--- title: LBT14 subtitle: Laboratory Test Results Shift Table -- Highest NCI CTCAE Grade Post-Baseline by Baseline NCI CTCAE Grade --- ```{r setup, echo = FALSE, warning = FALSE, message = FALSE} library(dplyr) library(tern) adsl <- random.cdisc.data::cadsl adlb <- random.cdisc.data::cadlb adsl <- df_explicit_na(adsl) adlb <- df_explicit_na(adlb) # Please note that in real clinical data, population flag like SAFFL, and parameter category like PARCAT2 needs to be # selected properly. adsl_f <- adsl %>% filter(SAFFL == "Y") adlb <- adlb %>% filter(SAFFL == "Y") ``` ```{r include = FALSE} webr_code_labels <- c("setup") ``` ## Output ## Standard Table (High) `WGRHIFL` ) must be selected appropriately in the pre-processing step. New grouping variables `ATOXGR_GP` (post-baseline) and `BTOXGR_GP` (baseline) are created to display the correct output.## {{< fa regular file-lines sm fw >}} Preview ```{r variant1, test = list(result_v1 = "result")} adlb_f <- adlb %>% filter(WGRHIFL == "Y") # Please note the step below can be skipped if you are using DTYPE PHANTOM adlb_out <- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c("WGRHIFL" = "Y")) # Create new grouping variables ATOXGR_GP, BTOXGR_GP adlb_out <- adlb_out %>% mutate( ATOXGR_GP = factor(case_when( ATOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", ATOXGR == 1 ~ "1", ATOXGR == 2 ~ "2", ATOXGR == 3 ~ "3", ATOXGR == 4 ~ "4", ATOXGR == "<Missing>" ~ "Missing" ), levels = c("Not High", "1", "2", "3", "4", "Missing")) ) %>% mutate( BTOXGR_GP = factor(case_when( BTOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", BTOXGR == 1 ~ "1", BTOXGR == 2 ~ "2", BTOXGR == 3 ~ "3", BTOXGR == 4 ~ "4", BTOXGR == "<Missing>" ~ "Missing" ), levels = c("Not High", "1", "2", "3", "4", "Missing")) ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", split_fun = drop_split_levels, label_pos = "topleft", split_label = "Parameter" ) %>% split_rows_by( "BTOXGR_GP", label_pos = "topleft", split_label = " Baseline NCI-CTCAE Grade", indent_mod = 2L ) %>% summarize_num_patients(var = "USUBJID", .stats = c("unique_count"), unique_count_suffix = FALSE) %>% count_occurrences_by_grade("ATOXGR_GP", denom = "n", drop = FALSE, .indent_mods = 3L) %>% append_topleft(" Post-baseline NCI-CTCAE Grade") %>% build_table(df = adlb_out, alt_counts_df = adsl_f) %>% prune_table() result ``` ```{r include = FALSE} webr_code_labels <- c("variant1") ``` ## Standard Table (Low) `WGRLOFL` ) must be selected appropriately in the pre-processing step. New grouping variables `ATOXGR_GP` (post-baseline) and `BTOXGR_GP` (baseline) are created to display the correct output.## {{< fa regular file-lines sm fw >}} Preview ```{r variant2, test = list(result_v2 = "result")} adlb_f <- adlb %>% filter(WGRLOFL == "Y") # Please note the step below can be skipped if you are using DTYPE PHANTOM adlb_out <- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c("WGRLOFL" = "Y")) # Create new grouping variables ATOXGR_GP, BTOXGR_GP adlb_out <- adlb_out %>% mutate( ATOXGR_GP = factor(case_when( ATOXGR %in% c(0, 1, 2, 3, 4) ~ "Not Low", ATOXGR == -1 ~ "1", ATOXGR == -2 ~ "2", ATOXGR == -3 ~ "3", ATOXGR == -4 ~ "4", ATOXGR == "<Missing>" ~ "Missing" ), levels = c("Not Low", "1", "2", "3", "4", "Missing")) ) %>% mutate( BTOXGR_GP = factor(case_when( BTOXGR %in% c(0, 1, 2, 3, 4) ~ "Not Low", BTOXGR == -1 ~ "1", BTOXGR == -2 ~ "2", BTOXGR == -3 ~ "3", BTOXGR == -4 ~ "4", BTOXGR == "<Missing>" ~ "Missing" ), levels = c("Not Low", "1", "2", "3", "4", "Missing")) ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", split_fun = drop_split_levels, label_pos = "topleft", split_label = "Parameter" ) %>% split_rows_by( "BTOXGR_GP", label_pos = "topleft", split_label = " Baseline NCI-CTCAE Grade", indent_mod = 2L ) %>% summarize_num_patients(var = "USUBJID", .stats = c("unique_count"), unique_count_suffix = FALSE) %>% count_occurrences_by_grade("ATOXGR_GP", denom = "n", drop = FALSE, .indent_mods = 3L) %>% append_topleft(" Post-baseline NCI-CTCAE Grade") %>% build_table(df = adlb_out, alt_counts_df = adsl_f) %>% prune_table() result ``` ```{r include = FALSE} webr_code_labels <- c("variant2") ``` ## Table Without Patients with <br/> Missing Baseline (High) ## {{< fa regular file-lines sm fw >}} Preview ```{r variant3, test = list(result_v3 = "result")} adlb_f <- adlb %>% filter(WGRHIFL == "Y") # Please note the step below can be skipped if you are using DTYPE PHANTOM adlb_out <- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c("WGRHIFL" = "Y")) # Create new grouping variables ATOXGR_GP, BTOXGR_GP adlb_out <- adlb_out %>% filter(BTOXGR != "<Missing>") %>% mutate( ATOXGR_GP = factor(case_when( ATOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", ATOXGR == 1 ~ "1", ATOXGR == 2 ~ "2", ATOXGR == 3 ~ "3", ATOXGR == 4 ~ "4", ATOXGR == "<Missing>" ~ "Missing" ), levels = c("Not High", "1", "2", "3", "4", "Missing")) ) %>% mutate( BTOXGR_GP = factor(case_when( BTOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", BTOXGR == 1 ~ "1", BTOXGR == 2 ~ "2", BTOXGR == 3 ~ "3", BTOXGR == 4 ~ "4" ), levels = c("Not High", "1", "2", "3", "4")) ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", split_fun = drop_split_levels, label_pos = "topleft", split_label = "Parameter" ) %>% split_rows_by( "BTOXGR_GP", label_pos = "topleft", split_label = " Baseline NCI-CTCAE Grade", indent_mod = 2L ) %>% summarize_num_patients(var = "USUBJID", .stats = c("unique_count"), unique_count_suffix = FALSE) %>% count_occurrences_by_grade("ATOXGR_GP", denom = "n", drop = FALSE, .indent_mods = 3L) %>% append_topleft(" Post-baseline NCI-CTCAE Grade") %>% build_table(df = adlb_out, alt_counts_df = adsl_f) %>% prune_table() result ``` ```{r include = FALSE} webr_code_labels <- c("variant3") ``` ## Table with Missing Baseline <br/> Considered as Grade 0 (Low) `BTOXGR_GP` now is `"Not Low"` instead of `"Missing"` compared to *Standard Table (Low)*.## {{< fa regular file-lines sm fw >}} Preview ```{r variant4, test = list(result_v4 = "result")} adlb_f <- adlb %>% filter(WGRLOFL == "Y") # Please note the step below can be skipped if you are using DTYPE PHANTOM adlb_out <- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c("WGRLOFL" = "Y")) # Create new grouping variables ATOXGR_GP, BTOXGR_GP adlb_out <- adlb_out %>% mutate( ATOXGR_GP = factor(case_when( ATOXGR %in% c(0, 1, 2, 3, 4) ~ "Not Low", ATOXGR == -1 ~ "1", ATOXGR == -2 ~ "2", ATOXGR == -3 ~ "3", ATOXGR == -4 ~ "4", ATOXGR == "<Missing>" ~ "Missing" ), levels = c("Not Low", "1", "2", "3", "4")) ) %>% mutate( BTOXGR_GP = factor(case_when( BTOXGR %in% c(0, 1, 2, 3, 4, "<Missing>") ~ "Not Low", BTOXGR == -1 ~ "1", BTOXGR == -2 ~ "2", BTOXGR == -3 ~ "3", BTOXGR == -4 ~ "4" ), levels = c("Not Low", "1", "2", "3", "4", "Missing")) ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", split_fun = drop_split_levels, label_pos = "topleft", split_label = "Parameter" ) %>% split_rows_by( "BTOXGR_GP", label_pos = "topleft", split_label = " Baseline NCI-CTCAE Grade", indent_mod = 2L ) %>% summarize_num_patients(var = "USUBJID", .stats = c("unique_count"), unique_count_suffix = FALSE) %>% count_occurrences_by_grade("ATOXGR_GP", denom = "n", drop = FALSE, .indent_mods = 3L) %>% append_topleft(" Post-baseline NCI-CTCAE Grade") %>% build_table(df = adlb_out, alt_counts_df = adsl_f) %>% prune_table() result ``` ```{r include = FALSE} webr_code_labels <- c("variant4") ``` ## Table with Fill-In of Grades `prune_table()` is not applied.## {{< fa regular file-lines sm fw >}} Preview ```{r variant5, test = list(result_v5 = "result")} adlb_f <- adlb %>% filter(WGRHIFL == "Y") # Please note the step below can be skipped if you are using DTYPE PHANTOM adlb_out <- h_adsl_adlb_merge_using_worst_flag(adsl_f, adlb_f, worst_flag = c("WGRHIFL" = "Y")) # Create new grouping variables ATOXGR_GP, BTOXGR_GP adlb_out <- adlb_out %>% mutate( ATOXGR_GP = factor(case_when( ATOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", ATOXGR == 1 ~ "1", ATOXGR == 2 ~ "2", ATOXGR == 3 ~ "3", ATOXGR == 4 ~ "4", ATOXGR == "<Missing>" ~ "Missing" ), levels = c("Not High", "1", "2", "3", "4", "Missing")) ) %>% mutate( BTOXGR_GP = factor(case_when( BTOXGR %in% c(0, -1, -2, -3, -4) ~ "Not High", BTOXGR == 1 ~ "1", BTOXGR == 2 ~ "2", BTOXGR == 3 ~ "3", BTOXGR == 4 ~ "4", BTOXGR == "<Missing>" ~ "Missing" ), levels = c("Not High", "1", "2", "3", "4", "Missing")) ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by("ACTARM") %>% split_rows_by( "PARAM", split_fun = drop_split_levels, label_pos = "topleft", split_label = "Parameter" ) %>% split_rows_by( "BTOXGR_GP", label_pos = "topleft", split_label = " Baseline NCI-CTCAE Grade", indent_mod = 2L ) %>% summarize_num_patients(var = "USUBJID", .stats = c("unique_count"), unique_count_suffix = FALSE) %>% count_occurrences_by_grade("ATOXGR_GP", denom = "n", drop = FALSE, .indent_mods = 3L) %>% append_topleft(" Post-baseline NCI-CTCAE Grade") %>% build_table(df = adlb_out, alt_counts_df = adsl_f) result ``` ```{r include = FALSE} webr_code_labels <- c("variant5") ``` ## Data Setup `adlb` dataset needs to be filtered on correct flags like `WGRLOFL` , `WGRHIFL` , et al., otherwise the layout function will not return the correct counts. There is an option to create a record for a lab test where no record is found at that visit. If you specified `add_derived_type = "PHANTOM"` & `dtype_phantom_cond` , you don't have to use the `h_adsl_adlb_merge_using_worst_flag` function to preprocess your `adlb` dataset. Otherwise please follow the pre-processing steps below before applying the layout functions.```{r setup} #| code-fold: show ``` ## `teal` App ## {{< fa regular file-lines fa-sm fa-fw >}} Preview ```{r teal, opts.label = c("skip_if_testing", "app")} library(teal.modules.clinical) ## Data reproducible code data <- teal_data() data <- within(data, { ADSL <- random.cdisc.data::cadsl ADLB <- random.cdisc.data::cadlb }) 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_shift_by_grade( label = "Grade Laboratory Abnormality Table", 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 = "ALT" ), worst_flag_var = choices_selected( choices = variable_choices(ADLB, subset = c("WGRLOVFL", "WGRLOFL", "WGRHIVFL", "WGRHIFL")), selected = c("WGRHIFL") ), worst_flag_indicator = choices_selected( value_choices(ADLB, "WGRLOVFL"), selected = "Y", fixed = TRUE ), anl_toxgrade_var = choices_selected( choices = variable_choices(ADLB, subset = c("ATOXGR")), selected = c("ATOXGR"), fixed = TRUE ), base_toxgrade_var = choices_selected( choices = variable_choices(ADLB, subset = c("BTOXGR")), selected = c("BTOXGR"), fixed = TRUE ), add_total = FALSE ) ), filter = teal_slices(teal_slice("ADSL", "SAFFL", selected = "Y")) ) shinyApp(app$ui, app$server) ``` 