--- title: LBT07 subtitle: Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline --- ------------------------------------------------------------------------ {{< include ../../_utils/envir_hook.qmd >}} ```{r setup, echo = FALSE, warning = FALSE, message = FALSE} library(tern) library(dplyr) library(forcats) adsl <- random.cdisc.data::cadsl adlb <- random.cdisc.data::cadlb adlb_labels <- var_labels(adlb) # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels. adsl <- df_explicit_na(adsl) adlb <- df_explicit_na(adlb) # Select worst post-baseline records. adlb_f <- adlb %>% filter(ATOXGR != "<Missing>") %>% filter(ONTRTFL == "Y") %>% filter(WGRLOFL == "Y" | WGRHIFL == "Y") var_labels(adlb_f) <- adlb_labels # Derive GRADE_DIR and GRADE_ANL to use in layout from ATOXGR adlb_f <- adlb_f %>% mutate( GRADE_DIR = factor( case_when( ATOXGR %in% c("-1", "-2", "-3", "-4") & .data$WGRLOFL == "Y" ~ "LOW", ATOXGR == "0" ~ "ZERO", ATOXGR %in% c("1", "2", "3", "4") & .data$WGRHIFL == "Y" ~ "HIGH", TRUE ~ "NONE" ), levels = c("LOW", "ZERO", "HIGH", "NONE") ), GRADE_ANL = forcats::fct_relevel( forcats::fct_recode(ATOXGR, `1` = "-1", `2` = "-2", `3` = "-3", `4` = "-4" ), c("0", "1", "2", "3", "4") ) ) %>% var_relabel( GRADE_DIR = "Direction of Abnormality", GRADE_ANL = "Analysis Grade" ) # Construct analysis map map <- expand.grid( PARAM = levels(adlb$PARAM), GRADE_DIR = c("LOW", "HIGH"), GRADE_ANL = as.character(1:4), stringsAsFactors = FALSE ) %>% arrange(PARAM, desc(GRADE_DIR), GRADE_ANL) ``` ```{r include = FALSE} webr_code_labels <- c("setup") ``` {{< include ../../_utils/webr_no_include.qmd >}} ## Output :::: panel-tabset ## Standard Table ::: {.panel-tabset .nav-justified group="webr"} ## {{< fa regular file-lines sm fw >}} Preview ```{r variant1, test = list(result_v1 = "result")} lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", label_pos = "topleft", split_label = obj_label(adlb_f$PARAM) ) %>% summarize_num_patients( var = "USUBJID", required = "ATOXGR", .stats = "unique_count" ) %>% split_rows_by( "GRADE_DIR", label_pos = "topleft", split_label = obj_label(adlb_f$GRADE_DIR), split_fun = trim_levels_to_map(map) ) %>% count_abnormal_by_worst_grade( var = "GRADE_ANL", variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR"), .indent_mods = 4L ) %>% append_topleft(" Highest NCI CTCAE Grade") result <- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl) result <- result %>% prune_table() result ``` ```{r include = FALSE} webr_code_labels <- c("variant1") ``` {{< include ../../_utils/webr.qmd >}} ::: ## Data Setup ```{r setup} #| code-fold: show ``` :::: {{< include ../../_utils/save_results.qmd >}} ## `teal` App ::: {.panel-tabset .nav-justified} ## {{< fa regular file-lines fa-sm fa-fw >}} Preview ```{r teal, opts.label = c("skip_if_testing", "app"), eval = packageVersion("teal.slice") > "0.5.1"} library(teal.modules.clinical) ## Data reproducible code data <- teal_data() data <- within(data, { library(dplyr) ADSL <- random.cdisc.data::cadsl ADLB <- random.cdisc.data::cadlb %>% filter(!AVISIT %in% c("SCREENING", "BASELINE")) }) datanames <- c("ADSL", "ADLB") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] ## Reusable Configuration For Modules ADSL <- data[["ADSL"]] ADLB <- data[["ADLB"]] ## Setup App app <- init( data = data, modules = modules( tm_t_abnormality_by_worst_grade( label = "Laboratory Test Results with Highest Grade Post-Baseline", dataname = "ADLB", arm_var = choices_selected( choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")), selected = "ARM" ), paramcd = choices_selected( choices = value_choices(ADLB, "PARAMCD", "PARAM"), selected = c("ALT", "CRP", "IGA") ), add_total = FALSE ) ), filter = ( teal_slices( teal_slice("ADSL", "SAFFL", selected = "Y"), teal_slice("ADLB", "ONTRTFL", selected = "Y") ) ) ) shinyApp(app$ui, app$server) ``` {{< include ../../_utils/shinylive.qmd >}} ::: {{< include ../../repro.qmd >}} 