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Type: Package
Title: ADaM in R Asset Library - Ophthalmology
Version: 1.3.0
Description: Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package).
License: Apache License (≥ 2)
URL: https://pharmaverse.github.io/admiralophtha/, https://github.com/pharmaverse/admiralophtha/
BugReports: https://github.com/pharmaverse/admiralophtha/issues/
Depends: R (≥ 4.1)
Imports: admiral (≥ 1.1.1), admiraldev (≥ 1.1.0), dplyr (≥ 1.0.5), hms (≥ 0.5.3), lifecycle (≥ 0.1.0), lubridate (≥ 1.7.4), magrittr (≥ 1.5), purrr (≥ 0.3.3), rlang (≥ 0.4.4), stringr (≥ 1.4.0), tidyr (≥ 1.0.2), tidyselect (≥ 1.1.0)
Suggests: devtools, diffdf, knitr, lintr, methods, miniUI, pharmaversesdtm (≥ 1.3.0), pkgdown, rmarkdown, roxygen2, spelling, testthat (≥ 3.0.0), tibble, usethis
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-04 12:36:42 UTC; mancinie
Author: Edoardo Mancini ORCID iD [aut, cre], Ritika Aggarwal [aut], Jane Gao [aut], William Holmes [aut], Josie Jackson [aut], Sonali Jain [aut], Yuki Matsunaga [aut], Gordon Miller [aut], Rachel Linacre [aut], Lucy Palmen [aut], Nandini R Thampi [aut], Aldrich Salva [aut], Steven Ting [aut]
Maintainer: Edoardo Mancini <edoardo.mancini@roche.com>
Repository: CRAN
Date/Publication: 2025-07-04 13:30:02 UTC

admiralophtha: ADaM in R Asset Library - Ophthalmology

Description

logo

Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package).

Author(s)

Maintainer: Edoardo Mancini edoardo.mancini@roche.com (ORCID)

Authors:

See Also

Useful links:

Best Corrected Visual Acuity Analysis Dataset

Description

An example Best Corrected Visual Acuity (BCVA) analysis dataset

Usage

admiralophtha_adbcva

Format

An object of class tbl_df (inherits from tbl, data.frame) with 7672 rows and 116 columns.

Source

Derived from the OE and ADSL datasets using {admiral}, {admiralophtha} and the ADBCVA template.

See Also

Other datasets: admiralophtha_adoe, admiralophtha_advfq

Ophthalmology Exam Analysis Dataset

Description

An example Ophthalmology Exam Analysis dataset

Usage

admiralophtha_adoe

Format

An object of class tbl_df (inherits from tbl, data.frame) with 19136 rows and 103 columns.

Source

Derived from the OE and ADSL datasets using {admiral}, {admiralophtha} and the ADOE template.

See Also

Other datasets: admiralophtha_adbcva, admiralophtha_advfq

Visual Function Questionnaire Analysis Dataset

Description

An example Visual Function Questionnaire (VFQ) analysis dataset

Usage

admiralophtha_advfq

Format

An object of class tbl_df (inherits from tbl, data.frame) with 28798 rows and 41 columns.

Source

Derived from the ADSL and QS datasets using {admiral}, {admiralophtha} and the ADVFQ template. The full, open-source VFQ questionnaire can be accessed here.

See Also

Other datasets: admiralophtha_adbcva, admiralophtha_adoe

ETDRS –> LogMAR conversion

Description

Convert ETDRS score to LogMAR units

Usage

convert_etdrs_to_logmar(value)

Arguments

value

object containing ETDRS score to convert to logMAR

Details

ETDRS value converted to logMAR as logMAR = -0.02 * ETDRS + 1.7

Source for conversion formula: Beck, R.W., et al. A computerized method of visual acuity testing. American Journal of Ophthalmology, 135(2), pp.194-205. doi:https://doi.org/10.1016/s0002-9394(02)01825-1.

Value

The input value converted converted to logMAR units

Author(s)

Rachel Linacre

Examples

library(tibble)
library(dplyr)
library(admiral)
library(admiraldev)

adbcva <- tribble(
  ~STUDYID, ~USUBJID, ~AVAL,
  "XXX001", "P01", 5,
  "XXX001", "P02", 10,
  "XXX001", "P03", 15,
  "XXX001", "P04", 20,
  "XXX001", "P05", 25
)

adbcva <- adbcva %>% mutate(AVAL = convert_etdrs_to_logmar(AVAL))

LogMAR –> ETDRS conversion

Description

Convert LogMAR score to ETDRS units

Usage

convert_logmar_to_etdrs(value)

Arguments

value

object containing logMAR score to convert to ETDRS

Details

logMAR value converted to ETDRS as ETDRS = -(logMAR - 1.7) / 0.02

Source for conversion formula: Beck, R.W., et al. A computerized method of visual acuity testing. American Journal of Ophthalmology, 135(2), pp.194-205. doi:https://doi.org/10.1016/s0002-9394(02)01825-1.

Value

The input value converted to ETDRS units

Author(s)

Nandini R Thampi

Examples

library(tibble)
library(dplyr)
library(admiral)

oe <- tribble(
  ~STUDYID, ~USUBJID, ~OETESTCD, ~OEMETHOD, ~OESTRESN,
  "XXX001", "P01", "VACSCORE", "logMAR EYE CHART", 1.08,
  "XXX001", "P02", "VACSCORE", "logMAR EYE CHART", 1.66,
  "XXX001", "P03", "VACSCORE", "logMAR EYE CHART", 1.60,
  "XXX001", "P04", "VACSCORE", "ETDRS EYE CHART", 57,
  "XXX001", "P05", "VACSCORE", "ETDRS EYE CHART", 1
)

adbcva <- oe %>%
  filter(OETESTCD == "VACSCORE" & toupper(OEMETHOD) == "LOGMAR EYE CHART") %>%
  mutate(OESTRESN = convert_logmar_to_etdrs(OESTRESN))

Derive Affected Eye

Description

Derive Affected Eye (AFEYE) in occurrence datasets

Usage

derive_var_afeye(dataset, loc_var, lat_var, loc_vals = "EYE")

Arguments

dataset

Input dataset

[Deprecated] Please use dataset instead.

loc_var

Location variable

lat_var

Laterality variable

loc_vals

xxLOC values for which AFEYE is derived

[Deprecated] Please simply ensure xxLAT values are contained in c("LEFT", "RIGHT", "BILATERAL").

Details

Affected Eye is derived in the occurrence dataset using laterality and Study Eye. This assumes Study Eye has already been added from ADSL.

Value

The input occurrence dataset with Affected Eye (AFEYE) added.

Author(s)

Lucy Palmen

Examples

library(tibble)
library(admiral)

adae1 <- tribble(
  ~STUDYID, ~USUBJID, ~STUDYEYE, ~AELOC, ~AELAT,
  "XXX001", "P01", "RIGHT", "EYE", "RIGHT",
  "XXX001", "P01", "RIGHT", "EYE", "LEFT",
  "XXX001", "P01", "RIGHT", "EYE", "",
  "XXX001", "P01", "RIGHT", "", "RIGHT",
  "XXX001", "P02", "LEFT", "", "",
  "XXX001", "P02", "LEFT", "EYE", "LEFT",
  "XXX001", "P04", "BILATERAL", "EYE", "RIGHT",
  "XXX001", "P05", "RIGHT", "EYE", "RIGHT",
  "XXX001", "P05", "RIGHT", "EYE", "BILATERAL",
  "XXX001", "P06", "BILATERAL", "", "",
  "XXX001", "P06", "BILATERAL", "", "RIGHT",
  "XXX001", "P07", "BILATERAL", "EYE", "BILATERAL",
  "XXX001", "P08", "", "EYE", "BILATERAL",
  "XXX001", "P09", "NONSENSE", "EYE", "BILATERAL",
  "XXX001", "P09", "BILATERAL", "EYE", "NONSENSE",
  "XXX001", "P09", "BILATERAL", "NONSENSE", "BILATERAL",
  "XXX001", "P10", "RIGHT", "EYE", "BOTH"
)

derive_var_afeye(adae1, loc_var = AELOC, lat_var = AELAT)

adae2 <- tribble(
  ~STUDYID, ~USUBJID, ~STUDYEYE, ~AELOC, ~AELAT,
  "XXX001", "P01", "RIGHT", "EYES", "RIGHT",
  "XXX001", "P02", "RIGHT", "RETINA", "LEFT",
  "XXX001", "P03", "LEFT", "", ""
)

derive_var_afeye(adae2, loc_var = AELOC, lat_var = AELAT, loc_vals = c("EYES", "RETINA"))

Adds CRITx/CRITxFL pairs to BCVA dataset

Description

[Superseded] The derive_var_bcvacritxfl() function has been superseded in favor of admiral::derive_vars_crit_flag().

Adds a criterion variables CRITx and their corresponding flags CRITxFL to a dataset containing BCVA records

Usage

derive_var_bcvacritxfl(
  dataset,
  crit_var,
  bcva_ranges = NULL,
  bcva_uplims = NULL,
  bcva_lowlims = NULL,
  additional_text = "",
  critxfl_index = NULL
)

Arguments

dataset

Input dataset containing BCVA data (usually ADBCVA).

crit_var

Variable with respect to which CRITx/CRITxFL are derived (usually CHG or AVAL).

bcva_ranges

List containing one or more numeric vectors of length 2. For each vector c(a,b) in bcva_ranges, a pair of variables CRITx, CRITxFL is created with the condition: ⁠a <= crit_var <= b⁠. If criterion flags of that type are not required, then leave as NULL.

bcva_uplims

List containing one or more numeric elements. For each element a in bcva_uplims, a pair of variables CRITx, CRITxFL is created with the condition: crit_var <= a. If criterion flags of that type are not required, then leave as NULL.

bcva_lowlims

List containing one or more numeric elements. For each element b in bcva_lowlims, a pair of variables CRITx, CRITxFL is created with the condition: crit_var >= b. If criterion flags of that type are not required, then leave as NULL.

additional_text

string containing additional text to append to CRITx

critxfl_index

positive integer detailing the first value of x to use in CRITxFL. If not supplied, the function takes the first available value of x, counting up from x = 1.

Details

This function works by calling derive_var_bcvacritxfl() once for each of the elements in bcva_ranges, bcva_uplims and bcva_lowlims. NOTE: if crit_var is equal to NA, then the resulting criterion flag is also marked as NA.

Value

The input BCVA dataset with additional column pairsCRITx, CRITxFL.

Author(s)

Edoardo Mancini

Examples

library(tibble)
library(admiral)
library(admiraldev)

adbcva1 <- tribble(
  ~STUDYID, ~USUBJID, ~AVISIT, ~BASETYPE, ~PARAMCD, ~CHG,
  "XXX001", "P01", "BASELINE", "LAST", "SBCVA", 0,
  "XXX001", "P01", "WEEK 2", "LAST", "FBCVA", 2,
  "XXX001", "P02", "BASELINE", "LAST", "SBCVA", -13,
  "XXX001", "P02", "WEEK 2", "LAST", "FBCVA", 5,
  "XXX001", "P03", "BASELINE", "LAST", "SBCVA", NA,
  "XXX001", "P03", "WEEK 2", "LAST", "FBCVA", 17
)

derive_var_bcvacritxfl(
  dataset = adbcva1,
  crit_var = exprs(CHG),
  bcva_ranges = list(c(0, 5), c(-5, -1), c(10, 15)),
  bcva_uplims = list(5, 10),
  bcva_lowlims = list(8),
  additional_text = ""
)

adbcva2 <- tribble(
  ~STUDYID, ~USUBJID, ~AVISIT, ~BASETYPE, ~PARAMCD, ~AVAL, ~CHG,
  "XXX001", "P01", "BASELINE", "LAST", "SBCVA", 4, NA,
  "XXX001", "P01", "BASELINE", "LAST", "SBCVA", 6, NA,
  "XXX001", "P01", "AVERAGE BASELINE", "AVERAGE", "SBCVA", 5, NA,
  "XXX001", "P01", "WEEK 2", "LAST", "SBCVA", -3, NA,
  "XXX001", "P01", "WEEK 4", "LAST", "SBCVA", -10, NA,
  "XXX001", "P01", "WEEK 6", "LAST", "SBCVA", 12, NA,
  "XXX001", "P01", "WEEK 2", "AVERAGE", "SBCVA", -2, -7,
  "XXX001", "P01", "WEEK 4", "AVERAGE", "SBCVA", 6, 1,
  "XXX001", "P01", "WEEK 6", "AVERAGE", "SBCVA", 3, -2
)

restrict_derivation(
  adbcva2,
  derivation = derive_var_bcvacritxfl,
  args = params(
    crit_var = exprs(CHG),
    bcva_ranges = list(c(0, 5), c(-10, 0)),
    bcva_lowlims = list(5),
    additional_text = " (AVERAGE)"
  ),
  filter = PARAMCD %in% c("SBCVA", "FBCVA") & BASETYPE == "AVERAGE"
)

Add CRITx/CRITxFL pair to BCVA dataset

Description

[Superseded] The derive_var_bcvacritxfl_util() function has been superseded in favor of admiral::derive_vars_crit_flag().

Helper function for derive_var_bcvacritxfl() that adds a criterion variable CRITx and its corresponding flag CRITxFL to a dataset containing BCVA records

Usage

derive_var_bcvacritxfl_util(
  dataset,
  crit_var,
  critx_text,
  critxfl_cond,
  counter,
  bcva_range = NULL,
  bcva_uplim = NULL,
  bcva_lowlim = NULL
)

Arguments

dataset

Input dataset (usually ADBCVA).

crit_var

Variable with respect to which CRITx/CRITxFL are derived (usually CHG or AVAL).

critx_text

String containing the text for CRITx variable.

critxfl_cond

String containing R code detailing the criterion to be satisfied for CRITxFL variable to be equal to "Y".

counter

Integer detailing the value of x to use in CRITxFL.

bcva_range

Numeric vector of length two detailing lower and upper change in BCVA limits (bcva_range will be called in critxfl_cond if the criterion stipulates that change in BCVA lie inside some range).

bcva_uplim

Numeric value detailing highest change in BCVA limit (bcva_uplim will be called in critxfl_cond if the criterion stipulates that change in BCVA lie below some upper limit).

bcva_lowlim

Numeric value detailing lowest change in BCVA limit (bcva_lowlim will be called in critxfl_cond if the criterion stipulates that change in BCVA lie above some lower limit).

Details

The criterion for change in BCVA in CRITxFL can be of three types: (1) value lies within some range; ⁠a <= crit_var <= b⁠; (2) value is below some upper limit; crit_var <= a; (3) value is above some lower limit; b <= crit_var. For (1), bcva_range must be specified to this function; for (2), bcva_uplim; for (3) bcva_lowlim. It is necessary to supply at least one of these three arguments. NOTE: if crit_var is equal to NA, then the resulting criterion flag is also marked as NA.

Value

The input BCVA dataset with additional columns CRITx, CRITxFL.

Author(s)

Edoardo Mancini

Derive Study Eye

Description

Derive Study Eye (STUDYEYE) in the ADSL dataset

Usage

derive_var_studyeye(dataset_adsl, dataset_sc, sctestcd_value = "FOCID")

Arguments

dataset_adsl

ADSL input dataset

dataset_sc

SC input dataset

sctestcd_value

SCTESTCD value flagging Study Eye selection records. Default: "FOCID".

Details

Study Eye is derived in ADSL using the "Study Eye selection" records in the SC SDTM dataset.

Value

The input ADSL dataset with an additional column named STUDYEYE

Author(s)

Edoardo Mancini

Examples

library(tibble)
library(admiral)

adsl <- tribble(
  ~STUDYID, ~USUBJID,
  "XXX001", "P01",
  "XXX001", "P02",
  "XXX001", "P03",
  "XXX001", "P04",
  "XXX001", "P05"
)

sc <- tribble(
  ~STUDYID, ~USUBJID, ~SCTESTCD, ~SCSTRESC,
  "XXX001", "P01", "FOCID", "OS",
  "XXX001", "P01", "ACOHORT", "COHORT1",
  "XXX001", "P02", "FOCID", "OD",
  "XXX001", "P02", "ACOHORT", "COHORT3",
  "XXX001", "P04", "FOCID", "OU",
  "XXX001", "P05", "FOCID", "OD",
  "XXX001", "P06", "FOCID", "OS"
)

derive_var_studyeye(adsl, sc)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.