Identify Time-Varying Variables within Subjects — mStat_identify_time_varying

This function checks each specified adjustable variable to determine if it is time-varying within the same subject. If a variable is found to be time-varying for any subject, the function stops and reports which subjects caused this issue. If no variable is time-varying, a message confirms that all variables are consistent within subjects.

Usage

mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)

Arguments

meta.dat: A data frame containing the metadata where each row corresponds to an observation.
adj.vars: A character vector of names of the adjustable variables in meta.dat that need to be checked for time-variance within the same subject.
subject.var: The name of the variable in meta.dat that identifies the subject. This variable is used to group observations by subject.

Value

The function does not return a value. If it finds that an adjustable variable is time-varying within subjects, it stops and throws an error with a message specifying the variable and the problematic subjects. If all variables are non-time-varying within subjects, it outputs a message confirming this.

Examples

# Example 1: All variables are non-time-varying
meta.dat <- data.frame(
  subject = c(1, 1, 2, 2, 3, 3, 4, 4),
  age = c(30, 30, 25, 25, 40, 40, 35, 35),
  bmi = c(22, 22, 24, 24, 28, 28, 26, 26)
)
adj.vars <- c("age", "bmi")
subject.var <- "subject"
mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
#> $time_varying_vars
#> character(0)
#> 
#> $non_time_varying_vars
#> [1] "age" "bmi"
#> 

# Example 2: Missing variables in meta.dat
try({
  meta.dat <- data.frame(
    subject = c(1, 1, 2, 2, 3, 3, 4, 4),
    age = c(30, 30, 25, 25, 40, 40, 35, 35)
  )
  adj.vars <- c("age", "bmi")  # "bmi" is missing
  subject.var <- "subject"
  mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)
})
#> Error in mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var) : 
#>   The following required variables are missing in meta.dat: bmi.

# Example 3: One variable is time-varying
try({
  meta.dat <- data.frame(
    subject = c(1, 1, 2, 2, 3, 3, 4, 4),
    age = c(30, 31, 25, 25, 40, 40, 35, 35),
    bmi = c(22, 22, 24, 24, 28, 28, 26, 26)
  )
  adj.vars <- c("age", "bmi")
  subject.var <- "subject"
  mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)
})
#> $time_varying_vars
#> [1] "age"
#> 
#> $non_time_varying_vars
#> [1] "bmi"
#> 

# Example 4: Empty data set
try({
  meta.dat <- data.frame(subject = integer(0), age = integer(0), bmi = integer(0))
  adj.vars <- c("age", "bmi")
  subject.var <- "subject"
  mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)
})
#> $time_varying_vars
#> character(0)
#> 
#> $non_time_varying_vars
#> [1] "age" "bmi"
#> 

# Example 5: Mixed case with time-varying and non-time-varying variables
try({
  meta.dat <- data.frame(
    subject = c(1, 1, 2, 2, 3, 3, 4, 4),
    age = c(30, 31, 25, 26, 40, 41, 35, 36),
    bmi = c(22, 23, 24, 25, 28, 29, 26, 27)
  )
  adj.vars <- c("age", "bmi")
  subject.var <- "subject"
  mStat_identify_time_varying_vars(meta.dat, adj.vars, subject.var)
})
#> $time_varying_vars
#> [1] "age" "bmi"
#> 
#> $non_time_varying_vars
#> character(0)
#>