Missing Values in Tern
In tern: Create Common TLGs Used in Clinical Trials

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The packages used in this vignette are:

library(rtables)
library(formatters)
library(tern)
library(dplyr)

Variable Class Conversion

rtables requires that split variables to be factors. When you try and split a variable that isn't, a warning message will appear. Here we purposefully convert the SEX variable to character to demonstrate what happens when we try splitting the rows by this variable. To fix this, df_explict_na will convert this to a factor resulting in the table being generated.

adsl <- tern_ex_adsl
adsl$SEX <- as.character(adsl$SEX)

vars <- c("AGE", "SEX", "RACE", "BMRKR1")
var_labels <- c(
  "Age (yr)",
  "Sex",
  "Race",
  "Continous Level Biomarker 1"
)

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)
result

Including Missing Values in `rtables`

Here we purposefully convert all M values to NA in the SEX variable. After running df_explicit_na the NA values are encoded as <Missing> but they are not included in the table. As well, the missing values are not included in the n count and they are not included in the denominator value for calculating the percent values.

adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl)

vars <- c("AGE", "SEX")
var_labels <- c(
  "Age (yr)",
  "Sex"
)

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)
result

If you want the Na values to be displayed in the table and included in the n count and as the denominator for calculating percent values, use the na_level argument.

adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl, na_level = "Missing Values")

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)
result

Missing Values in Numeric Variables

Numeric variables that have missing values are not altered. This means that any NA value in a numeric variable will not be included in the summary statistics, nor will they be included in the denominator value for calculating the percent values. Here we make any value less than 30 missing in the AGE variable and only the valued greater than 30 are included in the table below.

adsl <- tern_ex_adsl
adsl$AGE[adsl$AGE < 30] <- NA
adsl <- df_explicit_na(adsl)

vars <- c("AGE", "SEX")
var_labels <- c(
  "Age (yr)",
  "Sex"
)

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)
result