| Title: | Assembling Data Sets for Non-Linear Mixed Effects Modeling |
|---|---|
| Description: | To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled. |
| Authors: | Olivier Barriere [aut], Mario Gonzalez Sales [aut, cre] |
| Maintainer: | Mario Gonzalez Sales <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.1 |
| Built: | 2024-10-29 04:18:06 UTC |
| Source: | https://github.com/syneoshealth/puzzle |
A dataset containing covariate information.
df_covdf_cov
A tibble with 12 rows and 4 variables:
Individual
Time, in hours
Variable
Value of the variable
A dataset containing covariate information.
df_cov_startdf_cov_start
A data frame with 4 rows and 3 variables:
Individual
Variable
Value of the variable
A dataset containing time dependent covariates.
df_cov_time_dependent_startdf_cov_time_dependent_start
A data frame with 6 rows and 4 variables:
Individual
Variable
Value of the variable
Time, in hours
A dataset containing dose information.
df_dosedf_dose
A data frame with 12 rows and 3 variables:
Individual
Time, in weeks
Dose, in mg
A dataset containing dose information in datetime format.
df_dose_datetimedf_dose_datetime
A data frame with 5 rows and 12 variables:
Individual
Treatment label
Dose, in mg
Period
Day of adminsitration
Dose, in mg
Dta ein datetime format
Timepoint
Cohort
Drug form
Treatment
Food status
A dataset containing dosing information.
df_dose_evid4df_dose_evid4
A data frame with 418 rows and 10 variables:
Individual
Period
Timepoint
Time, in hours
Dose, in mg
Treatment label
Day of adminsitration
Sequence
Treatment
Evid value
A dataset containing dosing information.
df_dose_optional_columnsdf_dose_optional_columns
A data frame with 4 rows and 6 variables:
Individual
Time, in hours
Dose, in mg
Occasion
Timepoint
Treatment
A dataset containing dosing information.
df_dose_startdf_dose_start
A data frame with 4 rows and 3 variables:
Individual
Time, in hours
Dose, in mg
A dataset containing extra times.
df_extra_timesdf_extra_times
A data frame with 251 rows and 1 variable:
Time, in hours
A dataset containing extra times in datetime format.
df_extra_times_datetimedf_extra_times_datetime
A data frame with 20 rows and 1 variable:
Individual
Datetime
Timepoint
A dataset containing extra times for an hypothetical metabolite.
df_extra_times_metabolite_evid4df_extra_times_metabolite_evid4
A data frame with 770 rows and 3 variable:
Period
Timepoint
Time, in hours
A dataset containing extra times for an hypothetical parent drug.
df_extra_times_parent_evid4df_extra_times_parent_evid4
A data frame with 770 rows and 3 variable:
Period
Timepoint
Time, in hours
A dataset containing extra times.
df_extra_times_timedf_extra_times_time
A data frame with 1040 rows and 3 variable:
Individual
Time, in hours
Timepoint
A dataset containing pharmacokinetic information for an hypothetical metabolite.
df_metabolite_evid4df_metabolite_evid4
A data frame with 1359 rows and 7 variables:
Individual
Period
Timepoint
Time, in hours
Drug concentration, in mg/L
Timeday
Day of adminsitration
A dataset containing pharmacokinetic information for an hypothetical parent drug.
df_parent_evid4df_parent_evid4
A data frame with 1359 rows and 7 variables:
Individual
Period
Timepoint
Time, in hours
Drug concentration, in mg/L
Timeday
Day of adminsitration
A dataset containing pharmacodynamic observations.
df_pd_startdf_pd_start
A tibble with 6 rows and 3 variable:
Individual
Time, in hours
Response, ng/mL
A dataset containing pharmacokinetic information.
df_pkdf_pk
A tibble with 132 rows and 4 variable:
Individual
Timepoint
Time, in hours
Drug concentration, ng/mL
A dataset containing pharmacokinetic information.
df_pk_datetimedf_pk_datetime
A data frame with 65 rows and 7 variable:
Individual
Response, ng/mL
Datetime
Timepoint
Day
Period
I a BLQ?
Lower limit of quantification, ng/mL
A dataset containing pharmacokinetic information for an hypothetical metabolite.
df_pk_metabolitedf_pk_metabolite
A data frame with 10 rows and 3 variable:
Individual
Time, in hours
Drug concentration, ng/mL
A dataset containing pharmacokinetic information.
df_pk_optional_columnsdf_pk_optional_columns
A data frame with 12 rows and 5 variable:
Individual
Time, in hours
Drug concentration, ng/mL
Occasion
Timepoint
A dataset containing pharmacokinetic information.
df_pk_parentdf_pk_parent
A data frame with 12 rows and 3 variable:
Individual
Time, in hours
Drug concentration, ng/mL
A dataset containing pharmacokinetic information.
A dataset containing pharmacokinetic information.
df_pk_start df_pk_startdf_pk_start df_pk_start
A tibble with 12 rows and 3 variable:
Individual
Time, in hours
Response, ng/mL
A dataset containing pharmacodynamic information for response 1.
df_response1df_response1
A data frame with 6 rows and 3 variable:
Individual
Time, in hours
Response, ng/mL
A dataset containing pharmacodynamic information for response 2.
df_response2df_response2
A data frame with 6 rows and 3 variable:
Individual
Time, in hours
Response, seconds
A dataset containing pharmacodynamic information for response 3.
df_response3df_response3
A data frame with 6 rows and 3 variable:
Individual
Time, in hours
Response, in hours
Build pharmacometric data sets from basic tabulated files
puzzle(directory = NULL, order, coercion = list(name = NULL, sep = ","), optionalcolumns = NULL, pk = list(name = NULL, data = NULL), dose = list(name = NULL, data = NULL), cov = list(name = NULL, data = NULL), pd = list(name = NULL, data = NULL), extratimes = list(name = NULL, data = NULL), nm = list(name = NULL), fillcolumns = NULL, nocoercioncolumns = NULL, norepeatcolumns = NULL, initialindex = 0, na.strings = "N/A", arrange = "ID,TIME,CMT,desc(EVID)", datetimeformat = "%Y-%m-%d %H:%M:%S", timeunits = "hours", timezone = Sys.timezone(), ignore = "C", missingvalues = ".", parallel = TRUE, verbose = FALSE, username = NULL)puzzle(directory = NULL, order, coercion = list(name = NULL, sep = ","), optionalcolumns = NULL, pk = list(name = NULL, data = NULL), dose = list(name = NULL, data = NULL), cov = list(name = NULL, data = NULL), pd = list(name = NULL, data = NULL), extratimes = list(name = NULL, data = NULL), nm = list(name = NULL), fillcolumns = NULL, nocoercioncolumns = NULL, norepeatcolumns = NULL, initialindex = 0, na.strings = "N/A", arrange = "ID,TIME,CMT,desc(EVID)", datetimeformat = "%Y-%m-%d %H:%M:%S", timeunits = "hours", timezone = Sys.timezone(), ignore = "C", missingvalues = ".", parallel = TRUE, verbose = FALSE, username = NULL)
directory |
path to your directory |
order |
define the absorption order, can be 0, 1, c(0,1), or c(1,1) |
coercion |
define name for coercion file |
optionalcolumns |
define optional columns |
pk |
define the required file containing the pk information. It can be a .csv or an .xlsx file |
dose |
define the required file containing the dose information. It can be a .csv, an .xlsx file or an R object. |
cov |
define the optional file containing the covariate information. It can be a .csv, an .xlsx file or an R object. |
pd |
define the optional file containing the pd information. It can be a .csv, or a .xlsx file. |
extratimes |
define the optional file containing the additional times. It can be a .csv, or a .xlsx file. |
nm |
name of output file generated by puzzle |
fillcolumns |
define columns to be filled |
nocoercioncolumns |
define columns to be dropped from the coercion file |
norepeatcolumns |
define columns not to be repeated |
initialindex |
define the lower category of categorical covariates |
na.strings |
define value for na |
arrange |
define how the columns should be arranged |
datetimeformat |
define format for date times |
timeunits |
define time units if needed |
timezone |
define timezone |
ignore |
define ignore value |
missingvalues |
define missing value |
parallel |
define parallel zero + first order absorption |
verbose |
define verbose |
username |
define person performing the assembling |
a pharmacometrics ready data set
## Not run: nm = list(pk = list(parent=as.data.frame(puzzle::df_pk_start)), dose=as.data.frame(puzzle::df_dose_start), cov=as.data.frame(puzzle::df_cov_start)) puzzle(directory=file.path(tempdir()), order=c(0), pk=list(data=nm$pk), dose=list(data=nm$dose), cov=list(data=nm$cov)) ## End(Not run)## Not run: nm = list(pk = list(parent=as.data.frame(puzzle::df_pk_start)), dose=as.data.frame(puzzle::df_dose_start), cov=as.data.frame(puzzle::df_cov_start)) puzzle(directory=file.path(tempdir()), order=c(0), pk=list(data=nm$pk), dose=list(data=nm$dose), cov=list(data=nm$cov)) ## End(Not run)