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‎DESCRIPTION‎

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Type: Package
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Package: admiralneuro
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Title: Neuroscience Extension Package for ADaM in 'R' Asset Library
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Version: 0.1.0.9005
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Version: 0.1.0.9009
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Authors@R: c(
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person("Jian", "Wang", , "wang_jian_wj@lilly.com", role = c("aut", "cre"),
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comment = c(ORCID = "0009-0002-4677-3781")),

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(compute_centiloid)
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export(compute_upsit_percentile)
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importFrom(admiral,derive_vars_dy)
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importFrom(admiraldev,assert_character_scalar)
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importFrom(admiraldev,assert_character_vector)

‎NEWS.md‎

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## Updates of Existing Functions
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- Improved test coverage in `compute_centiloid()` function when invalid tracer combination is provided. (#106)
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## New features
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## Documentation
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## Template programs
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## Various
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- Moved test SDTM datasets `dm_neuro`, `nv_neuro`, `suppnv_neuro` and `ag_neuro` from `{admiralneuro}` to `{pharmaversesdtm}`. (#92)
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# admiralneuro 0.1.0
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‎R/compute_upsit_percentile.R‎

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#' Compute UPSIT Percentile Based on Age, Sex, and Score
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#'
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#' This function computes the UPSIT (University of Pennsylvania Smell
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#' Identification Test) percentile for a given subject based on their age,
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#' sex, and UPSIT raw score. The percentile is determined by looking up the
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#' corresponding value in a normative reference table.
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#'
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#' @md
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#'
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#' @param sex Sex of the subject. Must be either "M" or "F"
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#' A character string of either "M" or "F" is expected
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#'
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#' @permitted [char_scalar]
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#'
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#' @param age Age of the subject in years.
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#'
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#' @permitted [num_scalar]
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#'
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#' @param upsit_score The UPSIT ranging from 0 to 40.
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#' Higher scores indicate better olfactory function.
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#'
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#' @permitted [num_scalar]
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#'
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#' @details
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#' The function uses an internal lookup table (`upsit_lookup`) that contains
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#' normative percentile data stratified by sex, age range, and UPSIT score.
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#' The lookup table is based on published normative data.
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#'
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#' Age ranges in the lookup table include:
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#' \itemize{
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#' \item 50-54 years
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#' \item 55-59 years
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#' \item 60-64 years
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#' \item 65-69 years
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#' \item 70-74 years
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#' \item 75-79 years
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#' \item 80+ years (no upper limit)
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#' }
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#'
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#' The function is designed to work efficiently in vectorized operations
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#' within data processing pipelines (e.g., with `dplyr::mutate()`)
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#'
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#' @references
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#' Brumm MC, et. al., Updated Percentiles for the University of
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#' Pennsylvania Smell Identification Test in Adults 50 Years of Age and Older.
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#' \doi{10.1212/WNL.0000000000201463}
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#'
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#' @return A numeric percentile value.
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#' The UPSIT percentile value corresponding to the input
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#' parameters. Returns `NA_real_` if no matching entry is found in the
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#' lookup table.
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#'
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#' @keywords com_bds_findings
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#' @family com_bds_findings
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#'
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#' @export
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#'
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#' @examplesx
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#' @caption Look up for male percentile
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#' @info A 52 years old male with upsit raw score of 25
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#' @code compute_upsit_percentile(sex = "M", age = 52, upsit_score = 25)
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#'
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#' @caption Look up for female percentile
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#' @info A 81 years old female with upsit raw score of 30
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#' @code compute_upsit_percentile(sex = "F", age = 81, upsit_score = 30)
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#'
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#' @caption Returns NA
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#' @info Minimal age is 50 and score of 0 and 40, return NA if no match found
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#' @code compute_upsit_percentile(sex = "M", age = 45, upsit_score = 25)
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#'
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compute_upsit_percentile <- function(sex, age, upsit_score) {
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# Vectorized implementation
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n <- length(sex)
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result <- rep(NA_real_, n)
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# Process each element
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for (i in seq_len(n)) {
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# Find matching row in lookup table
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# Handle age_high = NA for "80+" cases
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idx <- which(
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upsit_lookup$sex == sex[i] &
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upsit_lookup$upsit == upsit_score[i] &
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age[i] >= upsit_lookup$age_low &
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(is.na(upsit_lookup$age_high) | age[i] <= upsit_lookup$age_high)
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)
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# Assign percentile if match found
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if (length(idx) > 0) {
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result[i] <- upsit_lookup$upsit_pctl[idx[1]]
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}
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}
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result
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}

‎R/data.R‎

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#' Demographic Dataset - Neuro
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#'
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#' An updated SDTM DM dataset subset with age appropriate Alzheimer's Disease patients
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#' @keywords datasets
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#' @family datasets
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"dm_neuro"
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# This file is automatically generated by data-raw/nv_neuro.R.
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# Please rerun nv_enuro.R to create it again.
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# Manual edits are not recommended, as changes may be overwritten.
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#' Nervous System Findings Dataset
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#'
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#' A SDTM NV domain dataset for Alzheimer's disease observational and interventional studies, including amyloid and tau PET data at baseline and two follow-up visits reflect levels of pathology appropriate for disease or treatment course
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#' @keywords datasets
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#' @family datasets
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#'
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#' @name nv_neuro
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#' @title Nervous System Findings Dataset
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#' @description A SDTM NV domain dataset containing nervous system findings and measurements
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#' @docType data
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#' @format A data frame with 20 columns:
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#' \describe{
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#' \item{STUDYID}{Study Identifier}
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#' \item{DOMAIN}{Domain Abbreviation}
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#' \item{USUBJID}{Unique Subject Identifier}
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#' \item{NVSEQ}{Sequence Number}
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#' \item{NVLNKID}{Link ID}
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#' \item{NVTESTCD}{Short Name of Nervous System Test}
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#' \item{NVTEST}{Name of Nervous System Test}
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#' \item{NVCAT}{Category for Nervous System Test}
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#' \item{NVLOC}{Location Used for the Measurement}
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#' \item{NVMETHOD}{Method of Test or Examination}
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#' \item{NVNAM}{Vendor Name}
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#' \item{NVORRES}{Result or Finding in Original Units}
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#' \item{NVORRESU}{Original Units}
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#' \item{NVSTRESC}{Character Result/Finding in Std Format}
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#' \item{NVSTRESN}{Numeric Result/Finding in Standard Units}
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#' \item{NVSTRESU}{Standard Units}
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#' \item{VISITNUM}{Visit Number}
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#' \item{VISIT}{Visit Name}
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#' \item{NVDTC}{Date/Time of Collection}
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#' \item{NVDY}{Study Day of Collection}
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#' \item{NVLOBXFL}{Last Observation Before Exposure Flag}
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#' }
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#'
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#' @source Constructed using `dm_neuro` from `{admiralneuro}` package for USUBJID and cohort information, `vs` from `{pharmaversesdtm}` for visit schedule such as VISIT, NVDTC, NVDY
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"nv_neuro"
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# This file is automatically generated by data-raw/suppnv_neuro.R
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# Please rerun suppnv_neuro.R to create it again.
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# Manual edits are not recommended, as changes may be overwritten.
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#' Supplemental Nervous System Findings Dataset
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#'
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#' A SDTM SUPPNV domain dataset
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#' @keywords datasets
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#' @family datasets
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#'
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#' @name suppnv_neuro
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#' @title Supplemental Nervous System Findings Dataset
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#' @description A SDTM SUPPNV domain dataset containing reference regions used for SUVR calculation
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#' @docType data
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#' @format A data frame with 8 columns:
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#' \describe{
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#' \item{STUDYID}{Study Identifier}
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#' \item{RDOMAIN}{Related Domain Abbreviation}
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#' \item{USUBJID}{Unique Subject Identifier}
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#' \item{IDVAR}{Identifying Variable}
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#' \item{IDVARVAL}{Identifying Variable Value}
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#' \item{QNAM}{Qualifier Variable Name}
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#' \item{QLABEL}{Qualifier Variable Label}
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#' \item{QVAL}{Data Value}
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#' \item{QORIG}{Origin}
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#' \item{QEVAL}{Evaluator}
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#' }
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#' @source Constructed using `nv_neuro` from `{admiralneuro}` package
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"suppnv_neuro"
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# This file is automatically generated by data-raw/ag_neuro.R
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# Please rerun ag_neuro.R to create it again.
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# Manual edits are not recommended, as changes may be overwritten.
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#' Procedure Agents for Nervous System Dataset
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#'
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#' A SDTM AG domain dataset
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#' @keywords datasets
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#' @family datasets
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#'
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#' @name ag_neuro
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#' @title Procedure Agents for Nervous System Dataset
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#' @description A SDTM AG domain dataset containing procedure agents for nervous system
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#' @docType data
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#' @format A data frame with 12 columns:
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#' \describe{
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#' \item{STUDYID}{Study Identifier}
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#' \item{DOMAIN}{Domain Abbreviation}
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#' \item{USUBJID}{Unique Subject Identifier}
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#' \item{AGSEQ}{Sequence Number}
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#' \item{AGTRT}{Reported Agent Name}
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#' \item{AGCAT}{Category for Category}
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#' \item{AGDOSE}{Dose per Administration}
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#' \item{AGDOSEU}{Dose Units}
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#' \item{AGROUTE}{Route of Administration}
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#' \item{AGLNKID}{Link ID}
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#' \item{VISITNUM}{Visit Number}
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#' \item{VISIT}{Visit Name}
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#' \item{AGSTDTC}{Start Date/Time of Agent}
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#' }
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#' @source Constructed using `nv_neuro` from `{admiralneuro}` package
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"ag_neuro"
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#' Amyloid PET Scan Analysis Dataset - Neuro
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#'
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#' An updated ADaM ADAPET dataset using NV, AG, SUPPNV, ADSL

‎R/sysdata.rda‎

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‎README.md‎

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Neuroscience extension package for ADaM in R Asset Library `{admiral}`
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*Explore all the other packages in the [{admiral} ecosystem](https://pharmaverse.org/e2eclinical/adam/)
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to learn more about ADaM programming in R.*
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## Purpose
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To provide a complementary (to `{admiral}`) toolbox that enables users

‎_pkgdown.yml‎

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math-rendering: mathjax
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params:
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bootswatch: flatly
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includes:
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in_header: |
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<script
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async
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src="https://widget.kapa.ai/kapa-widget.bundle.js"
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data-website-id="2c7249cc-f1dc-4a63-8674-6ecbcb34d95c"
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data-project-name="admiral"
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data-project-color="#2c3e50"
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data-project-logo="https://pharmaverse.github.io/admiralneuro/logo.png"
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data-consent-required="true"
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data-user-analytics-cookie-enabled="true"
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data-consent-screen-title="Help us improve our AI assistant"
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data-consent-screen-disclaimer="By clicking 'Allow tracking', you consent to anonymous user tracking which helps us improve our service. We don't collect any personally identifiable information."
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data-consent-screen-accept-button-text="Allow tracking"
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data-consent-screen-reject-button-text="No, thanks"
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data-modal-example-questions="Tell me a little about the {admiralneuro} package., What are some important {admiralneuro} functions and what do they do?"
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data-modal-z-index="9999"
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></script>
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search:
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exclude: ['news/index.html']
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repo:
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url:
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home: https://github.com/pharmaverse/admiralneuro

‎data-raw/ag_neuro.R‎

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