---
title: "Visit and Period Variables"
output:
  rmarkdown::html_vignette:
vignette: >
  %\VignetteIndexEntry{Visit and Period Variables}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Introduction

The derivation of visit variables like `AVISIT`, `AVISITN`, `AWLO`,
`AWHI`, ... or period, subperiod, or phase variables like `APERIOD`,
`TRT01A`, `TRT02A`, `ASPER`, `PHSDTM`, `PHEDTM`, ... is highly
study-specific. Therefore admiral cannot provide functions which derive
these variables. However, for common scenarios like visit assignments
based on time windows or deriving BDS period variables from ADSL period
variables, functions are provided which support those derivations.

## Required Packages

The examples of this vignette require the following packages.

```{r, warning=FALSE, message=FALSE}
library(admiral)
library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)
```

```{r, echo=FALSE, warning=FALSE, message=FALSE}
library(admiraldev)
```

# Visit variables (`AVISIT`, `AVISITN`, `AWLO`, `AWHI`, ...) {#visits}

The most common ways of deriving `AVISIT` and `AVISITN` are:

-   The variables are set to the collected visits (`VISIT` and
    `VISITNUM`).
-   The variables are set based on time windows.

The former can be achieved simply by calling `mutate()`, like in the
vignettes and the template scripts.

For the latter a (study-specific) reference dataset needs to be created
which provides for each visit the start and end day (`AWLO` and `AWHI`)
and the values of other visit related variables (`AVISIT`, `AVISITN`,
`AWTARGET`, ...).

```{r}
windows <- tribble(
  ~AVISIT,    ~AWLO, ~AWHI, ~AVISITN, ~AWTARGET,
  "BASELINE",   -30,     1,        0,         1,
  "WEEK 1",       2,     7,        1,         5,
  "WEEK 2",       8,    15,        2,        11,
  "WEEK 3",      16,    22,        3,        19,
  "WEEK 4",      23,    30,        4,        26
)
```

Then the visits can be assigned based on the analysis day (`ADY`) by
calling `derive_vars_joined()`:

```{r}
adbds <- tribble(
  ~USUBJID, ~ADY,
  "1",       -33,
  "1",        -2,
  "1",         3,
  "1",        24,
  "2",        NA,
)

derive_vars_joined(
  adbds,
  dataset_add = windows,
  filter_join = AWLO <= ADY & ADY <= AWHI
)
```

# Period, Subperiod, and Phase Variables

If periods, subperiods, or phases are used, the corresponding variables
have to be consistent across all datasets. This can be achieved by
defining the periods, subperiods, or phases once and then use this
definition for all datasets. The definition can be stored in ADSL or in
a separate dataset. In the following examples, this separate dataset is
called period reference dataset.

## Period Reference Dataset {#reference}

The period reference dataset contains one observation per subject and
period, subperiod, or phase. For example:

```{r echo=FALSE}
phase_ref <- tribble(
  ~USUBJID, ~APHASEN, ~PHSDT,       ~PHEDT,       ~APHASE,
  "1",             1, "2021-01-04", "2021-02-06", "TREATMENT",
  "1",             2, "2021-02-07", "2021-03-07", "FUP",
  "2",             1, "2021-02-02", "2021-03-02", "TREATMENT"
) %>%
  mutate(
    STUDYID = "xyz",
    APHASEN = as.integer(APHASEN),
    across(matches("PH[ES]DT"), ymd)
  ) %>%
  select(STUDYID, everything())

phase_ref
```

The admiral functions expect separate datasets for periods, subperiods,
and phases. For periods the numeric variable `APERIOD` is expected, for
subperiods the numeric variables `APERIOD` and `ASPER`, and for phases
the numeric variable `APHASEN`.

## Creating ADSL Period, Subperiod, or Phase Variables {#periods_adsl}

If a period reference dataset is available, the ADSL variables for
periods, subperiods, or phases can be created from this dataset by
calling `derive_vars_period()`.

For example the period reference dataset from the previous section can
be used to add the phase variables (`PHwSDT`, `PHwEDT`, and `APHASEw`)
to ADSL:

```{r}
adsl <- tibble(STUDYID = "xyz", USUBJID = c("1", "2"))

adsl <- derive_vars_period(
  adsl,
  dataset_ref = phase_ref,
  new_vars = exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE)
) %>%
  select(STUDYID, USUBJID, PH1SDT, PH1EDT, PH2SDT, PH2EDT, APHASE1, APHASE2)

adsl
```

## Creating BDS and OCCDS Period, Subperiod, or Phase Variables {#periods_bds}

If a period reference dataset is available, BDS and OCCDS variables for
periods, subperiods, or phases can be created by calling
`derive_vars_joined()`.

For example the variables `APHASEN`, `PHSDT`, `PHEDT`, `APHASE` can be
derived from the period reference dataset defined above.

```{r}
adae <- tribble(
  ~USUBJID, ~ASTDT,
  "1",      "2021-01-01",
  "1",      "2021-01-05",
  "1",      "2021-02-05",
  "1",      "2021-03-05",
  "1",      "2021-04-05",
  "2",      "2021-02-15",
) %>%
  mutate(ASTDT = ymd(ASTDT))

derive_vars_joined(
  adae,
  dataset_add = phase_ref,
  by_vars = exprs(USUBJID),
  filter_join = PHSDT <= ASTDT & ASTDT <= PHEDT
)
```

If no period reference dataset is available but period variables are in
ADSL, the period reference dataset can be created from ADSL by calling
`create_period_dataset()`.

For [example]{#adsl_example}, a period reference dataset for phases can
be created from the ADSL dataset created above:

```{r}
create_period_dataset(
  adsl,
  new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw)
)
```

# Treatment Variables (`TRTxxP`, `TRTxxA`, `TRTP`, `TRTA`, ...)

In studies with multiple periods the treatment can differ by period,
e.g. for a crossover trial. CDISC defines variables for planned and
actual treatments in ADSL (`TRTxxP`, `TRTxxA`, `TRxxPGy`, `TRxxAGy`,
...) and corresponding variables in BDS and OCCDS datasets (`TRTP`,
`TRTA`, `TRTPGy`, `TRTAGy`, ...). They can be derived in the same way
(and same step) as the period, subperiod, and phase variables.

## Creating ADSL Treatment Variables {#treatment_adsl}

If the treatment information is included in the period reference
dataset, the treatment ADSL variables can be created by calling
`derive_vars_period()`:

```{r}
# Add period variables to ADSL
period_ref <- tribble(
  ~USUBJID, ~APERIOD, ~APERSDT,     ~APEREDT,     ~TRTA,
  "1",             1, "2021-01-04", "2021-02-06", "DRUG A",
  "1",             2, "2021-02-07", "2021-03-07", "DRUG B",
  "2",             1, "2021-02-02", "2021-03-02", "DRUG B",
  "2",             2, "2021-03-03", "2021-04-01", "DRUG B"
) %>%
  mutate(
    STUDYID = "xyz",
    APERIOD = as.integer(APERIOD),
    across(ends_with("DT"), ymd)
  )

adsl <- derive_vars_period(
  adsl,
  dataset_ref = period_ref,
  new_vars = exprs(
    APxxSDT = APERSDT,
    APxxEDT = APEREDT,
    TRTxxA = TRTA
  )
) %>%
  select(
    STUDYID, USUBJID,
    TRT01A, TRT02A,
    AP01SDT, AP01EDT, AP02SDT, AP02EDT
  )

adsl
```

## Creating BDS and OCCDS Treatment Variables {#treatment_bds}

If a period reference dataset is available, BDS and OCCDS variables for
treatment can be created by calling `derive_vars_joined()`.

For example the variables `APERIOD` and `TRTA` can be derived from the
period reference dataset defined above.

```{r}
adae <- tribble(
  ~USUBJID, ~ASTDT,
  "1",      "2021-01-05",
  "1",      "2021-02-05",
  "1",      "2021-03-05",
  "1",      "2021-04-05",
  "2",      "2021-02-15",
  "2",      "2021-03-10",
) %>%
  mutate(
    ASTDT = ymd(ASTDT),
    STUDYID = "xyz"
  )

derive_vars_joined(
  adae,
  dataset_add = period_ref,
  by_vars = exprs(STUDYID, USUBJID),
  new_vars = exprs(APERIOD, TRTA),
  join_vars = exprs(APERSDT, APEREDT),
  filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT
)
```

If no period reference dataset is available but period variables are in
ADSL, the period reference dataset can be created from ADSL by calling
`create_period_dataset()`.

For example, a period reference dataset for periods and treatments can
be created from the ADSL dataset created above:

```{r}
create_period_dataset(
  adsl,
  new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA)
)
```

# Study Specific Code

At some point study specific code is required to derive period/subperiod
variables. There are two options:

-   Study specific code is used to derive the variables `PxxSwSDT` and
    `PxxSwEDT` in ADSL. Then `create_period_dataset()` and
    `derive_vars_joined()` can be used to derive period/subperiod
    variables like `ASPER` or `ASPRSDT` in BDS and OCCDS datasets. (See
    [example](#adsl_example).)

-   Study specific code is used to derive a dataset with one
    observations per patient, period, and subperiod (see [period
    reference dataset](#reference) ). Then `derive_vars_period()` can be
    used to derive `PxxSwSDT` and `PxxSwEDT` in ADSL and
    `derive_vars_joined()` can be used to derive period/subperiod
    variables like `ASPER` or `ASPRSDT` in BDS and OCCDS datasets.

It depends on the specific definition of the periods/subperiods which
option works best. If the definition is based on other ADSL variables,
the first option would work best. If the definition is based on
vertically structured data like exposure data (EX dataset), the second
option should be used.
