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.
As the name ADBCVA implies, {admiralophtha}
suggests to
populate ADBCVA solely with BCVA records from the OE SDTM.
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.
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)
)
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.
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:
AVALCAxN
) to Snellen categories.AVAL
to a numeric
category.AVALCAxN
through a mutate statement using the
format function.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):
USUBJID | PARAMCD | AVAL | AVALCAT1 | AVALCA1N |
---|---|---|---|---|
01-701-1015 | FBCVA | 82 | 20/25 | 25 |
01-701-1015 | FBCVA | 77 | 20/32 | 32 |
01-701-1015 | FBCVA | 77 | 20/32 | 32 |
01-701-1015 | FBCVA | 64 | 20/50 | 50 |
01-701-1015 | FBCVA | 92 | 20/16 | 16 |
01-701-1015 | FBCVA | 41 | 20/160 | 160 |
01-701-1015 | FBCVA | 52 | 20/100 | 100 |
01-701-1015 | FBCVA | 2 | < 20/800 | 1000 |
01-701-1015 | FBCVA | 44 | 20/125 | 125 |
01-701-1015 | SBCVA | 97 | 20/12 | 12 |
{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:
CHG
value lying inside a range,
a <= CHG <= b
.CHG
value below an upper limit,
CHG <= a
.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:
5 <= CHG <= 10
)CHG <= 25
)CHG <= -5
)CHG >= 15
)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):
USUBJID | PARAMCD | AVAL | CHG | CRIT1 | CRIT1FL | CRIT2 | CRIT2FL | CRIT3 | CRIT3FL | CRIT4 | CRIT4FL | CRIT5 | CRIT5FL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
01-701-1015 | FBCVA | 82 | 5 | 5 <= CHG <= 10 | Y | CHG <= 25 | Y | CHG <= -5 | N | CHG >= 15 | N | CHG >= -10 | Y |
01-701-1015 | FBCVA | 77 | 0 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | N | CHG >= 15 | N | CHG >= -10 | Y |
01-701-1015 | FBCVA | 77 | 0 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | N | CHG >= 15 | N | CHG >= -10 | Y |
01-701-1015 | FBCVA | 64 | -13 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | Y | CHG >= 15 | N | CHG >= -10 | N |
01-701-1015 | FBCVA | 92 | 15 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | N | CHG >= 15 | Y | CHG >= -10 | Y |
01-701-1015 | FBCVA | 41 | -36 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | Y | CHG >= 15 | N | CHG >= -10 | N |
01-701-1015 | FBCVA | 52 | -25 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | Y | CHG >= 15 | N | CHG >= -10 | N |
01-701-1015 | FBCVA | 2 | -75 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | Y | CHG >= 15 | N | CHG >= -10 | N |
01-701-1015 | FBCVA | 44 | -33 | 5 <= CHG <= 10 | N | CHG <= 25 | Y | CHG <= -5 | Y | CHG >= 15 | N | CHG >= -10 | N |
01-701-1015 | SBCVA | 97 | 62 | 5 <= CHG <= 10 | N | CHG <= 25 | N | CHG <= -5 | N | CHG >= 15 | Y | CHG >= -10 | Y |
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")
)
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
).
ADaM | Sample Code |
---|---|
ADBCVA | ad_adbcva.R |