Apply 1/3 or 1/2 imputation rule to data

imputation_rule(
  df,
  x_stats,
  stat,
  imp_rule,
  post = FALSE,
  avalcat_var = "AVALCAT1"
)

Arguments

df

(data.frame)
data set containing all analysis variables.

x_stats

(named list)
a named list of statistics, typically the results of s_summary().

stat

(string)
statistic to return the value/NA level of according to the imputation rule applied.

imp_rule

(string)
imputation rule setting. Set to "1/3" to implement 1/3 imputation rule or "1/2" to implement 1/2 imputation rule.

post

(flag)
whether the data corresponds to a post-dose time-point (defaults to FALSE). This parameter is only used when imp_rule is set to "1/3".

avalcat_var

(string)
name of variable that indicates whether a row in df corresponds to an analysis value in category "BLQ", "LTR", "<PCLLOQ", or none of the above (defaults to "AVALCAT1"). Variable avalcat_var must be present in df.

Value

A list containing statistic value (val) and NA level (na_str) that should be displayed according to the specified imputation rule.

See also

analyze_vars_in_cols() where this function can be implemented by setting the imp_rule argument.

Examples

set.seed(1)
df <- data.frame(
  AVAL = runif(50, 0, 1),
  AVALCAT1 = sample(c(1, "BLQ"), 50, replace = TRUE)
)
x_stats <- s_summary(df$AVAL)
imputation_rule(df, x_stats, "max", "1/3")
#> $val
#>       max 
#> 0.9919061 
#> 
#> $na_str
#> [1] "ND"
#> 
imputation_rule(df, x_stats, "geom_mean", "1/3")
#> $val
#> [1] NA
#> 
#> $na_str
#> [1] "NE"
#> 
imputation_rule(df, x_stats, "mean", "1/2")
#> $val
#> [1] NA
#> 
#> $na_str
#> [1] "ND"
#>