irecist.Rmd

---
title: "Creating ADRS with iRECIST endpoints"
output: 
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Creating ADRS with iRECIST endpoints}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

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

library(admiraldev)
```

# Introduction

This article describes creating an `ADRS` ADaM with oncology endpoint parameters
based on iRECIST. It shows a similar way of deriving the endpoints presented in 
[Creating ADRS (Including Non-standard Endpoints)](adrs.html). Most of the endpoints 
are derived by calling `admiral::derive_extreme_event()`.

This vignette follows the iRECIST guidelines, for more information user may visit
https://recist.eortc.org/irecist/

Examples are currently presented and tested using `ADSL` (ADaM) and
`RS` (SDTM) inputs. However, other domains could be used. The `RS` test data
contains iRECIST response for target, non-target and overall response. Further 
pre-processing and considerations may be needed if iRECIST are only collected 
after RECIST 1.1 progression and input data contains multiple response criteria. 
The functions and workflow could similarly be used to create an intermediary 
`ADEVENT` ADaM.

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

# Programming Workflow

-   [Read in Data](#readdata)
-   [Pre-processing of Input Records](#input)
-   [Derive Confirmed Progressive Disease Parameter](#pd)
-   [Derive Response Parameter](#rsp)
-   [Derive Clinical Benefit Parameter](#cb)
-   [Derive Best Overall Response Parameter](#bor)
-   [Derive Response Parameters requiring Confirmation](#confirm)
-   [Other Endpoints](#Other)

## Read in Data {#readdata}

To start, all data frames needed for the creation of `ADRS` should be read into
the environment. This will be a company specific process. Some of the data
frames needed may be `ADSL`, `RS` and `TU`. For this vignette we assume that
`RS` provides the response values `"iCR"`, `"iPR"`, `"iSD"`,
`"NON-iCR/NON-iUPD"`, `"iUPD"`, `"iCPD"`, and `"NE"`. All examples can be easily
modified to consider other response values (see [Handling Different Input
Response Values](#different_resp_vals)).

For example purpose, the SDTM and ADaM datasets (based on CDISC Pilot
test data)---which are included in `{pharmaversesdtm}` and `{pharmaverseadam}`---are used.

```{r message=FALSE}
library(admiral)
library(admiralonco)
library(dplyr)
library(pharmaverseadam)
library(pharmaversesdtm)
library(lubridate)
library(stringr)
data("adsl")
# iRECIST oncology sdtm data
data("rs_onco_irecist")

rs <- rs_onco_irecist

rs <- convert_blanks_to_na(rs)
```

```{r echo=FALSE}
# select subjects from adsl such that there is one subject without RS data
rs_subjects <- unique(rs$USUBJID)
adsl_subjects <- unique(adsl$USUBJID)
adsl <- filter(
  adsl,
  USUBJID %in% union(rs_subjects, setdiff(adsl_subjects, rs_subjects)[1])
)
```

At this step, it may be useful to join `ADSL` to your `RS` domain. Only
the `ADSL` variables used for derivations are selected at this step. The
rest of the relevant `ADSL` would be added later.

```{r eval=TRUE}
adsl_vars <- exprs(RANDDT)
adrs <- derive_vars_merged(
  rs,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = exprs(STUDYID, USUBJID)
)
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, RSTESTCD, RSDTC, VISIT, RANDDT),
  filter = RSTESTCD == "OVRLRESP"
)
```

## Pre-processing of Input Records {#input}

The first step involves company-specific pre-processing of records for
the required input to the downstream parameter functions. Note that this
could be needed multiple times (e.g. once for investigator and once for
Independent Review Facility (IRF)/Blinded Independent Central Review
(BICR) records). It could even involve merging input data from other
sources besides `RS`, such as `ADTR`.

This step would include any required selection/derivation of `ADT` or applying
any necessary partial date imputations, updating `AVAL` (e.g. this should be
ordered from best to worst response), and setting analysis flag `ANL01FL`.
Common options for `ANL01FL` would be to set null for invalid assessments or
those occurring after new anti-cancer therapy, or to only flag assessments on or
after date of first treatment/randomization, or rules to cover the case when a
patient has multiple observations per visit (e.g. by selecting the worst value).
Another consideration could be extra potential protocol-specific sources of
Progressive Disease such as radiological assessments, which could be
pre-processed here to create a PD record to feed downstream derivations.

For the derivation of the parameters it is expected that the subject
identifier variables (usually `STUDYID` and `USUBJID`) and `ADT` are a
unique key. This can be achieved by deriving an analysis flag
(`ANLzzFL`). See [Derive `ANL01FL`](#anl01fl) for an example.

The below shows an example of a possible company-specific implementation
of this step.

### Select Overall Response Records and Set Parameter Details

In this case we use the overall response records from `RS` from the
investigator as our starting point. The parameter details such as
`PARAMCD`, `PARAM` etc will always be company-specific, but an example
is shown below so that you can trace through how these records feed into
the other parameter derivations.

```{r}
adrs <- adrs %>%
  filter(RSEVAL == "INVESTIGATOR" & RSTESTCD == "OVRLRESP") %>%
  mutate(
    PARAMCD = "OVR",
    PARAM = "Overall Response by Investigator",
    PARCAT1 = "Tumor Response",
    PARCAT2 = "Investigator",
    PARCAT3 = "iRECIST"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, VISIT, RSTESTCD, RSEVAL, PARAMCD, PARAM, PARCAT1, PARCAT2, PARCAT3)
)
```

### Partial Date Imputation and Deriving `ADT`, `ADTF`, `AVISIT` etc

If your data collection allows for partial dates, you could apply a
company-specific imputation rule at this stage when deriving `ADT`. For
this example, here we impute missing day to last possible date.

```{r}
adrs <- adrs %>%
  derive_vars_dt(
    dtc = RSDTC,
    new_vars_prefix = "A",
    highest_imputation = "D",
    date_imputation = "last"
  ) %>%
  mutate(AVISIT = VISIT)
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, RSSTRESC, RSDTC, ADT, ADTF)
)
```

### Derive `AVALC` and `AVAL`

Here we populate `AVALC` and create the numeric version as `AVAL`
(ordered from worst to best response, followed by `NE` and MISSING). The `AVAL` values are not considered in
the parameter derivations below, and so changing `AVAL` here would not change
the result of those derivations. However, please note that the ordering of `AVAL` 
will be used to determine `ANL01FL` in the subsequent step, ensure that the appropriate 
`mode` is being set in the `admiral::derive_var_extreme_flag()`. 

iRECIST ordering will be used or if you'd like to provide your own company-specific ordering here you
could do this as follows:

```{r}
aval_resp_new <- function(arg) {
  case_when(
    arg == "NE" ~ 8,
    arg == "MISSING" ~ 7,
    arg == "iCR" ~ 6,
    arg == "iPR" ~ 5,
    arg == "iSD" ~ 4,
    arg == "NON-iCR/NON-iUPD" ~ 3,
    arg == "iUPD" ~ 2,
    arg == "iCPD" ~ 1,
    TRUE ~ NA_real_
  )
}

adrs <- adrs %>%
  mutate(
    AVALC = RSSTRESC,
    AVAL = aval_resp_new(AVALC)
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, RSSTRESC, AVALC, AVAL)
)
```

### Flag Worst Assessment at Each Date (`ANL01FL`) {#anl01fl}

When deriving `ANL01FL` this is an opportunity to exclude any records
that should not contribute to any downstream parameter derivations. In
the below example this includes only selecting valid assessments and
those occurring on or after randomization date. If there is more than
one assessment at a date, the worst one is flagged.

```{r}
adrs <- adrs %>%
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = exprs(STUDYID, USUBJID, ADT),
      order = exprs(AVAL, RSSEQ),
      new_var = ANL01FL,
      mode = "first"
    ),
    filter = !is.na(AVAL) & AVALC != "MISSING" & ADT >= RANDDT
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, RANDDT, ANL01FL)
)
```

Here is an alternative example where those records occurring after new
anti-cancer therapy are additionally excluded (where `NACTDT` would be
pre-derived as first date of new anti-cancer therapy. See `{admiralonco}`
[Creating and Using New Anti-Cancer Start Date](nactdt.html) for deriving this
variable).

```{r, eval=FALSE}
adrs <- adrs %>%
  mutate(
    ANL01FL = case_when(
      !is.na(AVAL) & ADT >= RANDDT & ADT < NACTDT ~ "Y",
      TRUE ~ NA_character_
    )
  )
```

### Flag Assessments up to First iCPD (`ANL02FL`) {#anl02fl}

To restrict response data up to and including first reported progressive disease
`ANL02FL` flag could be created by using `{admiral}` function
`admiral::derive_var_relative_flag()`.

```{r}
adrs <- adrs %>%
  derive_var_relative_flag(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(ADT, RSSEQ),
    new_var = ANL02FL,
    condition = AVALC == "iCPD",
    mode = "first",
    selection = "before",
    inclusive = TRUE
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, AVALC, ADT, ANL01FL, ANL02FL)
)
```

### Select Source Assessments for Parameter derivations

For most parameter derivations the post-baseline overall response assessments up
to and including first iCPD are considered.
```{r}
ovr <- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y")
```

```{r, echo=FALSE}
dataset_vignette(
  ovr,
  display_vars = exprs(USUBJID, AVISIT, AVALC, ADT, RANDDT)
)
```

## Define Events

The building blocks for the events that contribute to deriving common endpoints
like what constitutes a responder, or a Best Overall Response of complete
response (CR), ... are predefined in admiralonco for RECIST 1.1 (see [Pre-Defined
Response Event Objects](../reference/event_objects.html)). New Response Event Objects 
are needed for iRECIST and any study-specific needs. 

```{r}
icpd_y <- event_joined(
  description = paste(
    "Define confirmed progressive disease (iCPD) as",
    "iUPD followed by iCPD with only other iUPD and NE responses in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join == "iCPD",
  condition = AVALC == "iUPD" &
    all(AVALC.join %in% c("iCPD", "iUPD", "NE")),
  set_values_to = exprs(AVALC = "Y")
)

iupd_y <- event_joined(
  description = paste(
    "Define unconfirmed progressive disease (iUPD) as",
    "iUPD followed only by other iUPD or NE responses"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "all",
  condition = ADT <= ADT.join & AVALC == "iUPD" & all(AVALC.join %in% c("iUPD", "NE")),
  set_values_to = exprs(AVALC = "Y")
)

no_data_n <- event(
  description = "Define no response for all patients in adsl (should be used as last event)",
  dataset_name = "adsl",
  condition = TRUE,
  set_values_to = exprs(AVALC = "N"),
  keep_source_vars = adsl_vars
)

no_data_missing <- event(
  description = paste(
    "Define missing response (MISSING) for all patients in adsl (should be used",
    "as last event)"
  ),
  dataset_name = "adsl",
  condition = TRUE,
  set_values_to = exprs(AVALC = "MISSING"),
  keep_source_vars = adsl_vars
)
```

### Handling Different Input Response Values {#different_resp_vals}
If `RS` contains other response values than the iRECIST responses, the `event()`
and `event_joined()` can be adjusted to cover this scenario. For example, if
RECIST responses (`"CR"`, `"PR"`, `"SD"`, ...) are collected up to first PD and
iRECIST responses (`"iCR"`, `"iPR"`, `"iSD"`, ...) thereafter, the `event()`
object defining unconfirmed response can be adjusted in the following way.
```
irsp_y <- event(
  description = "Define CR, iCR, PR, or iPR as (unconfirmed) response",
  dataset_name = "ovr",
  condition = AVALC %in% c("CR", "iCR", "PR", "iPR"),
  set_values_to = exprs(AVALC = "Y")
)

```

## Derive Confirmed and Unconfirmed Progressive Disease Parameter {#pd}

Now that we have the input records prepared above with any
company-specific requirements, we can start to derive new parameter
records. For the parameter derivations, all values except those
overwritten by `set_values_to` argument are kept from the earliest
occurring input record fulfilling the required criteria.

When an `iCPD` occurs, the date of progression would be the first occurrence of `iUPD` in that block.
For example, when we have values of `iUPD`, `iUPD`, and `iCPD`, the iRECIST `PD` date would
be the first occurrence of `iUPD`. In cases where we have `SD`, `SD`, `iUPD`, `PR`, `PR`, `iUPD`, and `iCPD`,
the iRECIST `PD` date would be the second occurrence of `iUPD`.

The function `admiral::derive_extreme_records()`, in conjunction with the event `icpd_y`, 
could be used to find the date of the first `iUPD`.

For the Unconfirmed Progressive Disease Parameter, it can be of interest to look at `iUPD` that has
never been confirmed and no subsequent `iSD`, `iPR` or `iCR` has been observed.

```{r}
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(ADT),
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(icpd_y, no_data_n),
    set_values_to = exprs(
      PARAMCD = "ICPD",
      PARAM = "iRECIST Confirmation of Disease Progression by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

ovr_orig <- ovr
ovr <- ovr %>%
  group_by(USUBJID) %>%
  filter(ADT >= max_cond(var = ADT, cond = AVALC == "iUPD")) %>%
  ungroup(USUBJID)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(ADT),
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(iupd_y, no_data_n),
    set_values_to = exprs(
      PARAMCD = "IUPD",
      PARAM = "iRECIST Unconfirmed Disease Progression by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )
ovr <- ovr_orig
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, ANL01FL),
  filter = PARAMCD %in% c("ICPD", "IUPD")
)
```

For progressive disease and response shown in steps here and below, in our 
examples we show these as `ADRS` parameters, but they could equally be 
achieved via `ADSL` dates or `ADEVENT` parameters.If you prefer to store 
as an ADSL date, then the function `admiral::derive_var_extreme_dt()` 
could be used to find the date of first `iCPD` as a variable, rather than 
as a new parameter record.

## Derive Response Parameter {#rsp}

The function `admiral::derive_extreme_event()` can then be used to find the date
of first response. In the below example, the response condition has been defined
as `iCR` or `iPR` via the event `irsp_y` that was created for iRECIST.

```{r}
irsp_y <- event(
  description = "Define iCR or iPR as (unconfirmed) response",
  dataset_name = "ovr",
  condition = AVALC %in% c("iCR", "iPR"),
  set_values_to = exprs(AVALC = "Y")
)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(ADT),
    mode = "first",
    events = list(irsp_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "IRSP",
      PARAM = "iRECIST Response by Investigator (confirmation not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, ANL01FL),
  filter = PARAMCD == "IRSP"
)
```

## Derive Clinical Benefit Parameter {#cb}

The function `admiral::derive_extreme_event()` can then be used to derive the
clinical benefit parameter, which we define as a patient having had a response
or a sustained period of time before first `iUPD`. This could also be known as
disease control. In this example the "sustained period" has been defined as 42
days after randomization date via the created `icb_y` event.

```{r}
icb_y <- event(
  description = paste(
    "Define iCR, iPR, iSD, or NON-iCR/NON-iUPD occuring at least 42 days after",
    "randomization as clinical benefit"
  ),
  dataset_name = "ovr",
  condition = AVALC %in% c("iCR", "iPR", "iSD", "NON-iCR/NON-iUPD") &
    ADT >= RANDDT + 42,
  set_values_to = exprs(AVALC = "Y")
)
```

Please note that the result `AVALC = "Y"` is defined by the first _two_ events
specified for `events`. For subjects with observations fulfilling both events
the one with the earlier date should be selected (and not the first one in the
list). Thus `ignore_event_order` and `tmp_event_nr_var` are not specified.

```{r}
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(desc(AVALC), ADT),
    mode = "first",
    events = list(irsp_y, icb_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "ICB",
      PARAM = "iRECIST Clinical Benefit by Investigator (confirmation for response not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    ),
    check_type = "none"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, RANDDT, ANL01FL),
  filter = PARAMCD == "ICB"
)
```

## Derive Best Overall Response Parameter {#bor}

The function `admiral::derive_extreme_event()` can be used to derive the best
overall response (without confirmation required) parameter. Similar to the above
function you can optionally decide what period would you consider an `iSD` or
`NON-iCR/NON-iUPD` as being eligible from. In this example, 42 days after
randomization date has been used again.

Please note that the order of the events specified for `events` is important.
For example, a subject with `iPR`, `iPR`, `iCR` qualifies for both `ibor_icr` and
`ibor_ipr`. As `ibor_icr` is listed before `ibor_ipr`, `iCR` is selected as best overall
response for this subject.

```{r}
ibor_icr <- event(
  description = "Define complete response (iCR) for best overall response (iBOR)",
  dataset_name = "ovr",
  condition = AVALC == "iCR",
  set_values_to = exprs(AVALC = "iCR")
)

ibor_ipr <- event(
  description = "Define partial response (iPR) for best overall response (iBOR)",
  dataset_name = "ovr",
  condition = AVALC == "iPR",
  set_values_to = exprs(AVALC = "iPR")
)

ibor_isd <- event(
  description = paste(
    "Define stable disease (iSD) for best overall response (iBOR) as iCR, iPR, or iSD",
    "occurring at least 42 days after randomization"
  ),
  dataset_name = "ovr",
  condition = AVALC %in% c("iCR", "iPR", "iSD") & ADT >= RANDDT + 42,
  set_values_to = exprs(AVALC = "iSD")
)

ibor_non_icriupd <- event(
  description = paste(
    "Define NON-iCR/NON-iUPD for best overall response (iBOR) as NON-iCR/NON-iUPD",
    "occuring at least 42 days after randomization"
  ),
  dataset_name = "ovr",
  condition = AVALC == "NON-iCR/NON-iUPD" & ADT >= RANDDT + 42,
  set_values_to = exprs(AVALC = "NON-iCR/NON-iUPD")
)


ibor_icpd <- event_joined(
  description = paste(
    "Define confirmed progressive disease (iCPD) for best overall response (iBOR) as",
    "iUPD followed by iCPD with only other iUPD and NE responses in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join == "iCPD",
  condition = AVALC == "iUPD" &
    all(AVALC.join %in% c("iCPD", "iUPD", "NE")),
  set_values_to = exprs(AVALC = "iCPD")
)


ibor_iupd <- event(
  description = "Define unconfirmed progressive disease (iUPD) for best overall response (iBOR)",
  dataset_name = "ovr",
  condition = AVALC == "iUPD",
  set_values_to = exprs(AVALC = "iUPD")
)

ibor_ne <- event(
  description = paste(
    "Define not evaluable (NE) for best overall response (iBOR) as iCR, iPR, iSD,",
    "NON-iCR/NON-iUPD, or NE (should be specified after ibor_isd and ibor_non_icriupd)"
  ),
  dataset_name = "ovr",
  condition = AVALC %in% c("iCR", "iPR", "iSD", "NON-iCR/NON-iUPD", "NE"),
  set_values_to = exprs(AVALC = "NE")
)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADT),
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(ibor_icr, ibor_ipr, ibor_isd, ibor_non_icriupd, ibor_icpd, ibor_iupd, ibor_ne, no_data_missing),
    set_values_to = exprs(
      PARAMCD = "IBOR",
      PARAM = "iRECIST Best Overall Response by Investigator (confirmation not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = aval_resp_new(AVALC),
      ANL01FL = "Y"
    )
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, RANDDT, ANL01FL),
  filter = PARAMCD == "IBOR"
)
```

## Derive Response Parameters requiring Confirmation {#confirm}

Any of the above response parameters can be repeated for "confirmed" responses
only. For these the function `admiral::derive_extreme_event()` can be used with
different events. Some of the other functions from above can then be re-used
passing in these confirmed response records. See the examples below of derived
parameters requiring confirmation. The assessment and the confirmatory
assessment here need to occur at least 28 days apart *(without any +1 applied to
this calculation of days between visits)*, using the `icrsp_y_cr`,
`icrsp_y_ipr`, `icbor_icr`, and `icbor_ipr` event. Here the confirmation period
and the `keep_source_vars` argument is updated, as well as the `first_cond_upper` and 
`condition` for the iRECIST values.

```{r }
confirmation_period <- 28

icrsp_y_icr <- event_joined(
  description = paste(
    "Define confirmed response as iCR followed by iCR at least",
    confirmation_period,
    "days later and at most one NE in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  order = exprs(ADT),
  first_cond_upper = AVALC.join == "iCR" &
    ADT.join >= ADT + days(confirmation_period),
  condition = AVALC == "iCR" &
    all(AVALC.join %in% c("iCR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1,
  set_values_to = exprs(AVALC = "Y")
)

icrsp_y_ipr <- event_joined(
  description = paste(
    "Define confirmed response as iPR followed by iCR or iPR at least",
    confirmation_period,
    "days later at most one NE in between, and no iPR after iCR"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  order = exprs(ADT),
  first_cond_upper = AVALC.join %in% c("iCR", "iPR") &
    ADT.join >= ADT + days(confirmation_period),
  condition = AVALC == "iPR" &
    all(AVALC.join %in% c("iCR", "iPR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1 &
    (
      min_cond(
        var = ADT.join,
        cond = AVALC.join == "iCR"
      ) > max_cond(var = ADT.join, cond = AVALC.join == "iPR") |
        count_vals(var = AVALC.join, val = "iCR") == 0 |
        count_vals(var = AVALC.join, val = "iPR") == 0
    ),
  set_values_to = exprs(AVALC = "Y")
)

icbor_icr <- event_joined(
  description = paste(
    "Define complete response (iCR) for confirmed best overall response (iCBOR) as",
    "iCR followed by iCR at least",
    confirmation_period,
    "days later and at most one NE in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join == "iCR" &
    ADT.join >= ADT + confirmation_period,
  condition = AVALC == "iCR" &
    all(AVALC.join %in% c("iCR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1,
  set_values_to = exprs(AVALC = "iCR")
)

icbor_ipr <- event_joined(
  description = paste(
    "Define partial response (iPR) for confirmed best overall response (iCBOR) as",
    "iPR followed by iCR or iPR at least",
    confirmation_period,
    "days later, at most one NE in between and no iPR after iCR"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join %in% c("iCR", "iPR") &
    ADT.join >= ADT + confirmation_period,
  condition = AVALC == "iPR" &
    all(AVALC.join %in% c("iCR", "iPR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1 &
    (
      min_cond(
        var = ADT.join,
        cond = AVALC.join == "iCR"
      ) > max_cond(var = ADT.join, cond = AVALC.join == "iPR") |
        count_vals(var = AVALC.join, val = "iCR") == 0 |
        count_vals(var = AVALC.join, val = "iPR") == 0
    ),
  set_values_to = exprs(AVALC = "iPR")
)
```

Please note that the result `AVALC = "Y"` for confirmed clinical benefit is
defined by the first _two_ events specified for `events`. For subjects with
observations fulfilling both events the one with the earlier date should be
selected (and not the first one in the list).

```{r}
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(desc(AVALC), ADT),
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(icrsp_y_icr, icrsp_y_ipr, no_data_n),
    set_values_to = exprs(
      PARAMCD = "ICRSP",
      PARAM = "iRECIST Confirmed Response by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(desc(AVALC), ADT),
    mode = "first",
    events = list(icrsp_y_icr, icrsp_y_ipr, icb_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "ICCB",
      PARAM = "iRECIST Confirmed Clinical Benefit by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    ),
    check_type = "none"
  )

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADT),
    mode = "first",
    events = list(icbor_icr, icbor_ipr, ibor_isd, ibor_non_icriupd, ibor_icpd, ibor_iupd, ibor_ne, no_data_missing),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "ICBOR",
      PARAM = "iRECIST Best Confirmed Overall Response by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "iRECIST",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  )
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
  adrs,
  display_vars = exprs(USUBJID, AVISIT, PARAMCD, PARAM, AVALC, ADT, RANDDT, ANL01FL),
  filter = PARAMCD %in% c("ICRSP", "ICCB", "ICBOR")
)
```
## Other Endpoints {#other}

The following parameters may also be added:

IBCP - iRECIST Best Overall Response of CR/PR by Investigator (confirmation not required)<br>
ICBCP - iRECIST Best Confirmed Overall Response of CR/PR by Investigator<br>
IOVRB - iRECIST Overall Response by BICR<br>
ILSTA - iRECIST Last Disease Assessment by Investigator<br>
IMDIS - iRECIST Measurable Disease at Baseline by Investigator

For examples on the additional endpoints, please see [Creating ADRS (Including Non-standard Endpoints)](adrs.html).