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Closes #28 update advs vignette (#46)
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@Siddhesh2097
Siddhesh2097 authored Nov 1, 2024
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143 changes: 65 additions & 78 deletions vignettes/advs.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -108,11 +108,11 @@ One key addition in metabolic trials are vital sign parameters associated to bod
```{r, echo=TRUE, message=FALSE}
param_lookup <- tribble(
~VSTESTCD, ~PARAMCD, ~PARAM, ~PARAMN, ~PARCAT1, ~PARCAT1N,
"HEIGHT", "HEIGHT", "Height (cm)", 1, "Subject Characteristic", 1,
"WEIGHT", "WEIGHT", "Weight (kg)", 2, "Subject Characteristic", 1,
"BMI", "BMI", "Body Mass Index(kg/m^2)", 3, "Subject Characteristic", 1,
"HIPCIR", "HIPCIR", "Hip Circumference (cm)", 4, "Subject Characteristic", 1,
"WSTCIR", "WSTCIR", "Waist Circumference (cm)", 5, "Subject Characteristic", 1,
"HEIGHT", "HEIGHT", "Height (cm)", 1, "Anthropometric Measurement", 1,
"WEIGHT", "WEIGHT", "Weight (kg)", 2, "Anthropometric Measurement", 1,
"BMI", "BMI", "Body Mass Index(kg/m^2)", 3, "Anthropometric Measurement", 1,
"HIPCIR", "HIPCIR", "Hip Circumference (cm)", 4, "Anthropometric Measurement", 1,
"WSTCIR", "WSTCIR", "Waist Circumference (cm)", 5, "Anthropometric Measurement", 1,
"DIABP", "DIABP", "Diastolic Blood Pressure (mmHg)", 6, "Vital Sign", 2,
"PULSE", "PULSE", "Pulse Rate (beats/min)", 7, "Vital Sign", 2,
"SYSBP", "SYSBP", "Systolic Blood Pressure (mmHg)", 8, "Vital Sign", 2,
Expand Down Expand Up @@ -175,11 +175,10 @@ advs <- derive_param_bmi(
PARAMCD = "BMI",
PARAM = "Body Mass Index (kg/m^2)",
PARAMN = 3,
PARCAT1 = "Subject Characteristic",
PARCAT1 = "Anthropometric Measurement",
PARCAT1N = 1
),
get_unit_expr = VSSTRESU,
filter = VSSTAT != "NOT DONE" | is.na(VSSTAT),
constant_by_vars = exprs(USUBJID)
)
```
Expand Down Expand Up @@ -240,84 +239,66 @@ outlined below.
assigning `AVALCATy`/ `AVALCAvN` and `BASECATy`/ `BASECAvN` values.
Below is a simple example of how these values may be assigned:

For deriving categorization variables (`AVALCATx`, `BASECATx`)
`{admiral}` provides
[`derive_vars_cat()`](https://pharmaverse.github.io/admiral/dev/reference/derive_vars_cat.html) (see
documentation of the function for details).

```{r eval=TRUE}
avalcat_lookup <- tibble::tribble(
~PARAMCD, ~AVALCA1N, ~AVALCAT1,
"BMI", 1, "Underweight",
"BMI", 2, "Normal weight",
"BMI", 3, "Overweight",
"BMI", 4, "Obesity class I",
"BMI", 5, "Obesity class II",
"BMI", 6, "Obesity class III",
"BMI", NA, NA_character_
avalcat_lookup <- exprs(
~PARAMCD, ~condition, ~AVALCAT1, ~AVALCA1N,
"BMI", AVAL < 18.5, "Underweight", 1,
"BMI", AVAL >= 18.5 & AVAL < 25, "Normal weight", 2,
"BMI", AVAL >= 25 & AVAL < 30, "Overweight", 3,
"BMI", AVAL >= 30 & AVAL < 35, "Obesity class I", 4,
"BMI", AVAL >= 35 & AVAL < 40, "Obesity class II", 5,
"BMI", AVAL >= 40, "Obesity class III", 6,
"BMI", is.na(AVAL), NA_character_, NA_integer_
)
format_avalcat1n <- function(param, aval) {
case_when(
param == "BMI" & aval < 18.5 ~ 1,
param == "BMI" & aval >= 18.5 & aval < 25 ~ 2,
param == "BMI" & aval >= 25 & aval < 30 ~ 3,
param == "BMI" & aval >= 30 & aval < 35 ~ 4,
param == "BMI" & aval >= 35 & aval < 40 ~ 5,
param == "BMI" & aval >= 40 ~ 6,
TRUE ~ NA_real_
)
}
# Derive BMI class (AVALCAT1, AVALCA1N)
advs <- advs %>%
mutate(AVALCA1N = format_avalcat1n(param = PARAMCD, aval = AVAL)) %>%
derive_vars_merged(
avalcat_lookup,
by = exprs(PARAMCD, AVALCA1N)
derive_vars_cat(
definition = avalcat_lookup,
by_vars = exprs(PARAMCD)
)
```

```{r eval=TRUE, echo=FALSE}
dataset_vignette(
advs,
arrange(advs, USUBJID, PARAMCD, VISITNUM),
display_vars = exprs(USUBJID, PARAMCD, VISIT, AVAL, AVALCA1N, AVALCAT1),
filter = PARAMCD == "BMI"
filter = PARAMCD == "BMI" & USUBJID %in% c("01-701-1023", "01-701-1034")
)
```

In a similar way, we will create `BASECATy`/ `BASECAvN` variables.

```{r eval=TRUE}
basecat_lookup <- tibble::tribble(
~PARAMCD, ~BASECA1N, ~BASECAT1,
"BMI", 1, "Underweight",
"BMI", 2, "Normal weight",
"BMI", 3, "Overweight",
"BMI", 4, "Obesity class I",
"BMI", 5, "Obesity class II",
"BMI", 6, "Obesity class III",
"BMI", NA, NA_character_
basecat_lookup <- exprs(
~PARAMCD, ~condition, ~BASECAT1, ~BASECA1N,
"BMI", BASE < 18.5, "Underweight", 1,
"BMI", BASE >= 18.5 & BASE < 25, "Normal weight", 2,
"BMI", BASE >= 25 & BASE < 30, "Overweight", 3,
"BMI", BASE >= 30 & BASE < 35, "Obesity class I", 4,
"BMI", BASE >= 35 & BASE < 40, "Obesity class II", 5,
"BMI", BASE >= 40, "Obesity class III", 6,
"BMI", is.na(BASE), NA_character_, NA_integer_
)
format_basecat1n <- function(param, base) {
case_when(
param == "BMI" & base < 18.5 ~ 1,
param == "BMI" & base >= 18.5 & base < 25 ~ 2,
param == "BMI" & base >= 25 & base < 30 ~ 3,
param == "BMI" & base >= 30 & base < 35 ~ 4,
param == "BMI" & base >= 35 & base < 40 ~ 5,
param == "BMI" & base >= 40 ~ 6,
TRUE ~ NA_real_
)
}
# Derive baseline BMI class (BASECAT1, BASECA1N)
advs <- advs %>%
mutate(BASECA1N = format_basecat1n(param = PARAMCD, base = BASE)) %>%
derive_vars_merged(
basecat_lookup,
by = exprs(PARAMCD, BASECA1N)
derive_vars_cat(
definition = basecat_lookup,
by_vars = exprs(PARAMCD)
)
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
advs,
arrange(advs, USUBJID, PARAMCD, VISITNUM),
display_vars = exprs(USUBJID, PARAMCD, VISIT, AVAL, BASE, ABLFL, BASECA1N, BASECAT1),
filter = PARAMCD == "BMI"
filter = PARAMCD == "BMI" & USUBJID %in% c("01-701-1023", "01-701-1034")
)
```

Expand All @@ -329,34 +310,40 @@ For deriving criterion variables (`CRITy`, `CRITyFL`, `CRITyFLN`)
It ensures that they are derived in an ADaM-compliant way (see
documentation of the function for details).

In most cases the criterion depends on the parameter. In the following
example, the criterion flags for weight based on percentage change in
weight reduction from baseline is derived. Additional criterion flags
can be added as needed.
In most cases the criterion depends on the parameter and in this case the higher order function [`restrict_derivation()`](https://pharmaverse.github.io/admiral/dev/reference/restrict_derivation.html) can be useful.
In the following example, the criterion flags for weight based on percentage change in weight reduction from baseline is derived. Additional criterion flags can be added as needed.

```{r eval=TRUE}
advs <- advs %>%
derive_vars_crit_flag(
condition = PCHG <= -5 & PARAMCD == "WEIGHT",
description = "Achievement of ≥ 5% weight reduction from baseline",
crit_nr = 1,
values_yn = TRUE,
create_numeric_flag = FALSE
restrict_derivation(
derivation = derive_vars_crit_flag,
args = params(
condition = PCHG <= -5 & PARAMCD == "WEIGHT",
description = "Achievement of ≥ 5% weight reduction from baseline",
crit_nr = 1,
values_yn = TRUE,
create_numeric_flag = FALSE
),
filter = VISITNUM > 0 & PARAMCD == "WEIGHT"
) %>%
derive_vars_crit_flag(
condition = PCHG <= -10 & PARAMCD == "WEIGHT",
description = "Achievement of ≥ 10% weight reduction from baseline",
crit_nr = 2,
values_yn = TRUE,
create_numeric_flag = FALSE
restrict_derivation(
derivation = derive_vars_crit_flag,
args = params(
condition = PCHG <= -10 & PARAMCD == "WEIGHT",
description = "Achievement of ≥ 10% weight reduction from baseline",
crit_nr = 2,
values_yn = TRUE,
create_numeric_flag = FALSE
),
filter = VISITNUM > 0 & PARAMCD == "WEIGHT"
)
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
arrange(advs, USUBJID, VISITNUM, VSTPTNUM, PARAMCD),
display_vars = exprs(USUBJID, PARAMCD, PCHG, CRIT1, CRIT1FL, CRIT2, CRIT2FL),
filter = PARAMCD %in% c("WEIGHT")
display_vars = exprs(USUBJID, PARAMCD, PCHG, CRIT1, CRIT1FL, CRIT2, CRIT2FL, VISIT, VISITNUM),
filter = PARAMCD %in% c("WEIGHT") & USUBJID %in% c("01-701-1033", "01-701-1034") & VISITNUM %in% c(3, 10, 11, 12, 13)
)
```

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