Create a delta data.frame template

Usage

delta_template(imputations, delta = NULL, dlag = NULL, missing_only = TRUE)

Arguments

imputations

an imputation object as created by impute().

delta

NULL or a numeric vector. Determines the baseline amount of delta to be applied to each visit. See details. If a numeric vector it must have the same length as the number of unique visits in the original dataset.

dlag

NULL or a numeric vector. Determines the scaling to be applied to delta based upon which visit the ICE occurred on. See details. If a numeric vector it must have the same length as the number of unique visits in the original dataset.

missing_only

Logical, if TRUE then non-missing post-ICE data will have a delta value of 0 assigned. Note that the calculation (as described in the details section) is performed first and then overwritten with 0's at the end (i.e. the delta values for missing post-ICE visits will stay the same regardless of this option).

Details

To apply a delta adjustment the analyse() function expects a delta data.frame with 3 variables: vars$subjid, vars$visit and delta (where vars is the object supplied in the original call to draws() as created by the set_vars() function).

This function will return a data.frame with the aforementioned variables with one row per subject per visit. If the delta argument to this function is NULL then the delta column in the returned data.frame will be 0 for all observations. If the delta argument is not NULL then delta will be calculated separately for each subject as the accumulative sum of delta multiplied by the scaling coefficient dlag based upon how many visits after the subject's intercurrent event (ICE) the visit in question is. This is best illustrated with an example:

Let delta = c(5,6,7,8) and dlag=c(1,2,3,4) (i.e. assuming there are 4 visits) and lets say that the subject had an ICE on visit 2. The calculation would then be as follows:

v1  v2  v3  v4
--------------
 5   6   7   8  # delta assigned to each visit
 0   1   2   3  # lagged scaling starting from the first visit after the subjects ICE
--------------
 0   6  14  24  # delta * lagged scaling
--------------
 0   6  20  44  # accumulative sum of delta to be applied to each visit

That is to say the subject would have a delta offset of 0 applied for visit-1, 6 for visit-2, 20 for visit-3 and 44 for visit-4. As a comparison, lets say that the subject instead had their ICE on visit 3, the calculation would then be as follows:

v1  v2  v3  v4
--------------
 5   6   7   8  # delta assigned to each visit
 0   0   1   2  # lagged scaling starting from the first visit after the subjects ICE
--------------
 0   0   7  16  # delta * lagged scaling
--------------
 0   0   7  23  # accumulative sum of delta to be applied to each visit

In terms of practical usage, lets say that you wanted a delta of 5 to be used for all post ICE visits regardless of their proximity to the ICE visit. This can be achieved by setting delta = c(5,5,5,5) and dlag = c(1,0,0,0). For example lets say a subject had their ICE on visit-1, then the calculation would be as follows:

v1  v2  v3  v4
--------------
 5   5   5   5  # delta assigned to each visit
 1   0   0   0  # lagged scaling starting from the first visit after the subjects ICE
--------------
 5   0   0  0  # delta * lagged scaling
--------------
 5   5   5  5  # accumulative sum of delta to be applied to each visit

Another way of using these arguments is to set delta to be the difference in time between visits and dlag to be the amount of delta per unit of time. For example lets say that we have a visit on weeks 1, 5, 6 & 9 and that we want a delta of 3 to be applied for each week after an ICE. This can be achieved by setting delta = c(0,4,1,3) (the difference in weeks between each visit) and dlag = c(3, 3, 3, 3). For example lets say we have a subject who had their ICE on week-5 (i.e. visit-2) then the calculation would be:

v1  v2  v3  v4
--------------
 0   4   1   3  # delta assigned to each visit
 0   0   3   3  # lagged scaling starting from the first visit after the subjects ICE
--------------
 0   0   3   9  # delta * lagged scaling
--------------
 0   0   3  12  # accumulative sum of delta to be applied to each visit

i.e. on week-6 (1 week after the ICE) they have a delta of 3 and on week-9 (4 weeks after the ICE) they have a delta of 12.

Please note that this function also returns several utility variables so that the user can create their own custom logic for defining what delta should be set to. These additional variables include:

• is_mar - If the observation was missing would it be regarded as MAR? This variable is set to FALSE for observations that occurred after a non-MAR ICE, otherwise it is set to TRUE.

• is_missing - Is the outcome variable for this observation missing.

• is_post_ice - Does the observation occur after the patient's ICE as defined by the data_ice dataset supplied to draws().

• strategy - What imputation strategy was assigned to for this subject.

The design and implementation of this function is largely based upon the same functionality as implemented in the so called "five marcos" by James Roger. See Roger (2021).

References

Roger, James. Reference-based mi via multivariate normal rm (the “five macros” and miwithd), 2021. URL https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data.

See also

Examples

if (FALSE) { # \dontrun{
delta_template(imputeObj)
delta_template(imputeObj, delta = c(5,6,7,8), dlag = c(1,2,3,4))
} # }