Package 'aNCA'

Description

Compresses a numeric vector into the simplest possible character string that, when evaluated, will create the same numeric vector.

Usage

.compress_range(range_vector)

Arguments

Value

simplest possible character string representing provided vector

Description

Helper function create text used to filter data frame

Usage

.create_filter_expr(boxplotdata, varvalstofilter)

Arguments

Value

The filter expression

Description

Evaluates range notation. If provided notation is invalid, returns NA.

Usage

.eval_range(x)

Arguments

Value

numeric vector with specified range of numbers, NA if notation is invalid

Description

Tooltip construction helper

Usage

.handle_tooltips(data, tooltip_vars, labels_df)

Arguments

Value

data with added tooltip_text column

Description

This function adds exclusion reasons to the exclude column of the concentration object within a PKNCAdata object, based on a list of reasons and row indices.

Usage

add_exclusion_reasons(pknca_data, exclusion_list)

Arguments

Value

The modified PKNCAdata object with updated exclusion reasons, PKSUMXF, and PKSUM1RS in the concentration object.

Description

Helper function to add "label" attribute to columns based on parameter names.

Usage

add_label_attribute(df, myres)

Arguments

Value

Data frame with "label" attributes added to specified columns.

Examples

# 1. Setup example data
df <- data.frame(a = c(5.2, 6.3), b = c(50.1, 60.2))
names(df) <- c("CMAX[ng/mL]", "AUCLST[hr*ng/mL]")

# 2. Setup mock metadata (myres)
myres <- list(
  result = data.frame(
    PPTESTCD = c("CMAX", "AUCLST"),
    PPSTRESU = c("ng/mL", "hr*ng/mL"),
    type_interval = "main",
    start = 0,
    end = 24
  )
)

# 3. Apply labels
labeled_df <- add_label_attribute(df, myres)

# 4. Check label
attr(labeled_df[["CMAX[ng/mL]"]], "label")

Description

Add a slide with a plot only

Usage

add_pptx_sl_plot(pptx, plot)

Arguments

Value

rpptx object with slide added

Description

Add a slide with both a plot and a table

Usage

add_pptx_sl_plottable(pptx, df, plot)

Arguments

Value

rpptx object with slide added

Description

Add a slide with a table only

Usage

add_pptx_sl_table(
  pptx,
  df,
  title = "",
  subtitle = "",
  footer = "Click here for individual results"
)

Arguments

Value

rpptx object with slide added

Description

Used internally to format a plot chunk for Quarto documents.

Usage

add_qmd_plot(plot_expr, use_plotly = FALSE)

Arguments

Value

Character vector for Quarto code chunk.

Description

Used internally for dose escalation reporting.

Usage

add_qmd_sl_plot(quarto_path, plot, use_plotly = FALSE)

Arguments

Value

Invisibly returns TRUE if the slide was added.

Description

Used internally for dose escalation reporting.

Usage

add_qmd_sl_plottabletable(quarto_path, df1, df2, plot, use_plotly = FALSE)

Arguments

Value

Invisibly returns TRUE if the slide was added.

Description

Used internally to format a table chunk for Quarto documents.

Usage

add_qmd_table(table_expr)

Arguments

Value

Character vector for Quarto code chunk.

Description

Prepends a new version entry so it becomes the current version. The previous current entry moves to the previous list.

Usage

add_settings_version(versions, new_version)

Arguments

Value

Updated list of version entries with the new entry first.

Description

Helper: adjust class and length (optional) for a data.frame based on metadata_nca_variables

Usage

adjust_class_and_length(df, metadata, adjust_length = TRUE)

Arguments

Details

Adjusts the class and length of each variable in the provided data frame according to the specifications in the metadata data frame. Only adjusts the length if adjust_length = TRUE

Value

Adjusted data frame.

Description

A pharmacokinetic concentration-time dataset containing multiple dose administration data with specimen types (SERUM and URINE), dosing information, and subject demographics. This is an example dataset for demonstrating pharmacokinetic analysis workflows. It follows the ADaMIG-ADNCA v1.0 structure.

Usage

adnca_example

Format

A data frame with 76 rows and 32 variables:

STUDYID

Character. Study identifier.

USUBJID

Character. Unique subject identifier.

PCSPEC

Character. Specimen type (SERUM or URINE).

PARAM

Character. Parameter name (e.g., DrugA).

METABFL

Character. Metabolite flag indicator.

AFRLT

Numeric. Actual relative time to first dose (hours).

NFRLT

Numeric. Nominal relative time to first dose (hours).

ARRLT

Numeric. Actual relative time to reference dose (hours).

NRRLT

Numeric. Nominal relative time to reference dose (hours).

TRTRINT

Numeric. Treatment interval in hours.

RRLTU

Character. Relative reference time units.

AVAL

Numeric. Actual analysis value (concentration).

AVALU

Character. Analysis value units (ug/mL or mg/mL).

VOLUME

Numeric. Volume of specimen collected (mL for urine).

VOLUMEU

Character. Volume units.

DOSETRT

Character. Dose treatment identifier.

TRT01A

Character. Actual treatment received (formatted dose and route).

DOSEA

Numeric. Actual dose amount (mg).

DOSEU

Character. Dose units.

ROUTE

Character. Route of administration.

ADOSEDUR

Numeric. Actual dose duration (hours).

ATPTREF

Character. Analysis time point reference (e.g., DOSE 1).

AGE

Numeric. Subject age.

AGEU

Character. Age units.

RACE

Character. Race/ethnicity of subject.

SEX

Character. Sex of subject.

NCA1XRS

Character. NCA result code 1 (if applicable).

NCA2XRS

Character. NCA result code 2 (if applicable).

NEFRLT

Numeric. Nominal relative time to first dose (alternative column).

NERRLT

Numeric. Nominal relative time to reference dose (alternative column).

AEFRLT

Numeric. Actual relative time to first dose (alternative column).

AERRLT

Numeric. Actual relative time to reference dose (alternative column).

DOSFRM

Character. Dose formulation (e.g., "TABLET").

Details

This dataset contains pharmacokinetic data from multiple subjects receiving multiple dose regimens with different levels and routes of administration at 24-hour intervals. Concentration measurements are collected at multiple time points post-dose for both serum and urine samples. Analytes include parent drug and a metabolite.

Source

Example data created for package demonstration and testing purposes.

Description

This function applies a set of filters to a dataset. Each filter specifies a column, condition, and value to filter the dataset.

Usage

apply_filters(data, filters)

Arguments

Details

The function iterates over the list of filters and applies each filter to the dataset. The supported conditions are ==, >, <, >=, <= and !=.

Value

A data frame containing the filtered data.

Examples

# Example usage:
data <- mtcars
filters <- list(
  list(column = "mpg", condition = ">", value = "20"),
  list(column = "cyl", condition = "==", value = "6")
)
filtered_data <- apply_filters(data, filters)

Description

This function adds "label" attributes to all columns in a dataset

Usage

apply_labels(data, labels_df = metadata_nca_variables, type = "ADNCA")

Arguments

Value

The same dataset with label attributes applied to all columns. If a column is not present in the labels list, it will be assigned the name of the col. If label already exists in the original data, it will be preserved.

Examples

data <- data.frame(USUBJID = c(1, 2, 3), AVAL = c(4, 5, 6))
 labels <- data.frame(
   Variable = c("USUBJID", "AVAL"),
   Label = c("Unique Subject Identifier", "Analysis Value"),
   Dataset = c("ADNCA", "ADNCA")
 )
 data <- apply_labels(data, labels, "ADNCA")
 print(attr(data$USUBJID, "label")) # "Unique Subject Identifier"
 print(attr(data$AVAL, "label"))    # "Analysis Value"

Description

This function takes a dataset and applies user-specified column mappings provided through a Shiny input object. It renames columns based on the selected mappings, handles special cases such as ADOSEDUR, updates units for key variables, applies labels, and removes detected duplicate concentration records.

Usage

apply_mapping(
  dataset,
  mapping,
  desired_order = MAPPING_DESIRED_ORDER,
  silent = TRUE,
  req_mappings = c("USUBJID", "AFRLT", "NFRLT", "ARRLT", "NRRLT", "PCSPEC", "ROUTE",
    "AVAL", "STUDYID", "ATPTREF", "AVALU", "RRLTU", "DOSEU", "PARAM")
)

Arguments

Details

• Validates that all required columns are mapped and no duplicates exist.

• If ADOSEDUR is not mapped, it is assigned a value of 0.

• Removes concentration data duplicates using all columns except ARRLT, NRRLT, and ATPTREF.

Value

A transformed data frame with:

• Renamed columns according to user mappings

• Created columns for mapped non-existent variables (e.g., mg/L in AVALU)

• Created columns for unmapped required variables (e.g., ADOSEDUR)

• Ordered columns as specified

• Labels applied to columns (via apply_labels())

• Concentration duplicates removed based on key identifiers: AFRLT, STUDYID, PCSPEC, DOSETRT, USUBJID, and PARAM

Description

Takes a table of desired target units (with PPTESTCD and PPSTRESU) and merges it with a data-derived units table to fill in PPORRESU and calculate conversion_factor. Parameters not present in the defaults retain their data-derived values.

Usage

apply_unit_defaults(default_units, data_units)

Arguments

Value

A list with two elements:

• units - The complete units table with defaults applied.

• failed - A data frame of rows where conversion factor could not be calculated.

Description

Links the table ratio of the App with the ratio calculations via PKNCA results

Usage

calculate_ratio_app(
  res,
  test_parameter,
  ref_parameter = test_parameter,
  test_group = "(all other levels)",
  ref_group = "PARAM: Analyte01",
  aggregate_subject = "no",
  adjusting_factor = 1,
  custom_pptestcd = NULL
)

Arguments

Value

A data.frame with the calculated ratios for the specified settings.

Description

Calculate Ratios from PKNCA Results

Usage

calculate_ratios(
  data,
  test_parameter,
  ref_parameter = test_parameter,
  match_cols,
  ref_groups,
  test_groups = NULL,
  adjusting_factor = 1,
  custom_pptestcd = NULL
)

Arguments

Value

A data.frame result object with the calculated ratios.

Description

This function calculates various summary statistics for formatted output of PKNCA::pk.nca().

Usage

calculate_summary_stats(data, input_groups = "ATPTREF")

Arguments

Details

The function calculates the following statistics for numeric variables:

• Geometric mean (geomean)

• Geometric coefficient of variation (geocv)

• Arithmetic mean (mean)

• Standard deviation (sd)

• Minimum value (min)

• Maximum value (max)

• Median value (median)

• Count of missing values (count.missing)

• Count (count)

The resulting summary statistics are rounded to three decimal places. If units are different, they are standardized to the group's most frequent first unit.

Value

A data frame with summary statistics for each group and parameter.

Examples

data <- data.frame(
  ATPTREF = c(1, 1, 1, 1, 1, 1),
  PPTESTCD = c("A", "A", "B", "B", "C", "C"),
  PPORRES = c(10, 20, 5, 15, NA, 30),
  PPSTRES = c(10, 20, 5, 15, NA, 30),
  PPORRESU = c("mg/L", "mg/L", "ng/mL", "ng/mL", "µg/L", "µg/L"),
  PPSTRESU = c("mg/L", "mg/L", "ng/mL", "ng/mL", "µg/L", "µg/L")
)
calculate_summary_stats(data)

Description

This function takes a PKNCAresult object and a data.frame specifying ratio calculations.

Usage

calculate_table_ratios(res, ratio_table)

Arguments

Value

The updated PKNCAresult object with added rows in the result data.frame.

Description

Predicate used by the export/Save flow to refuse a save when any error-severity finding is present. warning-severity findings (for example selected custom grouping columns) do not block saving.

Usage

cdisc_validation_blocks_save(findings)

Arguments

Value

TRUE when at least one error-severity finding exists, otherwise FALSE.

Examples

findings <- validate_cdisc_types(list())
cdisc_validation_blocks_save(findings)

Description

Change detection is value-based: a row is considered "changed" when its target unit (PPSTRESU) differs from the original unit (PPORRESU). This replaces the previous default flag, which only tracked edits made through the Units modal and missed automatic changes such as volume simplification.

Usage

changed_units(units)

Arguments

Value

The subset of rows where PPSTRESU differs from PPORRESU. Rows where either unit is NA are excluded to avoid emitting NA-based entries downstream.

Description

This function identifies rows associated with excretion samples (e.g., urine, feces, bile) and adjusts the VOLUME and VOLUMEU columns so that the volume unit matches the denominator unit in the corresponding concentration unit (AVALU). This is necessary for PKNCA calculation of excretion parameters.

Usage

convert_volume_units(
  df,
  avalu = "AVALU",
  volume = "VOLUME",
  volumeu = "VOLUMEU"
)

Arguments

Details

It uses the units package to perform unit-safe conversions. If a direct conversion between volume and the concentration denominator is not possible (e.g., between mass and volume), a fallback conversion is attempted using a neutral density of 1 (target_unit / original_unit). The function modifies only the VOLUME and VOLUMEU columns when necessary and leaves all other data unchanged.

The function:

1. Parses the denominator from AVALU (e.g., "ug/mL" → "mL").

2. Attempts to convert the corresponding VOLUME to that unit.

3. If direct conversion fails, assumes a neutral density of 1 (i.e., ⁠1 unit_target / unit_original⁠) and retries.

4. Leaves units unchanged for non-excreta samples or already-valid combinations.

The function assumes that the AVALU column contains concentration units in the form of "x/y" (e.g., "ug/mL", "mg/g").

Value

A modified data frame with VOLUME and VOLUMEU converted (where necessary) so that multiplying AVAL * VOLUME results in a unit with consistent dimensionality (typically mass or moles). A new column AMOUNTU is created to represent the product of AVALU and VOLUMEU.

Examples

df <- data.frame(
  PCSPEC = c("urine", "feces", "plasma"),
  AVAL = c(100, 5, 70),
  AVALU = c("ug/mL", "mg/g", "ng/mL"),
  VOLUME = c(2, 1.5, 3),
  VOLUMEU = c("L", "mL", "mL"),
  stringsAsFactors = FALSE
)

df_converted <- convert_volume_units(df)

Description

Create METABFL Column Based on Selected Metabolites

Usage

create_metabfl(dataset, metabolites)

Arguments

Value

The input dataset with an additional METABFL column indicating metabolite records ("Y") or non-metabolite records ("").

Description

Create a PowerPoint presentation with dose escalation results, including main and extra figures Adds slides for summary tables, mean plots, line plots, and individual subject results

Usage

create_pptx_dose_slides(res_dose_slides, path, title, template)

Arguments

Value

TRUE (invisible). Writes the PowerPoint file to the specified path

Description

Used internally to initialize a Quarto document for exporting plots and tables.

Usage

create_qmd_doc(
  quarto_path,
  title = "NCA Report",
  libraries = c("plotly", "flextable", "dplyr"),
  rda_path = NULL,
  template = NULL,
  extra_setup = NULL
)

Arguments

Value

Invisibly returns TRUE if the file was created.

Description

Used internally to generate main and individual slides for each dose group.

Usage

create_qmd_dose_slides(res_dose_slides, quarto_path, title, use_plotly = TRUE)

Arguments

Value

Invisibly returns TRUE if slides were created.

Description

Wraps a settings payload with metadata (timestamp, comment, dataset name, aNCA version, active tab) for storage in a versioned settings YAML file.

Usage

create_settings_version(settings_data, comment = "", dataset = "", tab = "")

Arguments

Value

A named list representing one version entry.

Description

Defines an impute column in the intervals of the PKNCAdata object based on data

Usage

create_start_impute(pknca_data)

Arguments

Value

A PKNCAdata object with updated intervals table including start imputation strategies. If the intervals are ambiguous and can refer to multiple concentration groups it will define them to choose the proper imputation for each.

Examples

adnca <- adnca_example
pknca_data <- PKNCA_create_data_object(adnca)
pknca_data <- create_start_impute(pknca_data)

Description

Removes the version at the given index. Cannot delete the last remaining version.

Usage

delete_settings_version(versions, index)

Arguments

Value

Updated list of version entries.

Description

This function detects the type of study based on the provided data.

Usage

detect_study_types(
  data,
  groups,
  metabfl_column,
  route_column,
  volume_column = "volume"
)

Arguments

Details

The function identifies a possible five different types of studies based on grouping by STUDYID, DOSETRT, USUBJID, PCSPEC, and the route column. The study types are determined as follows:

• "Excretion Data": If the volume column for a group is not NA and has values > 0.

• "Single Extravascular Dose": If there is only one dose and TRTRINT is not available, and the route is extravascular.

• "Single IV Infusion Dose": If there is only one dose and TRTRINT is not present, and the route is not extravascular.

• "Single IV Bolus Dose": If there is only one dose and TRTRINT is not present, the route is not extravascular, and ADOSEDUR == 0.

• "Multiple Extravascular Doses": If there are multiple doses (or TRTRINT is available) and the route is extravascular.

• "Multiple IV Infusion Doses": If there are multiple doses (or TRTRINT is available) and the route is not extravascular.

• "Multiple IV Bolus Doses": If there are multiple doses or TRTRINT is not present, the route is not extravascular, and ADOSEDUR == 0.

• If none of these conditions are met, the type is marker as "Unknown".

Value

A data frame with the unique combinations of study types, including the grouping columns and the identified type.

Examples

sample_data <- data.frame(
  STUDYID = "STUDY001",
  DOSETRT = "Drug",
  ANALYTE = "DOSETRT",
  USUBJID = c(
    # 1. Single IV Dose subject
    "Subj-01", "Subj-01",
    # 2. Multiple Extravascular Doses subject (identified by TRTRINT)
    "Subj-02", "Subj-02",
    # 3. Excretion Data subject (identified by positive volume)
    "Subj-03", "Subj-03"
  ),
  PCSPEC = "PLASMA",
  DOSNOA = c(
    1, 1,        # Single dose
    1, 1,        # Appears as single dose...
    1, 1         # Single dose
  ),
  ROUTE = c(
    "INTRAVENOUS", "INTRAVENOUS",
    "extravascular", "extravascular",
    "INTRAVENOUS", "INTRAVENOUS"
  ),
  ADOSEDUR = c(
  0, 0,
  0, 0,
  2, 2),
  SAMPLE_VOLUME = c(
    NA, 0,
    NA, 0,
    10, 12       # Positive volume indicates excretion
  ),
  TRTRINT = c(
    NA, NA,
    24, 24,      # ...but TRTRINT indicates a multiple-dose regimen
    NA, NA
  ),
  METABFL = c(
    "N", "N",
    "N", "N",
    "Y", "Y"    # mark last subject as metabolite
  )
)

study_summary <- detect_study_types(
  data = sample_data,
  groups = c("USUBJID", "PCSPEC", "DOSETRT"),
  metabfl_column = "METABFL",
  route_column = "ROUTE",
  volume_column = "SAMPLE_VOLUME"
)

Description

An SDTM DM-like demographics dataset derived from adnca_example. Contains one row per subject with demographic variables and a synthetic treatment start date (RFXSTDTC) that serves as the reference origin for datetimes in pc_example and ex_example.

Usage

dm_example

Format

A data frame with one row per subject and 9 variables:

DOMAIN

Character. Domain Abbreviation.

STUDYID

Character. Study identifier.

USUBJID

Character. Unique subject identifier.

AGE

Numeric. Age of the subject.

AGEU

Character. Age units (e.g., "Years").

SEX

Character. Sex of the subject.

RACE

Character. Race of the subject.

ARM

Character. Planned treatment arm (mapped from TRT01A).

ACTARM

Character. Actual treatment arm (mapped from TRT01A).

RFXSTDTC

Character. Date/time of first study treatment (ISO 8601). Synthetic, staggered by 3 days per subject to simulate different enrollment dates.

Source

Derived from adnca_example via data-raw/sdtm_example.R.

Description

This function duplicates and adjusts concentration data to ensure all dosing cycles have complete pre-dose and last concentration values. It is designed for use in pharmacokinetic analyses where dosing intervals and concentration values need to be aligned for each dose.

Usage

dose_profile_duplicates(
  conc_data,
  groups = c("USUBJID", "DOSNOA", "PARAM"),
  dosno = "DOSNOA",
  arrlt = "ARRLT",
  afrlt = "AFRLT",
  nrrlt = "NRRLT",
  nfrlt = "NFRLT"
)

Arguments

Value

A data frame with adjusted concentration data, including:

• Duplicated pre-dose values assigned to the previous dose.

• Duplicated last values assigned to the next dose if pre-dose values are missing, filtered so only samples within 4 hours of the next dose are included.

• Sorted by the grouping variables and relative time.

Examples

# Example usage
conc_data <- data.frame(
USUBJID = c("001", "001", "001", "001", "001", "001", "001", "001", "001", "001"),
AVAL = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
DOSNOA = c(1, 1, 1, 2, 2, 2, 2, 3, 3, 3),
ARRLT = c(-1, 0, 1, -1, 0, 1, 2, 0, 1, 2),
AFRLT = c(-1, 0, 1, 2, 3, 4, 5, 6, 7, 8),
NRRLT = c(-1, 0, 1, -1, 0, 1, 2, 0, 1, 2),
NFRLT = c(-1, 0, 1, 2, 3, 4, 5, 6, 7, 8)
)
result <- dose_profile_duplicates(conc_data,
                            groups = c("USUBJID", "DOSNOA"), dosno = "DOSNOA")

Description

An SDTM EX-like exposure/dosing dataset derived from adnca_example. Contains one row per dosing event per subject. Intended to support and test the PC+EX upload workflow (see GitHub issue #624).

Usage

ex_example

Format

A data frame with 11 variables:

DOMAIN

Character. Domain abbreviation.

STUDYID

Character. Study identifier.

USUBJID

Character. Unique subject identifier.

EXTRT

Character. Name of the treatment (mapped from DOSETRT).

EXDOSE

Numeric. Dose amount (mapped from DOSEA).

EXDOSU

Character. Dose units (mapped from DOSEU).

EXROUTE

Character. Route of administration (mapped from ROUTE).

EXSTDTC

Character. Dosing start datetime (ISO 8601), synthesised from AFRLT - ARRLT using the subject's RFXSTDTC as reference origin.

EXENDTC

Character. Dosing end datetime (ISO 8601), computed as EXSTDTC + ADOSEDUR.

EXDUR

Character. Duration of dose in ISO 8601 duration format (e.g., "PT2.9H"), derived from ADOSEDUR.

EXELTM

Character. Planned elapsed time from first dose in ISO 8601 duration format (e.g., "PT0H"), derived from NFRLT - NRRLT relative to the subject's first dose.

Source

Derived from adnca_example and dm_example via data-raw/sdtm_example.R.

Description

Generates a line plot for individual pharmacokinetic (PK) concentration-time profiles

Usage

exploration_individualplot(
  pknca_data,
  color_by,
  facet_by = NULL,
  show_facet_n = FALSE,
  ylog_scale = FALSE,
  lock_y_axis = FALSE,
  threshold_value = NULL,
  x_limits = NULL,
  y_limits = NULL,
  show_dose = FALSE,
  palette = "default",
  tooltip_vars = NULL,
  labels_df = NULL,
  filtering_list = NULL,
  use_time_since_last_dose = FALSE,
  show_legend = TRUE,
  line_type = "default"
)

Arguments

Value

A ggplot object representing the individual PK line plot.

Description

Generates a line plot for mean pharmacokinetic (PK) concentration-time profiles

Usage

exploration_meanplot(
  pknca_data,
  color_by,
  facet_by = NULL,
  show_facet_n = FALSE,
  ylog_scale = FALSE,
  lock_y_axis = FALSE,
  show_legend = TRUE,
  threshold_value = NULL,
  show_dose = FALSE,
  palette = "default",
  sd_min = FALSE,
  sd_max = FALSE,
  ci = FALSE,
  tooltip_vars = NULL,
  labels_df = NULL,
  filtering_list = NULL,
  use_time_since_last_dose = FALSE,
  x_limits = NULL,
  y_limits = NULL,
  line_type = "default"
)

Arguments

Value

A ggplot object representing the mean PK line plot, with error bars and/or confidence intervals if requested.

Description

This function processes the results from a PKNCA and exports them into CDISC compliant datasets. Attention: All parameters that do no match pptest dataframe will be lost in this pipeline!

Usage

export_cdisc(res_nca, grouping_vars = character(0), flag_rules = NULL)

Arguments

Details

Outputs are the following:

• pknca_result Output from function call pk.nca() (formatted)

• pknca_result_raw Output from function call pk.nca() (needs to be merged with upper later on but now we avoid merge conflict)

Value

A list with two data frames:

pp

A data frame containing the PP (Pharmacokinetic Parameters) domain data.

adpp

A data frame containing the ADPP (Analysis Dataset for Pharmacokinetic Parameters) domain data.

Description

Predicate used by the export/Save flow to refuse a save when any error-severity finding is present in the output of validate_export_outputs().

Usage

export_validation_blocks_save(findings)

Arguments

Value

TRUE when at least one error-severity finding exists, otherwise FALSE.

Examples

findings <- validate_export_outputs(list())
export_validation_blocks_save(findings)

Description

Filters an axis for consecutive breaks that are far enough apart to be drawn without their labels colliding. A break is kept when its distance from the last kept break is at least min_cm_distance and at least the space the two labels occupy along the axis, so a wide label such as 119.917 thins the axis further than a short one such as 12 does.

Usage

filter_breaks(
  breaks = NA,
  plot = plot,
  min_cm_distance = 0.5,
  axis = "x",
  labels = format_axis_labels
)

Arguments

Details

Candidates outside the plotted range are dropped before any of that, so a break that is never drawn cannot decide which of the visible ones survive.

Value

A numeric vector of filtered x-axis breaks.

Author(s)

Gerardo Rodriguez

Description

This function generates a violin or box plot based on the provided data, parameter, and dose information.

Usage

flexible_violinboxplot(
  res_nca,
  parameter,
  xvars,
  colorvars,
  varvalstofilter = NULL,
  tooltip_vars,
  labels_df = metadata_nca_variables,
  box = TRUE,
  plotly = TRUE,
  seed = NULL,
  show_excluded = FALSE
)

Arguments

Value

A plotly object representing the violin or box plot.

Description

This function creates a pharmacokinetic concentration dataset from the provided ADNCA data.

Usage

format_pkncaconc_data(
  ADNCA,
  group_columns,
  time_column = "AFRLT",
  rrlt_column = "ARRLT",
  route_column = "ROUTE",
  time_end_column = NULL,
  nca_exclude_reason_columns = NULL,
  dose_group_columns
)

Arguments

Details

The function performs the following steps:

• Checks for required columns and data.

• Filters out rows with EVID = 0 and PARAMCD containing "DOSE" (removes dosing data; not CDISC standard).

• Optionally creates CONCDUR if time_end_column is provided.

• Optionally creates nca_exclude by merging specified exclusion reason columns.

• Creates DOSNOA variable: sequential numbers based on time of dose within each group.

• Creates a std_route column with PKNCA values "intravascular" or "extravascular" based on the route column (ROUTE, CDISC: C66729).

• Arranges the data by group_columns and dose time.

Value

A data frame containing the filtered and processed concentration data. The following new columns are created:

• DOSNOA: Sequential dose number within each group, based on time of dose.

• std_route: Standardized route, either "intravascular" or "extravascular", derived from the route column.

• nca_exclude: Character column with concatenated exclusion reasons (if nca_exclude_reason_columns is provided), otherwise empty string.

• CONCDUR: (Optional) Sampling duration, calculated as time_end_column - time_column if time_end_column is provided.

Examples

adnca <- adnca_example
conc_data <- format_pkncaconc_data(
  ADNCA = adnca,
  group_columns = c("STUDYID", "DOSETRT", "USUBJID", "PARAM"),
  time_column = "AFRLT",
  rrlt_column = "ARRLT",
  route_column = "ROUTE",
  dose_group_columns = c("STUDYID", "DOSETRT", "USUBJID")
)

Description

This function creates a dataset with dose intervals and specified pharmacokinetic parameters.

Usage

format_pkncadata_intervals(
  pknca_conc,
  pknca_dose,
  start_from_last_dose = TRUE,
  keep_interval_cols = NULL
)

Arguments

Details

The function performs the following steps:

• Creates a vector with all pharmacokinetic parameters.

• Based on dose times, creates a data frame with start and end times.

• If TRTRINT column is present in data, if TRTRINT is NA then it sets end time to Inf if only one dose present, or max end time if not.

• If no TRTRINT column in data, sets last dose end time to the time of last sample or Inf if single dose data.

• Adds logical columns for each specified parameter.

Assumes that multiple dose data will have a TRTRINT column or contain multiple doses in dataset

Value

A data frame containing the dose intervals and specified pharmacokinetic parameters.

Examples

adnca <- adnca_example
pknca_data <- PKNCA_create_data_object(adnca)
pknca_conc <- pknca_data$conc
pknca_dose <- pknca_data$dose
dose_intervals <- format_pkncadata_intervals(pknca_conc, pknca_dose)

Description

This function creates a pharmacokinetic dose dataset from the provided concentration data.

Usage

format_pkncadose_data(
  pkncaconc_data,
  time_column = "AFRLT",
  rrlt_column = "ARRLT",
  group_columns
)

Arguments

Details

The function performs the following steps:

• Arranges and groups the data by group_columns

• Selects the first row within each group (arranged by DOSNOA- a variable created in format_pkncaconc_data)

Note*: This function is designed to work with the output of format_pkncaconc_data.

Value

A data frame containing the dose data.

Description

Formats a unit string if a unique unit exists

Usage

format_unit_string(data, unit_var)

Arguments

Value

A formatted string like " (hr)" or an empty string "".

Description

This function creates a ggplot2 line plot for pharmacokinetic (PK) data. The function supports various customizations including log scales, faceting and threshold lines.

Usage

g_lineplot(
  data,
  x_var,
  y_var,
  x_unit = NULL,
  y_unit = NULL,
  color_by,
  color_labels = NULL,
  facet_by = NULL,
  group_by = NULL,
  facet_count_n = NULL,
  x_limits = NULL,
  y_limits = NULL,
  ylog_scale = FALSE,
  lock_y_axis = FALSE,
  threshold_value = NULL,
  palette = "default",
  tooltip_vars = NULL,
  labels_df = NULL,
  vline_var = NULL,
  linetype_by = NULL,
  show_legend = TRUE
)

Arguments

Value

A ggplot object representing the line plot.

Examples

library(dplyr)
ind_data <- expand.grid(
  time_var = c(0, 1, 2, 4, 8, 12),
  USUBJID = c("Subject1", "Subject2")
) %>%
  mutate(
    AVAL = ifelse(USUBJID == "Subject1", 50, 80) * exp(-0.5 * time_var) + rnorm(n(), 0, 1),
    PARAM = "Analyte1",
    DOSEA = "Dose 1",
    RRLTU = "hours",
    AVALU = "ng/mL"
  )

p <- g_lineplot(
  data = ind_data,
  x_var = "time_var",
  y_var = "AVAL",
  color_by = "USUBJID"
)
print(p)

Description

Wrapper around aNCA::pkcg01() function. Calls the function with LIN scale argument.

Usage

g_pkcg01_lin(data, ...)

Arguments

Value

ggplot2 object for pkcg01.

Description

Wrapper around aNCA::pkcg01() function. Calls the function with LOG scale argument.

Usage

g_pkcg01_log(data, ...)

Arguments

Value

ggplot2 object for pkcg01.

Description

Wrapper around aNCA::pkcg01() function. Calls the function with SBS scale argument.

Usage

g_pkcg01_sbs(data, ...)

Arguments

Value

ggplot2 object for pkcg01.

Description

Wrapper around aNCA::pkcg02() function. Calls the function with LIN scale argument.

Usage

g_pkcg02_lin(data, ...)

Arguments

Value

ggplot2 object for pkcg02.

Description

Wrapper around aNCA::pkcg02() function. Calls the function with LOG scale argument.

Usage

g_pkcg02_log(data, ...)

Arguments

Value

ggplot2 object for pkcg02.

Description

Wrapper around aNCA::pkcg02() function. Calls the function with SBS scale argument.

Usage

g_pkcg02_sbs(data, ...)

Arguments

Value

ggplot2 object for pkcg02.

Description

Wrapper around aNCA::pkcg03() function. Calls the function with LIN scale argument.

Usage

g_pkcg03_lin(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Wrapper around aNCA::pkcg03() function. Calls the function with LOG scale argument.

Usage

g_pkcg03_log(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Wrapper around aNCA::pkcg03() function. Calls the function with SBS scale argument.

Usage

g_pkcg03_sbs(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Extracts variable-level metadata from metadata_nca_variables for the requested CDISC datasets (ADNCA, ADPP, PP). When cdisc_data is provided, specs are narrowed to only the variables present in each dataset.

Usage

generate_pre_specs(datasets = c("ADNCA", "ADPP", "PP"), cdisc_data = NULL)

Arguments

Value

A named list of data frames, one per dataset, each containing columns: Dataset, Order, Variable, Label, Type, Role, Core, Length.

Description

Generate HTML Tooltip Text

Usage

generate_tooltip_text(data, labels_df, tooltip_vars, type)

Arguments

Details

Creates a character vector of HTML tooltips for each row of a data frame, suitable for use with ggplotly. The output vector of this function should be added to original plotting data as a column, which then can be used as tooltip argument in the plotting function.

Value

A character vector of formatted HTML tooltip strings.

Examples

# Sample data
my_data <- data.frame(
  USUBJID = c("Subject-01", "Subject-02"),
  DOSE = c(100, 200),
  RESPONSE = c(5.4, 8.1)
  )

my_labels <- data.frame(
  Dataset = "ADNCA",
  Variable = "USUBJID",
  Label = "Unique Subject ID"
  ) # Dummy labels object

vars_to_show <- c("USUBJID", "DOSE", "RESPONSE")

# Generate the tooltip text vector
tooltips <- generate_tooltip_text(my_data, my_labels, vars_to_show, "ADNCA")
my_data$tooltip <- tooltips

Description

This function transforms a value from an initial unit to a target unit.

Usage

get_conversion_factor(initial_unit, target_unit)

Arguments

Value

A numeric value for the conversion factor from the initial to the target unit, or NA if the units are not convertible.

Examples

get_conversion_factor("meter", "kilometer")
get_conversion_factor("sec", "min")

Description

Generates a plotly plot for NCA half-life visualization, with a fit line and scatter points.

Usage

get_halflife_plots(pknca_data, add_annotations = TRUE, title_vars = NULL)

Arguments

Value

A list with plotly objects and data

Description

This function retrieves the label of a heading from a labels file.

Usage

get_label(variable, type = "ADNCA", labels_df = metadata_nca_variables)

Arguments

Value

The label of the heading if it exists in the labels file, otherwise the variable name.

Examples

LABELS <- data.frame(
   Variable = c("USUBJID", "AVAL"),
   Label = c("Unique Subject Identifier", "Analysis Value"),
   Dataset = c("ADNCA", "ADNCA")
 )
 get_label("USUBJID", "ADNCA", LABELS)  # Returns "Unique Subject Identifier"
 get_label("AGE", "ADNCA", LABELS)  # Returns "AGE"

Description

Reads a settings YAML file and generates an R script that can reproduce the session. Mapping, filters, ratio table, and units are all read from the YAML.

Usage

get_settings_code(
  settings_file_path,
  data_path,
  output_path = "settings_code.R",
  template_path = system.file("www/templates/script_template.R", package = "aNCA")
)

Arguments

Value

Invisibly returns the output_path.

Description

Add specified imputation methods to the intervals in a PKNCAdata or data.frame object.

Usage

interval_add_impute(
  data,
  target_impute,
  after,
  target_params,
  target_groups,
  ...
)

Arguments

Details

If already present the target_impute method will be added substituting the existing one. All new intervals created will be added right after their original ones.

Value

A modified PKNCAdata object with specified imputation methods on the target intervals.

Examples

d_conc <- data.frame(
  conc = c(1, 0.6, 0.2, 0.1, 0.9, 0.4, 1.2, 0.8, 0.3, 0.2, 1.1, 0.5),
  time = rep(0:5, 2),
  ID = rep(1:2, each = 6),
  param = rep(c("Analyte1", "Analyte2"), each = 6)
)

d_dose <- data.frame(
  dose = c(100, 200),
  time = c(0, 0),
  ID = c(1, 2)
)

o_conc <- PKNCA::PKNCAconc(d_conc, conc ~ time | ID / param)
o_dose <- PKNCA::PKNCAdose(d_dose, dose ~ time | ID)

intervals <- data.frame(
  start = c(0, 0, 0),
  end = c(3, 5, Inf),
  half.life = c(TRUE, TRUE, TRUE),
  cmax = c(TRUE, TRUE, TRUE),
  impute = c("start_conc0,start_predose", "start_predose", "start_conc0"),
  param = c("Analyte1", "Analyte2", "Analyte1")
)

o_data <- PKNCA::PKNCAdata(o_conc, o_dose, intervals = intervals)

# Apply interval_add_impute function
o_data <- interval_add_impute(o_data,
                              target_impute = "start_conc0",
                              target_params = "half.life",
                              target_groups = data.frame(param = "Analyte1"))

Description

Remove specified imputation from the intervals in a PKNCAdata or data.frame (intervals) object.

Usage

interval_remove_impute(data, target_impute, ...)

Arguments

Value

A modified object with the specified imputations removed from the targeted intervals.

Examples

d_conc <- data.frame(
  conc = c(1, 0.6, 0.2, 0.1, 0.9, 0.4, 1.2, 0.8, 0.3, 0.2, 1.1, 0.5),
  time = rep(0:5, 2),
  ID = rep(1:2, each = 6),
  param = rep(c("Analyte1", "Analyte2"), each = 6)
)

d_dose <- data.frame(
  dose = c(100, 200),
  time = c(0, 0),
  ID = c(1, 2)
)

o_conc <- PKNCA::PKNCAconc(d_conc, conc ~ time | ID / param)
o_dose <- PKNCA::PKNCAdose(d_dose, dose ~ time | ID)

intervals <- data.frame(
  start = c(0, 0, 0),
  end = c(3, 5, Inf),
  half.life = c(TRUE, FALSE, TRUE),
  cmax = c(TRUE, TRUE, TRUE),
  impute = c("start_conc0,start_predose", "start_predose", "start_conc0"),
  param = c("Analyte1", "Analyte2", "Analyte1")
)

o_data <- PKNCA::PKNCAdata(o_conc, o_dose, intervals = intervals)

# Apply interval_remove_impute function
o_data <- interval_remove_impute(data = o_data,
                                 target_impute = "start_conc0",
                                 target_params = "half.life",
                                 target_groups = data.frame(param = "Analyte1"))

Description

Helper function for BLQ processing

Usage

keep_blq_timepoints(plot_data, xvar, mean_group_var)

Arguments

Value

A data frame with valid timepoints based on BLQ criteria.

Description

This function creates a listing of pharmacokinetic (PK) concentration data segregating a dataset in lists that are customizable in title, footnotes, grouping/displayed variables, missing/zero values and/or number of digits displayed.

Usage

l_pkcl01(
  data,
  listgroup_vars = c("PARAM", "PCSPEC", "ROUTE"),
  grouping_vars = c("TRT01A", "USUBJID", "ATPTREF"),
  displaying_vars = c("NFRLT", "AFRLT", "AVAL"),
  formatting_vars_table = NULL,
  title = paste0("Listing of PK Concentration by Treatment Group,",
    "Subject and Nominal Time, PK Population"),
  subtitle = NULL,
  footnote = "*: Subjects excluded from the summary table and mean plots"
)

Arguments

Details

The function performs the following steps:

• Groups the data based on the specified grouping variables.

• Formats the 0 and NA values as defined by the formatting table.

• Creates a listing for each unique combination of the grouping variables.

The formatting_vars_table should be a data frame with the following columns:

• var_name: The name of the variable.

• Label: The label for the variable.

• na_str: The string to use for NA values.

• zero_str: The string to use for 0 values.

• align: The alignment for the variable (e.g., "center").

• format_fun: The formatting function to use ("round" or "signif").

• digits: The number of digits to use for numeric formatting.

Value

A list of listings, each corresponding to a unique combination of the grouping variables.

Author(s)

Gerardo Rodriguez

Examples

# Create a sample dataframe 'data' with the required variables
  set.seed(123)
  data <- data.frame(
    PARAM = rep(c("Param1", "Param2"), each = 6),
    PCSPEC = rep(c("Blood", "Urine"), each = 6),
    TRT01A = rep(c("Treatment1", "Treatment2"), each = 6),
    USUBJID = rep(c(rep(1, 3), rep(2, 3)), 2),
    NFRLT = rep(1:3, 4),
    AFRLT = rep(1:3, 4) + runif(12, 0, 0.5),
    TIMEU = "hours",
    AVAL = rep(0:2, 4) + runif(12, 0, 0.5),
    AVALU = "mg/L"
  )

  # Define the formatting table
  formatting_vars_table <- data.frame(var_name = names(data),
                                      Label = c("Parameter", "Specimen", "Treatment Arm",
                                                "Unique Subject ID", "Norminal Time ($TIMEU)",
                                                "Actual Time ($TIMEU)", "Time Unit",
                                                "Analyte Value ($AVALU)", "Analyte Unit"),
                                      na_str = "Missing",
                                      zero_str = c(rep("0", 7), "BLQ", "0"),
                                      align = "center",
                                      format_fun = c(NA, NA, NA, NA,
                                                     "round", "round", NA, "round", NA),
                                      digits = c(NA, NA, NA, NA, 2, 2, NA, 3, NA))

  # Call the l_pkcl01 function with the sample data
  listing_ex <- l_pkcl01(data = data,
                         listgroup_vars = c("PARAM", "PCSPEC"),
                         grouping_vars = c("TRT01A", "USUBJID"),
                         displaying_vars = c("NFRLT", "AFRLT", "AVAL"),
                         formatting_vars_table = formatting_vars_table,
                         title = "Listing of PK Concentration",
                         subtitle = "Subjects with !PARAM: $PARAM (!PCSPEC: $PCSPEC)"
                         )
  print(listing_ex)

Description

Wrapper around aNCA::l_pkcl01() for TAD-based concentration listings.

Usage

l_pkcl01_tad(data, ...)

Arguments

Value

A named list of listing_df objects.

Description

Filters ADNCA to urine specimen rows (where PCSPEC %in% urine_specs) then delegates to l_pkcl01() with VOLUME and VOLUMEU added to the displayed columns when those columns are present in the data.

Usage

l_pkcl02_uri(
  data,
  urine_specs = c("URINE"),
  listgroup_vars = c("PARAM", "PCSPEC"),
  displaying_vars = NULL,
  ...
)

Arguments

Value

A named list of listing_df objects.

Examples

## Not run: 
adnca <- export_cdisc(res_nca)$adnca
listings <- l_pkcl02_uri(adnca)
print(listings[[1]])

## End(Not run)

Description

Creates individual-level listings of PK parameters from ADPP data using the same engine as l_pkcl01(). Returns one listing per unique combination of listgroup_vars (default: PPCAT x PPSPEC).

Usage

l_pkpl01(
  data,
  listgroup_vars = c("PPCAT", "PPSPEC"),
  grouping_vars = c("TRT01A", "USUBJID"),
  param_var = "PARAM",
  value_var = "AVAL",
  unit_var = "AVALU",
  title = "Listing of Individual PK Parameters",
  subtitle = NULL,
  footnote = NULL
)

l_pkpl01_mp(data, ...)

Arguments

Value

A named list of listing_df objects (one per listgroup_vars combination), suitable for printing in a Shiny verbatimTextOutput.

Functions

• l_pkpl01_mp(): Listing filtered to metabolite rows (pkpl01 M/P). Uses the same METABFL -> PPCAT -> PARAM fallback as t_pkpt03_MP_col().

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
listings <- l_pkpl01(adpp)
print(listings[[1]])

## End(Not run)

Description

Produces a per-subject listing of individual PK parameter values organised for treatment comparison. Each listing page covers one PPCAT/PPSPEC combination. PK parameters become display columns (one column per PARAM value) and the rows are keyed by TRT01A and USUBJID.

Usage

l_pkpl04_mp(
  data,
  grouping_vars = c("PARAM", "TRT01A", "USUBJID"),
  title = "Listing of Individual PK Parameter Values by Treatment",
  ...
)

Arguments

Details

This listing shows the raw individual AVAL values from ADPP, not pre-computed ratios. If your ADPP contains NCA ratio parameters (e.g. metabolite-to-parent AUC ratios added via the aNCA ratio-calculation module), those parameters are displayed here just like any other PARAM row. The ⁠_mp⁠ suffix in the function name reflects its typical use with metabolite/parent ratio parameters, but no metabolite filtering is applied – all PARAM values in the data are included.

Note: PARAM is listed in grouping_vars so that it participates in the pivot_wider step (each unique PARAM value becomes a column header). After pivoting, PARAM is no longer a row-key column; the actual listing keys are TRT01A and USUBJID.

Value

A named list of listing_df objects.

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
listings <- l_pkpl04_mp(adpp)
print(listings[[1]])

## End(Not run)

Description

A dataset containing the mapping between PKNCA terms and CDISC terms. It mainly involves:

• Parameters: PPTEST, PPTESTCD

Usage

metadata_nca_parameters

Format

A data frame with 123 rows and 6 variables:

PKNCA

PKNCA term

PPANMETH

PPANMETH term, specifying when relevant the calculation method for PPTESTCD

PPTESTCD

CDISC term

PPTEST

Official CDISC term

input_names

Combination of PPTESTCD + ": " + PPTEST. Used for App inputs

FUN

PKNCA function used to calculate the parameter

description

PKNCA description of the term

is_cdisc_sure

Logical indicating if the term is a CDISC official name

unit_type

Type of unit associated with the term

TYPE

Type of data associated with the parameter/term

CAT

Arbitrary assigned subclass for the parameter/term

Depends

PKNCA derived. Designates all directly used parameters in calculation

can_excretion

Logical. Indicates if the parameter can be used in excretion analysis

can_non_excretion

Logical. Indicates if the parameter can be used in non-excretion analysis

can_single_dose

Logical. Indicates if the parameter can be used in single dose analysis

can_multiple_dose

Logical. Indicates if the parameter can be used in multiple dose analysis

can_extravascular

Logical. Indicates if the parameter can be used in extravascular analysis

can_metabolite

Logical. Indicates if the parameter can be used in metabolite analysis

show_in_matrix_selection

Logical. Indicates if the parameter will be shown for general interval calculations

Source

Generated for use in the translate_nomenclature function.

Description

A dataset containing pharmacokinetic variable specifications.

Usage

metadata_nca_variables

Format

A data frame with 366 rows and 19 variables:

Dataset

Character. Indicates the dataset the variable belongs to (PP, ADNCA, ADPP).

Order

Numeric. Variable order within its domain, based on Role, Core and Variable

Variable

Character. The short name of the variable.

Label

Character. A descriptive label for the variable.

Type

Character. Data type of the variable (Char, Num, text, integer, float, dateTime).

Role

Character. The CDISC role of the variable (e.g., Identifier, Topic, Timing...).

Core

Character. Indicates the core status of the variable (Req = Required, Perm = Permissible, Exp = Expected, Cond = Conditional).

company_specific

Logical. Indicates if the variable is company-specific (not CDISC).

is.core

Logical. TRUE if the variable is a core variable (always needed to be present).

Length

Numeric. The maximum length of the variable.

Controlled_Terms

Character. Reference to controlled terminology (e.g., C85839, C66731).

is.used

Logical. TRUE if the variable is meant to be included.

Values

Character. Possible values (if applicable) for the variable separated by ', '.

is.mapped

Logical. TRUE if the variable is mapped in ADNCA (App's input).

mapping_tooltip

Character. Tooltip text for mapping guidance in the App.

mapping_section

Character. Mapping section where the variable is classified in the App.

mapping_alternatives

Character. Alternative column names for the variable.

mapping_order

Numeric. Defines the mapped variables order in the mapped dataset

allow_create_numeric

Logical. Indicates if the mapping input should allow the user to introduce custom numbers

Source

Used for PP and ADPP mapping rules and checks in the export_cdisc function

Description

The ratios are calculated as specimen1 / specimen 2.

Usage

multiple_matrix_ratios(
  data,
  matrix_col,
  conc_col,
  units_col,
  groups = c("NFRLT", "USUBJID"),
  spec1,
  spec2
)

Arguments

Value

A data frame containing the ratios.

Examples

data <- data.frame(
  USUBJID = c("A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"),
  NFRLT = c(0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2),
  MATRIX = c(
    "BLOOD", "BLOOD", "BLOOD", "PLASMA", "PLASMA", "PLASMA",
    "BRAIN", "BRAIN", "BRAIN", "LIVER", "LIVER", "LIVER"
  ),
  CONC = c(10, 20, 15, 25, 30, 40, 5, 10, 8, 12, 18, 16),
  UNITS = rep("ng/mL", 12)
)
multiple_matrix_ratios(data, "MATRIX", "CONC", "UNITS", c("NFRLT", "USUBJID"), "BLOOD", "PLASMA")

Description

Wrapper around aNCA::pkcg03() function. Mean linear plot grouped by dose.

Usage

p_pkcg03_lin_dose(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Wrapper around aNCA::pkcg03() function. Mean log plot grouped by dose.

Usage

p_pkcg03_log_dose(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Wrapper around aNCA::pkcg03() function. Mean side-by-side plot grouped by dose.

Usage

p_pkcg03_sbs_dose(data, ...)

Arguments

Value

ggplot2 object for pkcg03.

Description

Computes mean (+/- SD) of a urine PK parameter per treatment arm across collection intervals and draws a connected line plot. Designed for ADPP data filtered to PPSPEC %in% urine_specs (e.g. cumulative amount excreted or fraction of dose recovered). Returns one ggplot per unique combination of list_vars.

Usage

p_pkpg01_cum(
  data,
  strat_var = "TRT01A",
  param_var = "PARAM",
  value_var = "AVAL",
  urine_specs = c("URINE"),
  paramcd_filter = "RCAMINT",
  time_end_var = "PPENINT",
  list_vars = c("PPCAT"),
  title = "Mean Cumulative Amount Recovered in Urine",
  subtitle = NULL,
  footnote = NULL,
  xlab = NULL,
  ylab = NULL
)

p_pkpg01_per(
  data,
  paramcd_filter = "FREXINT",
  title = "Mean Percentage of Dose Recovered in Urine",
  ylab = "Percent Dose Recovered (%)",
  ...
)

Arguments

Value

A named list of ggplot objects.

Functions

• p_pkpg01_per(): Mean percentage of dose recovered in urine (pkpg01 %). Identical to p_pkpg01_cum() but defaults to a % dose recovered title and y-axis label.

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
plots <- p_pkpg01_cum(adpp)
plots[[1]]

## End(Not run)

Description

Plots individual AVAL values against dose (DOSEA) on a log-log scale with one facet per PK parameter. A power-model regression line (log y = a + b * log x) is overlaid on each facet together with the slope estimate and its confidence interval, enabling visual assessment of dose-proportionality (slope b = 1).

Usage

p_pkpg02_doseprop(
  data,
  dose_var = "DOSEA",
  value_var = "AVAL",
  param_var = "PARAM",
  strat_var = "TRT01A",
  list_vars = c("PPCAT", "PPSPEC"),
  ci_level = 0.9,
  log_scale = TRUE,
  title = NULL,
  subtitle = NULL,
  footnote = NULL,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A named list of ggplot objects.

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
plots <- p_pkpg02_doseprop(adpp)
plots[[1]]

## End(Not run)

Description

Produces one boxplot per PK parameter with treatment arms on the x-axis. Returns a named list of ggplot objects, one per PPCAT x PPSPEC combination in the ADPP data.

Usage

p_pkpg03_boxp(
  data,
  strat_var = "TRT01A",
  param_var = "PARAM",
  value_var = "AVAL",
  list_vars = c("PPCAT", "PPSPEC"),
  all_points = FALSE,
  title = NULL,
  subtitle = NULL,
  footnote = NULL,
  ylab = NULL
)

p_pkpg04_boxp(data, ...)

Arguments

Value

A named list of ggplot objects.

Functions

• p_pkpg04_boxp(): Boxplot with all individual data points overlaid (pkpg04).

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
plots <- p_pkpg03_boxp(adpp)
plots[[1]]

## End(Not run)

Description

Filters ADPP to metabolite rows using the same fallback logic as t_pkpt03_MP_col() (METABFL preferred, then PPCAT/PARAM grep for "metab"), then delegates to p_pkpg03_boxp().

Usage

p_pkpg06_mp(data, ...)

Arguments

Value

A named list of ggplot objects (same format as p_pkpg03_boxp()).

Examples

## Not run: 
adpp <- export_cdisc(res_nca)$adpp
plots <- p_pkpg06_mp(adpp)
plots[[1]]

## End(Not run)

Description

This function returns a blank Plotly plot with optional annotation text. It ensures that when no valid data is available, a meaningful placeholder plot is displayed instead of causing an error.

Usage

parse_annotation(data, text)

Arguments

Details

Parses annotations in the context of data. Special characters and syntax are substituted by actual data and/or substituted for format that is better parsed via rendering functions (e.g. plotly).

• ⁠\n⁠ character is substituted for ⁠<br>⁠ tag in order to add new lines in rendered image.

• ⁠$COLNAME⁠ is parsed to provide unique data value from the mentioned column.

• !COLNAME is parsed to provide label attribute for a given column name. If any values are missing from the provided data, they are substituted for ERR string.

Value

Parsed annotation text.

Description

An SDTM PC-like pharmacokinetic concentrations dataset derived from adnca_example. Intended to support and test the PC+EX upload workflow (see GitHub issue #624).

Usage

pc_example

Format

A data frame with the same number of rows as adnca_example and 11 variables:

DOMAIN

Character. Domain Abbreviation.

STUDYID

Character. Study identifier.

USUBJID

Character. Unique subject identifier.

PCTEST

Character. Name of the measured analyte (mapped from PARAM).

PCSPEC

Character. Specimen type (e.g., SERUM, URINE).

PCSTRESN

Numeric. Numeric concentration result (mapped from AVAL).

PCSTRESU

Character. Concentration units (mapped from AVALU).

PCDTC

Character. Sample collection datetime (ISO 8601), synthesised from AFRLT using an arbitrary reference origin.

PCRFTDTC

Character. Reference dose datetime (ISO 8601), synthesised from AFRLT - ARRLT.

PCELTM

Character. Planned elapsed time from reference dose in ISO 8601 duration format (e.g., "PT0.5H"), derived from NFRLT.

VOLUME

Numeric. Volume of specimen collected (mL), for urine samples.

VOLUMEU

Character. Volume units.

Source

Derived from adnca_example and dm_example via data-raw/sdtm_example.R.

Description

This function reshapes the structure of the results produced by the main function of the PKNCA package (pk.nca) in a way that each row represents all the main results summarized for each profile in each individual/subject. Excluding the ID variables, each column name corresponds with a calculated parameter and between brackets its corresponding units. AUC intervals, if present, are be added as additional columns.

Usage

pivot_wider_pknca_results(myres, flag_rules = NULL, extra_vars_to_keep = NULL)

Arguments

Value

A data frame which provides an easy overview on the results from the NCA in each profile/subject and how it was computed lambda (half life) and the results of the NCA parameters (cmax, AUC, AUClast), including new columns Exclude (a derivation from pknca$exclude), Missing(indicating if flag parameters are missing from PKNCA calculation), and flagged (indicating if the row is ACCEPTED, FLAGGED or MISSING based on the flagging rules).

Description

Generates a PK Dose QC plot by layering concentration data (as black shapes) and dose data (as colored points). It creates a single, unified legend for both data types and can return either a static ggplot or an interactive plotly object.

Usage

pk_dose_qc_plot(
  data_conc,
  data_dose = NULL,
  x_var,
  y_var,
  colour_var,
  shape_var,
  grouping_vars,
  other_tooltip_vars = NULL,
  x_var_units = NULL,
  colour_var_units = NULL,
  labels_df = metadata_nca_variables,
  title = NULL,
  show_pk_samples = TRUE,
  show_doses = TRUE,
  as_plotly = FALSE,
  height = NULL
)

Arguments

Details

Unless specified, the variables required as arguments are expected to be present in both data_conc and data_dose.

Value

A ggplot object or, if as_plotly = TRUE, a plotly object.

Examples

# Sample concentration data
conc_data <- data.frame(
  USUBJID = rep(paste0("S-", 1:2), each = 2),
  ACTUAL_TIME = c(0, 24, 0, 24),
  SAMPLE_TYPE = rep(c("PLASMA", "URINE"), 2),
  COHORT = "A",
  TIME_UNIT = "hr"
 )

# Sample dose data
dose_data <- data.frame(
  USUBJID = rep(paste0("S-", 1:2), each = 1),
  ACTUAL_TIME = c(0, 0),
  DOSE_LEVEL = c(100, 100),
  COHORT = "A",
  DOSE_UNIT = "mg"
)

# Generate the plot
pk_dose_qc_plot(
  data_conc = conc_data,
  data_dose = dose_data,
  x_var = "ACTUAL_TIME",
  y_var = "USUBJID",
  colour_var = "DOSE_LEVEL",
  shape_var = "SAMPLE_TYPE",
  grouping_vars = "COHORT",
  x_var_units = "TIME_UNIT",
  colour_var_units = "DOSE_UNIT",
  title = "Sample Dosing and PK Plot"
)

Description

Calculate the maximum excretion rate

Usage

pk.calc.ermax(conc, volume, time, duration.conc, check = TRUE)

Arguments

Value

The maximum excretion rate, or NA if not available

Description

Calculate the midpoint collection time of the last measurable excretion rate

Usage

pk.calc.ertlst(conc, volume, time, duration.conc, check = TRUE)

Arguments

Value

The midpoint collection time of the last measurable excretion rate, or NA/0 if not available

Description

Calculate the midpoint collection time of the maximum excretion rate

Usage

pk.calc.ertmax(
  conc,
  volume,
  time,
  duration.conc,
  check = TRUE,
  first.tmax = NULL,
  options = list()
)

Arguments

Value

The midpoint collection time of the maximum excretion rate, or NA if not available

Description

Calculate the total urine volume

Usage

pk.calc.volpk(volume)

Arguments

Value

The sum of urine volumes for the interval

Description

This function generates a list of ggplots for PK concentration-time profiles.

Usage

pkcg01(
  adnca = data(),
  xvar = "AFRLT",
  yvar = "AVAL",
  xvar_unit = "RRLTU",
  yvar_unit = "AVALU",
  color_var = NULL,
  color_var_label = NULL,
  xbreaks_var = "NFRLT",
  xbreaks_mindist = 0.5,
  xmin = NA,
  xmax = NA,
  ymin = NA,
  ymax = NA,
  xlab = paste0("!", xvar, " [$", xvar_unit, "]"),
  ylab = paste0("!", yvar, " [$", yvar_unit, "]"),
  title = NULL,
  subtitle = NULL,
  footnote = NULL,
  plotgroup_vars = c("ROUTE", "PCSPEC", "PARAM", "USUBJID"),
  plotgroup_names = list(ROUTE = "Route", PCSPEC = "Specimen", PARAM = "Analyte", USUBJID
    = "Subject ID"),
  scale = c("LIN", "LOG", "SBS")[1],
  studyid = "STUDYID",
  trt_var = "TRT01A",
  plotly = TRUE
)

Arguments

Value

A list of ggplot or plotly objects for each unique group.

Author(s)

Gerardo Rodriguez magic numbers for footnote position and margin, work in app up to 4 lines NOTE: might require some fine tuning down the line, looks fine now

Examples

adnca <- adnca_example
adnca <- subset(adnca, adnca$USUBJID %in% unique(adnca$USUBJID)[c(1, 2)])
attr(adnca[["AFRLT"]], "label") <- "Actual time from first dose"
attr(adnca[["AVAL"]], "label") <- "Analysis val"

plots_lin <- pkcg01(adnca = adnca, xmax = 1, scale = "LIN")

Description

This function generates a list of plotly objects PK concentration-time profiles by group

Usage

pkcg02(
  adnca = data(),
  xvar = "AFRLT",
  yvar = "AVAL",
  xvar_unit = "RRLTU",
  yvar_unit = "AVALU",
  color_var = NULL,
  color_var_label = NULL,
  xbreaks_var = "NFRLT",
  xbreaks_mindist = 0.5,
  xmin = NA,
  xmax = NA,
  ymin = NA,
  ymax = NA,
  xlab = paste0("!", xvar, " [$", xvar_unit, "]"),
  ylab = paste0("!", yvar, " [$", yvar_unit, "]"),
  title = NULL,
  subtitle = NULL,
  footnote = NULL,
  plotgroup_vars = c("ROUTE", "PCSPEC", "PARAM", "TRT01A"),
  plotgroup_names = list(ROUTE = "Route", PCSPEC = "Specimen", PARAM = "Analyte", TRT01A
    = "Treatment", USUBJID = "Subject ID"),
  scale = c("LIN", "LOG", "SBS")[1],
  studyid = "STUDYID",
  trt_var = "TRT01A",
  plotly = TRUE
)

Arguments