Prepare Data for Sensitivity Analysis with Irregular Assessment Times

This function prepares the data for SensIAT analysis by transforming it into a format suitable for the SensIAT models.

Usage

prepare_SensIAT_data(
  data,
  id.var,
  time.var,
  outcome.var,
  End,
  add.terminal.observations = TRUE
)

Arguments

data: A data frame containing the data to be prepared.
id.var: The variable in data that identifies the subject.
time.var: The variable in data that identifies the time of the observation.
outcome.var: The variable in data that contains the outcome of interest.
End: The end time for the analysis. Observations with time greater than End will be filtered out.
add.terminal.observations: Logical indicating whether to add terminal observations to the data (TRUE), or terminal observations have already been added (FALSE).

Value

A data frame with the following transformations:

Data filtered to time less than or equal to End.
Observations are arranged by id.var and time.var.
Terminal observations added if add.terminal.observations is TRUE, with ..time.. set to End and ..outcome.. set to NA, if the subject has less observations than the maximum number of observations.
New variables created:
- ..id.. aliases id.var,
- ..time.. aliases time.var,
- ..outcome.. aliases outcome.var,
- ..visit_number.. is the visit number within each subject derived from time.var,
- ..prev_outcome.., i.e. lag-outcome, the outcome from the previous visit,
- ..prev_time.., i.e. lag-time, the time from the previous visit,
- ..delta_time.., the difference in time between the current and previous visit.

Examples


prepare_SensIAT_data( SensIAT_example_data, Subject_ID, Time, Outcome, 830)
#> # A tibble: 1,000 × 11
#>    ..id.. ..visit_number.. Subject_ID Visit  Time Outcome ..time.. ..outcome..
#>     <int>            <int>      <int> <dbl> <dbl>   <dbl>    <dbl>       <dbl>
#>  1      1                0          1     0     0   3            0       3    
#>  2      1                1          1     1   214   4.5        214       4.5  
#>  3      1                2          1     2   292   4.17       292       4.17 
#>  4      1                3          1     3   370   1.33       370       1.33 
#>  5      1                4          1     4   441   0.833      441       0.833
#>  6      2                0          2     0     0   3            0       3    
#>  7      2                1          2     1    72   0.5         72       0.5  
#>  8      2                2          2     2   181   2          181       2    
#>  9      2                3          2     3   297   1.5        297       1.5  
#> 10      2                4          2     4   366   1.83       366       1.83 
#> # ℹ 990 more rows
#> # ℹ 3 more variables: ..prev_outcome.. <dbl>, ..prev_time.. <dbl>,
#> #   ..delta_time.. <dbl>

exdata <- tibble::tibble(ID=rep(1:2, c(3,5)),
                         Time=c(0, 30, 60,
                                0, 30, 60, 90, 120),
                         Outcome=floor(runif(8, 1, 100)))

prepare_SensIAT_data(exdata, ID, Time, Outcome, 120)
#> # A tibble: 10 × 10
#>    ..id.. ..visit_number..    ID  Time Outcome ..time.. ..outcome..
#>     <int>            <int> <int> <dbl>   <dbl>    <dbl>       <dbl>
#>  1      1                0     1     0       6        0           6
#>  2      1                1     1    30      53       30          53
#>  3      1                2     1    60      69       60          69
#>  4      1                3     1   120      NA      120          NA
#>  5      1                4     1   120      NA      120          NA
#>  6      2                0     2     0      69        0          69
#>  7      2                1     2    30       4       30           4
#>  8      2                2     2    60      23       60          23
#>  9      2                3     2    90      30       90          30
#> 10      2                4     2   120      64      120          64
#> # ℹ 3 more variables: ..prev_outcome.. <dbl>, ..prev_time.. <dbl>,
#> #   ..delta_time.. <dbl>