README.Rmd

---
output: github_document
always_allow_html: true
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# dtlg

The goal of dtlg is to create pharmaceutical tables, listings and graphs for use at scale by utilising data.table as a back-end for processing. Currently only tables can be created but the functions should encompass a large number of possibilities for pharma tables.

The package was created using TLGcatalog to create examples.

Advance formatting options to follow in future.

## Installation

You can install the development version of dtlg from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("AscentSoftware/dtlg")
```

## Benchmarks

The following benchmarks compare dtlg with tern and use the TLGcatalog to get table code for tern.

#### DMT01

DMT01 was benchmarked using an ADSL table at 20 row intervals between 1,000 and 1,000,000 with a minimum of 10 iterations carried out for each interval. The results are as follows.

```{r, echo=FALSE,out.width="49%", out.height="20%",fig.cap="Min and Median benchmark times",fig.show='hold',fig.align='center'}

knitr::include_graphics(c("man/figures/DMT01_min.png","man/figures/DMT01_median.png"))

```

#### AET01

AET01 was benchmarked using an ADAEtable at 20 row intervals between 1,000 and 746,082 with a minimum of 10 iterations carried out for each interval. The ADSL table used for totals contained 20,000 rows. The results are as follows.

```{r, echo=FALSE,out.width="49%", out.height="20%",fig.cap="Min and Median benchmark times",fig.show='hold',fig.align='center'}

knitr::include_graphics(c("man/figures/AET01_min.png","man/figures/AET01_median.png"))
```

## Example

#### This example shows you how to create a demography table:

Carrying out data prep from TLG catalog DMT01

```{r example, echo=TRUE}
library(dtlg)
#Recreating data prep from TLG Catalog DMT01
adsl <- random.cdisc.data::cadsl
adsl <- adsl|>
  dplyr::mutate(adsl,
    SEX = factor(dplyr::case_when(
      SEX == "M" ~ "Male",
      SEX == "F" ~ "Female",
      SEX == "U" ~ "Unknown",
      SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
    )),
    AGEGR1 = factor(
      dplyr::case_when(
        dplyr::between(AGE, 18, 40) ~ "18-40",
        dplyr::between(AGE, 41, 64) ~ "41-64",
        AGE > 64 ~ ">=65"
      ),
      levels = c("18-40", "41-64", ">=65")
    ))

```

Creating DMT01

```{r}
vars <- c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BMRKR1")
var_labels <- c(
  "Age (yr)",
  "Age Group",
  "Sex",
  "Ethnicity",
  "Race",
  "Continous Level Biomarker 1"
)
DMT01<-summary_table(adsl, target = vars, treat = 'ARM', target_name = var_labels)
```

```{r echo=FALSE}
knitr::kable(DMT01) |>
  kableExtra::kable_styling() |>
  kableExtra::scroll_box(width = '100%', height = '500px')
```

#### This example shows how to create a labs table like LBT01

*Change from baseline is not currently available*

```{r echo=TRUE}
adlb <- random.cdisc.data::cadlb|>dplyr::filter(AVISIT != "SCREENING")
labs <- summary_table_by_targets(adlb, c('AVAL','CHG'), 'ARM', c('PARAM','AVISIT'))
```

```{r echo=FALSE}
knitr::kable(labs) |>
  kableExtra::kable_styling() |>
  kableExtra::scroll_box(width = '100%', height = '500px')
```

#### **This example shows how to create AET01**

Follow example in merge_table_lists to get the relevant tables created. This mirrors the set up in the TLGcatalog.

```{r echo=FALSE}
set.seed(99)
adae <- random.cdisc.data::cadae
adae <- adae |> dplyr::mutate(
    AEDECOD = formatters::with_label(as.character(AEDECOD), "Dictionary-Derived Term"),
    AESDTH = formatters::with_label(
        sample(c("N", "Y"), size = nrow(adae), replace = TRUE, prob = c(0.99, 0.01)),
        "Results in Death"
    ),
    AEACN = formatters::with_label(
        sample(
            c("DOSE NOT CHANGED", "DOSE INCREASED", "DRUG INTERRUPTED", "DRUG WITHDRAWN"),
            size = nrow(adae),
            replace = TRUE, prob = c(0.68, 0.02, 0.25, 0.05)
        ),
        "Action Taken with Study Treatment"
    ),
    FATAL = formatters::with_label(AESDTH == "Y", "AE with fatal outcome"),
    SEV = formatters::with_label(AESEV == "SEVERE", "Severe AE (at greatest intensity)"),
    SER = formatters::with_label(AESER == "Y", "Serious AE"),
    SERWD = formatters::with_label(AESER == "Y" & AEACN == "DRUG WITHDRAWN", "Serious AE leading to withdrawal from treatment"),
    SERDSM = formatters::with_label(
        AESER == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
        "Serious AE leading to dose modification/interruption"
    ),
    RELSER = formatters::with_label(AESER == "Y" & AEREL == "Y", "Related Serious AE"),
    WD = formatters::with_label(AEACN == "DRUG WITHDRAWN", "AE leading to withdrawal from treatment"),
    DSM = formatters::with_label(
        AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"), "AE leading to dose modification/interruption"
    ),
    REL = formatters::with_label(AEREL == "Y", "Related AE"),
    RELWD = formatters::with_label(AEREL == "Y" & AEACN == "DRUG WITHDRAWN", "Related AE leading to withdrawal from treatment"),
    RELDSM = formatters::with_label(
        AEREL == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
        "Related AE leading to dose modification/interruption"
    )) |> dplyr::filter(ANL01FL == "Y")
adsl <- random.cdisc.data::cadsl
```

```{r echo=TRUE}
a<-event_count(adsl,patient = "USUBJID",treat = "ARM", label = "Total number of deaths", .filters = "DTHFL == 'Y'", .total_dt = NULL)
b<-event_count(adsl,patient = "USUBJID",treat = "ARM", label = "Total number of patients withdrawn from study due to an AE", .filters = "DCSREAS == 'ADVERSE EVENT'", .total_dt = NULL)
d<-event_count(adae, patient = "USUBJID", treat = "ARM", label = "Total number of patients with at least one AE",.total_dt = adsl)

e <- total_events(adae, 'ARM', 'Total number of AEs')
aesi_vars <- c("FATAL", "SER", "SERWD", "SERDSM", "RELSER", "WD", "DSM", "REL", "RELWD", "RELDSM", "SEV")
f <- multi_event_true(adae, event_vars = aesi_vars, patient = "USUBJID", treat = "ARM", heading = "Total number of patients with at least one", .total_dt = adsl, indent = "  ")

AET01 <- merge_table_lists(list(d,e,a,b,f))

```

```{r echo=FALSE}
knitr::kable(AET01) |>
  kableExtra::kable_styling() |>
  kableExtra::scroll_box(width = '100%', height = '500px')
```