prochlorperazine mesylate", "Promazine HCl",
"Trifluoperazine HCl", "Triflupromazine HCl",
"Ziprasidone mesylate", "Zuclopenthixol acetate")
data_gardner_sai_median <- c(100, 40, 5, 5, 25, 100, 10, 10, 22, 100,
5, 60, 20, 50)
data_gardner_sai <- data.frame(ap = data_gardner_sai_names,
dose = data_gardner_sai_median)
to_cpz(data_gardner_sai, key=gardner2010_withsai, ap_label = "ap",
dose_label = "dose", route = "sai")
#> ap dose cpz_conv_factor cpz_eq
#> 1 chlorpromazine hcl 100 1.000000 100
#> 2 clotiapine injectable 40 2.500000 100
#> 3 fluphenazine hcl 5 20.000000 100
#> 4 haloperidol lactate 5 20.000000 100
#> 5 loxapine hcl 25 4.000000 100
#> 6 mesoridazine besylate 100 1.000000 100
#> 7 olanzapine tartrate 10 10.000000 100
#> 8 perphenazine usp 10 10.000000 100
#> 9 prochlorperazine mesylate 22 4.545455 100
#> 10 promazine hcl 100 1.000000 100
#> 11 trifluoperazine hcl 5 20.000000 100
#> 12 triflupromazine hcl 60 1.666667 100
#> 13 ziprasidone mesylate 20 5.000000 100
#> 14 zuclopenthixol acetate 50 2.000000 100to_ap(data_gardner_sai, key=gardner2010_withsai,
convert_to_ap = "haloperidol lactate",
ap_label = "ap", dose_label = "dose", route = "sai",
convert_to_route = "sai")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 chlorpromazine hcl 100 1.000000 100 5
#> 2 clotiapine injectable 40 2.500000 100 5
#> 3 fluphenazine hcl 5 20.000000 100 5
#> 4 haloperidol lactate 5 20.000000 100 5
#> 5 loxapine hcl 25 4.000000 100 5
#> 6 mesoridazine besylate 100 1.000000 100 5
#> 7 olanzapine tartrate 10 10.000000 100 5
#> 8 perphenazine usp 10 10.000000 100 5
#> 9 prochlorperazine mesylate 22 4.545455 100 5
#> 10 promazine hcl 100 1.000000 100 5
#> 11 trifluoperazine hcl 5 20.000000 100 5
#> 12 triflupromazine hcl 60 1.666667 100 5
#> 13 ziprasidone mesylate 20 5.000000 100 5
#> 14 zuclopenthixol acetate 50 2.000000 100 5data_gardner_lai_names <- c("Clopenthixol decanoate", "Flupenthixol decanoate",
"Fluphenazine decanoate", "Fluphenazine enanthate",
"Fluspirilene", "Haloperidol decanoate",
"Perphenazine enanthate", "Pipotiazine palmitate",
"Risperidone microspheres", "Zuclopenthixol decanoate")
data_gardner_lai_median <- c(300, 40, 25, 25, 6, 150, 100, 100, 50, 200)
data_gardner_lai_q <- c(14, 14, 14, 14, 7, 28, 14, 28, 14, 14)
data_gardner_lai <- data.frame(ap = data_gardner_lai_names,
dose = data_gardner_lai_median,
q = data_gardner_lai_q)
to_cpz(data_gardner_lai, key=gardner2010, ap_label = "ap",
dose_label = "dose", route = "lai", q_label = "q")
#> ap dose q cpz_conv_factor cpz_eq
#> 1 clopenthixol decanoate 300 14 28 600
#> 2 flupenthixol decanoate 40 14 210 600
#> 3 fluphenazine decanoate 25 14 336 600
#> 4 fluphenazine enanthate 25 14 336 600
#> 5 fluspirilene 6 7 700 600
#> 6 haloperidol decanoate 150 28 112 600
#> 7 perphenazine enanthate 100 14 84 600
#> 8 pipotiazine palmitate 100 28 168 600
#> 9 risperidone microspheres 50 14 168 600
#> 10 zuclopenthixol decanoate 200 14 42 600For the reference, see help(davis1974).
data_davis_names <- c("Chlorpromazine", "Triflupromazine", "Thioridazine", "Prochlorperazine",
"Perphenazine", "Fluphenazine", "Trifluoperazine", "Acetophenazine",
"Carphenazine", "Butaperazine", "Mesoridazine", "Piperacetazine",
"Haloperidol", "Chlorprothixene", "Thiothixene")
data_davis_doses <- c(100, 28.4, 95.3, 14.3, 8.9, 1.2, 2.8, 23.5, 24.3, 8.9, 55.3, 10.5, 1.6,
43.9, 5.2)
data_davis_oral <- data.frame(ap = data_davis_names,
dose = data_davis_doses)to_cpz(data_davis_oral, ap_label = "ap",
dose_label = "dose", route = "oral", key=davis1974)
#> ap dose cpz_conv_factor cpz_eq
#> 1 chlorpromazine 100.0 1.000000 100
#> 2 triflupromazine 28.4 3.521127 100
#> 3 thioridazine 95.3 1.049318 100
#> 4 prochlorperazine 14.3 6.993007 100
#> 5 perphenazine 8.9 11.235955 100
#> 6 fluphenazine 1.2 83.333333 100
#> 7 trifluoperazine 2.8 35.714286 100
#> 8 acetophenazine 23.5 4.255319 100
#> 9 carphenazine 24.3 4.115226 100
#> 10 butaperazine 8.9 11.235955 100
#> 11 mesoridazine 55.3 1.808318 100
#> 12 piperacetazine 10.5 9.523810 100
#> 13 haloperidol 1.6 62.500000 100
#> 14 chlorprothixene 43.9 2.277904 100
#> 15 thiothixene 5.2 19.230769 100The Davis ket converts parenteral (SAI) to oral chlorpromazine equivalents on the basis of the statement in the text that oral is assumed to be 3x the potency of oral.
to_cpz(data_davis_oral, ap_label = "ap",
dose_label = "dose", route = "sai", key=davis1974)
#> ap dose cpz_conv_factor cpz_eq
#> 1 chlorpromazine 100.0 3.000000 300
#> 2 triflupromazine 28.4 10.563380 300
#> 3 thioridazine 95.3 3.147954 300
#> 4 prochlorperazine 14.3 20.979021 300
#> 5 perphenazine 8.9 33.707865 300
#> 6 fluphenazine 1.2 250.000000 300
#> 7 trifluoperazine 2.8 107.142857 300
#> 8 acetophenazine 23.5 12.765957 300
#> 9 carphenazine 24.3 12.345679 300
#> 10 butaperazine 8.9 33.707865 300
#> 11 mesoridazine 55.3 5.424955 300
#> 12 piperacetazine 10.5 28.571429 300
#> 13 haloperidol 1.6 187.500000 300
#> 14 chlorprothixene 43.9 6.833713 300
#> 15 thiothixene 5.2 57.692308 300For the reference, see help(leucht2016).
leucht_names <- c("Acepromazine", "Acetophenazine", "Amisulpride", "Aripiprazole",
"Asenapine", "Benperidol", "Bromperidol", "Butaperazine", "Cariprazine",
"Chlorproethazine", "Chlorpromazine", "Chlorprothixene", "Clopenthixol",
"Clotiapine", "Clozapine", "Cyamemazine", "Dixyrazine", "Droperidol",
"Fluanisone", "Flupentixol", "Fluphenazine", "Fluspirilene", "Haloperidol",
"Iloperidone", "Levomepromazine", "Levosulpiride", "Loxapine", "Lurasidone",
"Melperone", "Mesoridazine", "Molindone", "Moperone", "Mosapramine",
"Olanzapine", "Oxypertine", "Paliperidone", "Penfluridol", "Perazine",
"Periciazine", "Perphenazine", "Pimozide", "Pipamperone", "Pipotiazine",
"Prochlorperazine", "Promazine", "Prothipendyl", "Quetiapine", "Remoxipride",
"Risperidone", "Sertindole", "Sulpiride", "Sultopride", "Thiopropazate",
"Thioproperazine", "Thioridazine", "Tiapride", "Tiotixene",
"Trifluoperazine", "Trifluperidol", "Triflupromazine", "Veralipride",
"Ziprasidone", "Zotepine", "Zuclopenthixol")
leucht_DDD_oral <- c(100, 50, 400, 15, 20, 1.5, 10, 10, NA, NA, 300, 300, 100, 80, 300, NA,
50, NA, NA, 6, 10, NA, 8, NA, 300, 400, 100, 60, 300, 200, 50, 20, NA,
10, 120, 6, 6, 100, 50, 30, 4, 200, 10, 100, 300, 240, 400, 300, 5, 16,
800, 1200, 60, 75, 300, 400, 30, 20, 2, 100, NA, 80, 200, 30)
leucht_DDD_sai <- c(50, NA, NA, 15, NA, NA, 10, NA, NA, NA, 100, 50, 100, 80, 300, NA, 30,
2.5, NA, NA, NA, NA, 8, NA, 100, NA, NA, NA, 300, 200, NA, 20, NA, 10,
NA, NA, NA, 100, 20, 10, NA, NA, NA, 50, 100, 240, NA, 300, NA, NA,
800, NA, NA, 20, NA, 400, NA, 8, NA, 100, NA, 40, NA, 30)
leucht_DDD_lai <- c(NA, NA, NA, NA, NA, NA, 3.3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 4, 1, 0.7, 3.3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.0,
NA, 2.5, NA, NA, NA, 7.0, NA, NA, 5, NA, NA, NA, NA, NA, 2.7, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15)
data_leucht_DDD_oral <- data.frame(ap = leucht_names,
dose = leucht_DDD_oral)
data_leucht_DDD_sai <- data.frame(ap = leucht_names,
dose = leucht_DDD_sai)
# pretend that all are given q 14 days
data_leucht_DDD_lai <- data.frame(ap = leucht_names,
dose = (leucht_DDD_lai*14),
q = rep(14, 64))
data_leucht_DDD_oral <- data_leucht_DDD_oral[!is.na(data_leucht_DDD_oral$dose),]
data_leucht_DDD_sai <- data_leucht_DDD_sai[!is.na(data_leucht_DDD_sai$dose),]
data_leucht_DDD_lai <- data_leucht_DDD_lai[!is.na(data_leucht_DDD_lai$dose),]
to_ap(data_leucht_DDD_oral, ap_label = "ap", dose_label = "dose",
route = "oral", key=leucht2016, convert_to_ap = "olanzapine")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 acepromazine 100.0 3.0003000 300.0300 9.990999
#> 2 acetophenazine 50.0 5.9988002 299.9400 9.988002
#> 3 amisulpride 400.0 0.7500188 300.0075 9.990250
#> 4 aripiprazole 15.0 20.0000000 300.0000 9.990000
#> 5 asenapine 20.0 14.9925037 299.8501 9.985007
#> 6 benperidol 1.5 200.0000000 300.0000 9.990000
#> 7 bromperidol 10.0 30.0300300 300.3003 10.000000
#> 8 butaperazine 10.0 30.0300300 300.3003 10.000000
#> 11 chlorpromazine 300.0 1.0000000 300.0000 9.990000
#> 12 chlorprothixene 300.0 1.0000000 300.0000 9.990000
#> 13 clopenthixol 100.0 3.0003000 300.0300 9.990999
#> 14 clotiapine 80.0 3.7495313 299.9625 9.988751
#> 15 clozapine 300.0 1.0000000 300.0000 9.990000
#> 17 dixyrazine 50.0 5.9988002 299.9400 9.988002
#> 20 flupentixol 6.0 50.0000000 300.0000 9.990000
#> 21 fluphenazine 10.0 30.0300300 300.3003 10.000000
#> 23 haloperidol 8.0 37.4531835 299.6255 9.977528
#> 25 levomepromazine 300.0 1.0000000 300.0000 9.990000
#> 26 levosulpiride 400.0 0.7500188 300.0075 9.990250
#> 27 loxapine 100.0 3.0003000 300.0300 9.990999
#> 28 lurasidone 60.0 5.0000000 300.0000 9.990000
#> 29 melperone 300.0 1.0000000 300.0000 9.990000
#> 30 mesoridazine 200.0 1.4999250 299.9850 9.989501
#> 31 molindone 50.0 5.9988002 299.9400 9.988002
#> 32 moperone 20.0 14.9925037 299.8501 9.985007
#> 34 olanzapine 10.0 30.0300300 300.3003 10.000000
#> 35 oxypertine 120.0 2.5000000 300.0000 9.990000
#> 36 paliperidone 6.0 50.0000000 300.0000 9.990000
#> 37 penfluridol 6.0 50.0000000 300.0000 9.990000
#> 38 perazine 100.0 3.0003000 300.0300 9.990999
#> 39 periciazine 50.0 5.9988002 299.9400 9.988002
#> 40 perphenazine 30.0 10.0000000 300.0000 9.990000
#> 41 pimozide 4.0 75.1879699 300.7519 10.015038
#> 42 pipamperone 200.0 1.4999250 299.9850 9.989501
#> 43 pipotiazine 10.0 30.0300300 300.3003 10.000000
#> 44 prochlorperazine 100.0 3.0003000 300.0300 9.990999
#> 45 promazine 300.0 1.0000000 300.0000 9.990000
#> 46 prothipendyl 240.0 1.2500000 300.0000 9.990000
#> 47 quetiapine 400.0 0.7500188 300.0075 9.990250
#> 48 remoxipride 300.0 1.0000000 300.0000 9.990000
#> 49 risperidone 5.0 59.8802395 299.4012 9.970060
#> 50 sertindole 16.0 18.7617261 300.1876 9.996248
#> 51 sulpiride 800.0 0.3749953 299.9963 9.989875
#> 52 sultopride 1200.0 0.2500000 300.0000 9.990000
#> 53 thiopropazate 60.0 5.0000000 300.0000 9.990000
#> 54 thioproperazine 75.0 4.0000000 300.0000 9.990000
#> 55 thioridazine 300.0 1.0000000 300.0000 9.990000
#> 56 tiapride 400.0 0.7500188 300.0075 9.990250
#> 57 tiotixene 30.0 10.0000000 300.0000 9.990000
#> 58 trifluoperazine 20.0 14.9925037 299.8501 9.985007
#> 59 trifluperidol 2.0 149.2537313 298.5075 9.940299
#> 60 triflupromazine 100.0 3.0003000 300.0300 9.990999
#> 62 ziprasidone 80.0 3.7495313 299.9625 9.988751
#> 63 zotepine 200.0 1.4999250 299.9850 9.989501
#> 64 zuclopenthixol 30.0 10.0000000 300.0000 9.990000
to_ap(data_leucht_DDD_sai, ap_label = "ap", dose_label = "dose",
route = "sai", key=leucht2016, convert_to_ap = "olanzapine",
convert_to_route = "sai")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 acepromazine 50.0 5.9988002 299.9400 9.988002
#> 4 aripiprazole 15.0 20.0000000 300.0000 9.990000
#> 7 bromperidol 10.0 30.0300300 300.3003 10.000000
#> 11 chlorpromazine 100.0 3.0003000 300.0300 9.990999
#> 12 chlorprothixene 50.0 5.9988002 299.9400 9.988002
#> 13 clopenthixol 100.0 3.0003000 300.0300 9.990999
#> 14 clotiapine 80.0 3.7495313 299.9625 9.988751
#> 15 clozapine 300.0 1.0000000 300.0000 9.990000
#> 17 dixyrazine 30.0 10.0000000 300.0000 9.990000
#> 18 droperidol 2.5 120.4819277 301.2048 10.030120
#> 23 haloperidol 8.0 37.4531835 299.6255 9.977528
#> 25 levomepromazine 100.0 3.0003000 300.0300 9.990999
#> 29 melperone 300.0 1.0000000 300.0000 9.990000
#> 30 mesoridazine 200.0 1.4999250 299.9850 9.989501
#> 32 moperone 20.0 14.9925037 299.8501 9.985007
#> 34 olanzapine 10.0 30.0300300 300.3003 10.000000
#> 38 perazine 100.0 3.0003000 300.0300 9.990999
#> 39 periciazine 20.0 14.9925037 299.8501 9.985007
#> 40 perphenazine 10.0 30.0300300 300.3003 10.000000
#> 44 prochlorperazine 50.0 5.9988002 299.9400 9.988002
#> 45 promazine 100.0 3.0003000 300.0300 9.990999
#> 46 prothipendyl 240.0 1.2500000 300.0000 9.990000
#> 48 remoxipride 300.0 1.0000000 300.0000 9.990000
#> 51 sulpiride 800.0 0.3749953 299.9963 9.989875
#> 54 thioproperazine 20.0 14.9925037 299.8501 9.985007
#> 56 tiapride 400.0 0.7500188 300.0075 9.990250
#> 58 trifluoperazine 8.0 37.4531835 299.6255 9.977528
#> 60 triflupromazine 100.0 3.0003000 300.0300 9.990999
#> 62 ziprasidone 40.0 7.5018755 300.0750 9.992498
#> 64 zuclopenthixol 30.0 10.0000000 300.0000 9.990000
to_ap(data_leucht_DDD_lai, ap_label = "ap", dose_label = "dose",
route = "lai", key=leucht2016, convert_to_ap = "olanzapine", q = "q")
#> ap dose q cpz_conv_factor cpz_eq ap_eq
#> 7 bromperidol 46.2 14 90.90909 300.0000 9.99000
#> 20 flupentixol 56.0 14 75.18797 300.7519 10.01504
#> 21 fluphenazine 14.0 14 303.03030 303.0303 10.09091
#> 22 fluspirilene 9.8 14 434.78261 304.3478 10.13478
#> 23 haloperidol 46.2 14 90.90909 300.0000 9.99000
#> 34 olanzapine 140.0 14 30.03003 300.3003 10.00000
#> 36 paliperidone 35.0 14 120.48193 301.2048 10.03012
#> 40 perphenazine 98.0 14 42.91845 300.4292 10.00429
#> 43 pipotiazine 70.0 14 59.88024 299.4012 9.97006
#> 49 risperidone 37.8 14 111.11111 300.0000 9.99000
#> 64 zuclopenthixol 210.0 14 20.00000 300.0000 9.99000For the reference, see help(woods2003).
woods_names <- c("haloperidol", "risperidone", "olanzapine",
"quetiapine", "ziprasidone", "aripiprazole")
woods_doses <- c(2, 2, 5, 75, 60, 7.5)
woods_oral <- data.frame(ap = woods_names,
dose = woods_doses)
to_ap(woods_oral, route="oral", ap_label="ap",
dose="dose", key=woods2003,
convert_to_ap = "olanzapine")
#> ap dose cpz_conv_factor cpz_eq ap_eq
#> 1 haloperidol 2.0 50.000000 100 5
#> 2 risperidone 2.0 50.000000 100 5
#> 3 olanzapine 5.0 20.000000 100 5
#> 4 quetiapine 75.0 1.333333 100 5
#> 5 ziprasidone 60.0 1.666667 100 5
#> 6 aripiprazole 7.5 13.333333 100 5