Creating ADBCVA • admiralophtha

Introduction

This article describes creating an ADBCVA ADaM with Best-Corrected Visual Acuity (BCVA) data for ophthalmology endpoints. It is to be used in conjunction with the article on creating a BDS dataset from SDTM. As such, derivations and processes that are not specific to ADBCVA are absent, and the user is invited to consult the aforementioned article for guidance.

Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.

Dataset Contents

As the name ADBCVA implies, admiralophtha suggests to populate ADBCVA solely with BCVA records from the OE SDTM.

Required Packages

The examples of this vignette require the following packages.

library(dplyr)
library(admiral)
library(pharmaversesdtm)
library(admiraldev)
library(admiralophtha)

Programming Workflow

Initial Set Up of ADBCVA
Deriving LogMAR Score Parameters
Further Derivations of Standard BDS Variables
Deriving Analysis Value Categories for Snellen Scores
Deriving Criterion Flags for BCVA Change
Additional Notes
Example Script

Initial set up of ADBCVA

As with all BDS ADaM datasets, one should start from the OE SDTM, where only the BCVA records are of interest. For the purposes of the next two sections, we shall be using the admiral OE and ADSL test data. We will also require a lookup table for the mapping of parameter codes.

Note: to simulate an ophthalmology study, we add a randomly generated STUDYEYE variable to ADSL, but in practice STUDYEYE will already have been derived using derive_var_studyeye().

data("oe_ophtha")
data("admiral_adsl")

# Add STUDYEYE to ADSL to simulate an ophtha dataset
adsl <- admiral_adsl %>%
  as.data.frame() %>%
  mutate(STUDYEYE = sample(c("LEFT", "RIGHT"), n(), replace = TRUE)) %>%
  convert_blanks_to_na()

oe <- convert_blanks_to_na(oe_ophtha) %>%
  ungroup()

# ---- Lookup table ----
param_lookup <- tibble::tribble(
  ~OETESTCD, ~OECAT, ~OESCAT, ~AFEYE, ~PARAMCD, ~PARAM, ~PARAMN,
  "VACSCORE", "BEST CORRECTED VISUAL ACUITY", "OVERALL EVALUATION", "Study Eye", "SBCVA", "Study Eye Visual Acuity Score (letters)", 1, # nolint
  "VACSCORE", "BEST CORRECTED VISUAL ACUITY", "OVERALL EVALUATION", "Fellow Eye", "FBCVA", "Fellow Eye Visual Acuity Score (letters)", 2, # nolint
)

Following this setup, the programmer can start constructing ADBCVA. The first step is to subset OE to only BCVA parameters and merge with ADSL. This is required for two reasons: firstly, STUDYEYE is crucial in the mapping of AFEYE and PARAMCD’s. Secondly, the treatment start date (TRTSDT) is also a prerequisite for the derivation of variables such as Analysis Day (ADY).

adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P, STUDYEYE)

adbcva <- oe %>%
  filter(
    OETESTCD %in% c("VACSCORE")
  ) %>%
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = adsl_vars,
    by_vars = get_admiral_option("subject_keys")
  )

The next item of business is to derive AVAL, AVALU, and DTYPE. In this example, due to the small number of parameters their derivation is trivial. AFEYE is also created in this step using the function derive_var_afeye().

adbcva <- adbcva %>%
  mutate(
    AVAL = OESTRESN,
    AVALU = "letters",
    DTYPE = NA_character_
  ) %>%
  derive_var_afeye(loc_var = OELOC, lat_var = OELAT)

Moving forwards, PARAM and PARAMCD can be assigned using derive_vars_merged() from admiral and the lookup table param_lookup generated above.

adbcva <- adbcva %>%
  derive_vars_merged(
    dataset_add = param_lookup,
    new_vars = exprs(PARAM, PARAMCD),
    by_vars = exprs(OETESTCD, AFEYE),
    filter_add = PARAMCD %in% c("SBCVA", "FBCVA")
  )

Deriving LogMAR Score Parameters

Often ADBCVA datasets contain derived records for BCVA in LogMAR units. This can easily be achieved as follows using derive_param_computed(). The conversion of units is done using convert_etdrs_to_logmar(). Two separate calls are required due to the parameters being split by study and fellow eye. Once these extra parameters are added, all the records that will be in the end dataset are now present, so AVALC and day/date variables such as ADY and ADT can be derived.

adbcva <- adbcva %>%
  derive_param_computed(
    by_vars = c(
      get_admiral_option("subject_keys"),
      exprs(VISIT, VISITNUM, OEDY, OEDTC, AFEYE, !!!adsl_vars)
    ),
    parameters = c("SBCVA"),
    set_values_to = exprs(
      AVAL = convert_etdrs_to_logmar(AVAL.SBCVA),
      PARAMCD = "SBCVALOG",
      PARAM = "Study Eye Visual Acuity LogMAR Score",
      DTYPE = NA_character_,
      AVALU = "LogMAR"
    )
  ) %>%
  derive_param_computed(
    by_vars = c(
      get_admiral_option("subject_keys"),
      exprs(VISIT, VISITNUM, OEDY, OEDTC, AFEYE, !!!adsl_vars)
    ),
    parameters = c("FBCVA"),
    set_values_to = exprs(
      AVAL = convert_etdrs_to_logmar(AVAL.FBCVA),
      PARAMCD = "FBCVALOG",
      PARAM = "Fellow Eye Visual Acuity LogMAR Score",
      DTYPE = NA_character_,
      AVALU = "LogMAR"
    )
  ) %>%
  mutate(AVALC = as.character(AVAL)) %>%
  derive_vars_dt(
    new_vars_prefix = "A",
    dtc = OEDTC,
    flag_imputation = "none"
  ) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))

Importantly, the above calls to derive_param_computed() list the SDTM variables VISIT, VISITNUM, OEDY and OEDTC as by_vars for the function. This is because they will be necessary to derive ADaM variables such as AVISIT and ADY in successive steps. Once all the ADaM variables which require them are derived, the SDTM variables should be set to missing for the derived records, as per ADaM standards:

adbcva <- adbcva %>%
  mutate(
    VISIT = ifelse(PARAMCD %in% c("SBCVALOG", "FBCVALOG"), NA_character_, VISIT),
    VISITNUM = ifelse(PARAMCD %in% c("SBCVALOG", "FBCVALOG"), NA, VISITNUM),
    OEDY = ifelse(PARAMCD %in% c("SBCVALOG", "FBCVALOG"), NA, OEDY),
    OEDTC = ifelse(PARAMCD %in% c("SBCVALOG", "FBCVALOG"), NA_character_, OEDTC)
  )

Further Derivations of Standard BDS Variables

The user is invited to consult the article on creating a BDS dataset from SDTM to learn how to add standard BDS variables to ADBCVA. Henceforth, for the purposes of this article, the following sections use the ADBCVA dataset generated by the corresponding admiralophtha template program as a starting point.

Note: This dataset already comes with some criterion flags and analysis value categorisation variables, so for illustration purposes these are removed.

data("admiralophtha_adbcva")

adbcva <- admiralophtha_adbcva %>%
  select(-starts_with("CRIT"), -starts_with("AVALCA"))

Deriving Analysis Value Categories for Snellen Scores

Some ophthalmology studies may desire to subdivide BCVA records according to which Snellen category they fall into (eg, 20/320, 20/100, 20/20 etc). This is best done through the use of AVALCATx/AVALCAxN variable pairs. Currently, admiralophtha does not provide specific functionality to create AVALCATx/AVALCAxN pairs, although this may be included in future releases of the package. With the current toolset, the suggested approach to derive such variables is to:

Create a lookup table which assigns numeric equivalents (i.e. AVALCAxN) to Snellen categories.
Create a format function to map each AVAL to a numeric category.
Add AVALCAxN through a mutate statement using the format function.
Add AVALCATx using derive_vars_merged in combination with the lookup table.

avalcat_lookup <- tibble::tribble(
  ~PARAMCD, ~AVALCA1N, ~AVALCAT1,
  "SBCVA", 1000, "< 20/800",
  "SBCVA", 800, "20/800",
  "SBCVA", 640, "20/640",
  "SBCVA", 500, "20/500",
  "SBCVA", 400, "20/400",
  "SBCVA", 320, "20/320",
  "SBCVA", 250, "20/250",
  "SBCVA", 200, "20/200",
  "SBCVA", 160, "20/160",
  "SBCVA", 125, "20/125",
  "SBCVA", 100, "20/100",
  "SBCVA", 80, "20/80",
  "SBCVA", 63, "20/63",
  "SBCVA", 50, "20/50",
  "SBCVA", 40, "20/40",
  "SBCVA", 32, "20/32",
  "SBCVA", 25, "20/25",
  "SBCVA", 20, "20/20",
  "SBCVA", 16, "20/16",
  "SBCVA", 12, "20/12",
  "SBCVA", 1, "> 20/12",
)

avalcat_lookup <- avalcat_lookup %>%
  mutate(PARAMCD = "FBCVA") %>%
  rbind(avalcat_lookup)

format_avalcat1n <- function(param, aval) {
  case_when(
    param %in% c("SBCVA", "FBCVA") & aval >= 0 & aval <= 3 ~ 1000,
    param %in% c("SBCVA", "FBCVA") & aval >= 4 & aval <= 8 ~ 800,
    param %in% c("SBCVA", "FBCVA") & aval >= 9 & aval <= 13 ~ 640,
    param %in% c("SBCVA", "FBCVA") & aval >= 14 & aval <= 18 ~ 500,
    param %in% c("SBCVA", "FBCVA") & aval >= 19 & aval <= 23 ~ 400,
    param %in% c("SBCVA", "FBCVA") & aval >= 24 & aval <= 28 ~ 320,
    param %in% c("SBCVA", "FBCVA") & aval >= 29 & aval <= 33 ~ 250,
    param %in% c("SBCVA", "FBCVA") & aval >= 34 & aval <= 38 ~ 200,
    param %in% c("SBCVA", "FBCVA") & aval >= 39 & aval <= 43 ~ 160,
    param %in% c("SBCVA", "FBCVA") & aval >= 44 & aval <= 48 ~ 125,
    param %in% c("SBCVA", "FBCVA") & aval >= 49 & aval <= 53 ~ 100,
    param %in% c("SBCVA", "FBCVA") & aval >= 54 & aval <= 58 ~ 80,
    param %in% c("SBCVA", "FBCVA") & aval >= 59 & aval <= 63 ~ 63,
    param %in% c("SBCVA", "FBCVA") & aval >= 64 & aval <= 68 ~ 50,
    param %in% c("SBCVA", "FBCVA") & aval >= 69 & aval <= 73 ~ 40,
    param %in% c("SBCVA", "FBCVA") & aval >= 74 & aval <= 78 ~ 32,
    param %in% c("SBCVA", "FBCVA") & aval >= 79 & aval <= 83 ~ 25,
    param %in% c("SBCVA", "FBCVA") & aval >= 84 & aval <= 88 ~ 20,
    param %in% c("SBCVA", "FBCVA") & aval >= 89 & aval <= 93 ~ 16,
    param %in% c("SBCVA", "FBCVA") & aval >= 94 & aval <= 97 ~ 12,
    param %in% c("SBCVA", "FBCVA") & aval >= 98 ~ 1
  )
}

adbcva <- adbcva %>%
  mutate(AVALCA1N = format_avalcat1n(param = PARAMCD, aval = AVAL)) %>%
  derive_vars_merged(
    avalcat_lookup,
    by = exprs(PARAMCD, AVALCA1N)
  )

The resulting output is shown below (limited to the first patient only):

Deriving Criterion Flags for BCVA Change

admiralophtha suggests the use of criterion flag variable pairs (CRITx/CRITxFL) to program BCVA endpoints such as Avoiding a loss of x letters or Gain of y letters or Gain of between x and y letters (relative to baseline or other basetypes). The package provides the function derive_var_bcvacritxfl() to program these endpoints efficiently and consistently. In terms of the logic to apply to the variable CHG, the endpoints fall into three classes, which can be represented by inequalities:

Class 1: CHG value lying inside a range, a <= CHG <= b.
Class 2: CHG value below an upper limit, CHG <= a.
Class 3: CHG value above a lower limit, CHG => b.

By using derive_var_bcvacritxfl(), the ADaM programmer can implement all three types of endpoint at once. This is achieved by feeding the appropriate ranges, upper limits and lower limits to the bcva_ranges, bcva_uplims and bcva_lowlims arguments of the function. For instance, let’s suppose that the endpoints of interest are:

Gain of between 5 and 10 letters relative to baseline (Class 1: 5 <= CHG <= 10)
Gain of 25 letters or fewer relative to baseline (Class 2: CHG <= 25)
Loss of 5 letters or more relative to baseline (Class 2: CHG <= -5)
Gain of 15 letters or more relative to baseline (Class 3: CHG >= 15)
Loss of 10 letters or fewer relative to baseline (Class 3: CHG >= -10).

Then, the following call will implement criterion variable/flag pairs for the endpoints above. The CRITx variables will automatically encode the correct inequality. Note that that restrict_derivation() is wrapped around the call so as to only derive the variables for the relevant parameters. In this way, the filter argument can be altered to restrict derivation to only relevant records. Note also that the argument crit_var = exprs(CHG) has to be specified so that the criterion flags are derived with respect to the correct variable.

adbcva <- adbcva %>% restrict_derivation(
  derivation = derive_var_bcvacritxfl,
  args = params(
    crit_var = exprs(CHG),
    bcva_ranges = list(c(5, 10)),
    bcva_uplims = list(25, -5),
    bcva_lowlims = list(15, -10)
  ),
  filter = PARAMCD %in% c("SBCVA", "FBCVA")
)

The resulting output is shown below (limited to the first patient only):

It is also possible to assign significance to the “x” in CRITxFL. For instance, one could designate all criterion flags of Class 1 as CRIT1yFL, Class 2 as CRIT2yFL, and Class 3 as CRIT3yFL. The argument critxfl_index allows a simple implementation of this in conjunction with three separate calls to derive_var_bcvacritxfl():

adbcva <- adbcva %>%
  restrict_derivation(
    derivation = derive_var_bcvacritxfl,
    args = params(
      crit_var = exprs(CHG),
      bcva_ranges = list(c(5, 10)),
      critxfl_index = 10
    ),
    filter = PARAMCD %in% c("SBCVA", "FBCVA")
  ) %>%
  restrict_derivation(
    derivation = derive_var_bcvacritxfl,
    args = params(
      crit_var = exprs(CHG),
      bcva_uplims = list(25, -5),
      critxfl_index = 20
    ),
    filter = PARAMCD %in% c("SBCVA", "FBCVA")
  ) %>%
  restrict_derivation(
    derivation = derive_var_bcvacritxfl,
    args = params(
      crit_var = exprs(CHG),
      bcva_lowlims = list(15, -10),
      critxfl_index = 30
    ),
    filter = PARAMCD %in% c("SBCVA", "FBCVA")
  )

Additional Notes

When interpreting endpoints such as Loss of 5 letters or fewer relative to baseline, it is implicitly assumed in this article that this also includes the case where letters are gained, so that the inequality reads CHG >= -5. One would then use the bcva_lowlims = list(-5) argument of derive_var_bcvacritxfl() to program such an endpoint. If this is not the case, i.e. one wishes to exclude cases of letter gains, then the inequality of interest would instead be -5 <= CHG <= -1. Importantly, derive_var_bcvacritxfl() could still be used, but with the argument bcva_ranges = list(c(-5, -1)).
This vignette extensively showcases the use of derive_var_bcvacritxfl() applied to the variable CHG, but through the argument crit_var the function can also be used to create criterion flag relative to other variables (e.g. crit_var = exprs(AVAL) for AVAL).

Example Script

ADaM	Sample Code
ADBCVA	ad_adbcva.R