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Derive LOCF (Last Observation Carried Forward) Records

Source: R/derive_locf_records.R
derive_locf_records.Rd

Adds LOCF records as new observations for each 'by group' when the dataset does not contain observations for missed visits/time points and when analysis value is missing.

Usage 

derive_locf_records(
  dataset,
  dataset_ref,
  by_vars,
  id_vars_ref = NULL,
  analysis_var = AVAL,
  imputation = "add",
  order,
  keep_vars = NULL
)

Arguments

dataset

Input dataset

The variables specified by the by_vars, analysis_var, order, and keep_vars arguments are expected to be in the dataset.

Default value

none

dataset_ref

Expected observations dataset

Data frame with all the combinations of PARAMCD, PARAM, AVISIT, AVISITN, ... which are expected in the dataset is expected.

Default value

none

by_vars

Grouping variables

For each group defined by by_vars those observations from dataset_ref are added to the output dataset which do not have a corresponding observation in the input dataset or for which analysis_var is NA for the corresponding observation in the input dataset.

Default value

none

id_vars_ref

Grouping variables in expected observations dataset

The variables to group by in dataset_ref when determining which observations should be added to the input dataset.

Default value

All the variables in dataset_ref

analysis_var

Analysis variable.

Permitted values

a variable

Default value

AVAL

imputation

Select the mode of imputation:

add: Keep all original records and add imputed records for missing timepoints and missing analysis_var values from dataset_ref.

update: Update records with missing analysis_var and add imputed records for missing timepoints from dataset_ref.

update_add: Keep all original records, update records with missing analysis_var and add imputed records for missing timepoints from dataset_ref.

Permitted values

One of these 3 values: "add", "update", "update_add"

Default value

"add"

order

Sort order

The dataset is sorted by order before carrying the last observation forward (e.g. AVAL) within each by_vars.

For handling of NAs in sorting variables see the "Sort Order" section in vignette("generic").

Default value

none

keep_vars

Variables that need carrying the last observation forward

Keep variables that need carrying the last observation forward other than analysis_var (e.g., PARAMN, VISITNUM). If by default NULL, only variables specified in by_vars and analysis_var will be populated in the newly created records.

Default value

NULL

Value

The input dataset with the new "LOCF" observations added for each by_vars, based on the value passed to the imputation argument.

Details

For each group (with respect to the variables specified for the by_vars parameter) those observations from dataset_ref are added to the output dataset

  • which do not have a corresponding observation in the input dataset or

  • for which analysis_var is NA for the corresponding observation in the input dataset.

    For the new observations, analysis_var is set to the non-missing analysis_var of the previous observation in the input dataset (when sorted by order) and DTYPE is set to "LOCF".

    The imputation argument decides whether to update the existing observation when analysis_var is NA ("update" and "update_add"), or to add a new observation from dataset_ref instead ("add").

See also

BDS-Findings Functions for adding Parameters/Records: default_qtc_paramcd(), derive_expected_records(), derive_extreme_event(), derive_extreme_records(), derive_param_bmi(), derive_param_bsa(), derive_param_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Author

G Gayatri

Examples

Add records for missing analysis variable using reference dataset

Imputed records should be added for missing timepoints and for missing analysis_var (from dataset_ref), while retaining all original records.

  • The reference dataset for the imputed records is specified by the dataset_add argument. It should contain all expected combinations of variables. In this case, advs_expected_obsv is created by crossing() datasets paramcd and avisit, which includes all combinations of PARAMCD, AVISITN, and AVISIT.

  • The groups for which new records are added are specified by the by_vars argument. Here, one record should be added for each subject and parameter. Therefore, by_vars = exprs(STUDYID, USUBJID, PARAMCD) is specified.

  • The imputation method is specified using the imputation argument. In this case, records with missing analysis values add records from dataset_ref after the data are sorted by the variables in by_vars and by visit (AVISITN and AVISIT), as specified in the order argument.

  • Variables other than analysis_var and by_vars that require LOCF (Last-Observation- Carried-Forward handling (in this case, PARAMN) are specified in the keep_vars argument.

library(dplyr)
library(tibble)
library(tidyr)

advs <- tribble(
  ~STUDYID,  ~USUBJID,      ~VSSEQ, ~PARAMCD, ~PARAMN, ~AVAL, ~AVISITN, ~AVISIT,
  "CDISC01", "01-701-1015",      1, "PULSE",        1,    65,        0, "BASELINE",
  "CDISC01", "01-701-1015",      2, "DIABP",        2,    79,        0, "BASELINE",
  "CDISC01", "01-701-1015",      3, "DIABP",        2,    80,        2, "WEEK 2",
  "CDISC01", "01-701-1015",      4, "DIABP",        2,    NA,        4, "WEEK 4",
  "CDISC01", "01-701-1015",      5, "DIABP",        2,    NA,        6, "WEEK 6",
  "CDISC01", "01-701-1015",      6, "SYSBP",        3,   130,        0, "BASELINE",
  "CDISC01", "01-701-1015",      7, "SYSBP",        3,   132,        2, "WEEK 2"
)

paramcd <- tribble(
~PARAMCD,
"PULSE",
"DIABP",
"SYSBP"
)

avisit <- tribble(
~AVISITN, ~AVISIT,
0, "BASELINE",
2, "WEEK 2",
4, "WEEK 4",
6, "WEEK 6"
)

advs_expected_obsv <- paramcd %>%
crossing(avisit)

derive_locf_records(
  dataset = advs,
  dataset_ref = advs_expected_obsv,
  by_vars = exprs(STUDYID, USUBJID, PARAMCD),
  imputation = "add",
  order = exprs(AVISITN, AVISIT),
  keep_vars = exprs(PARAMN)
) |>
  arrange(USUBJID, PARAMCD, AVISIT)
#> # A tibble: 14 × 9
#>    STUDYID USUBJID     VSSEQ PARAMCD PARAMN  AVAL AVISITN AVISIT   DTYPE
#>    <chr>   <chr>       <dbl> <chr>    <dbl> <dbl>   <dbl> <chr>    <chr>
#>  1 CDISC01 01-701-1015     2 DIABP        2    79       0 BASELINE <NA>
#>  2 CDISC01 01-701-1015     3 DIABP        2    80       2 WEEK 2   <NA>
#>  3 CDISC01 01-701-1015    NA DIABP        2    80       4 WEEK 4   LOCF
#>  4 CDISC01 01-701-1015     4 DIABP        2    NA       4 WEEK 4   <NA>
#>  5 CDISC01 01-701-1015    NA DIABP        2    80       6 WEEK 6   LOCF
#>  6 CDISC01 01-701-1015     5 DIABP        2    NA       6 WEEK 6   <NA>
#>  7 CDISC01 01-701-1015     1 PULSE        1    65       0 BASELINE <NA>
#>  8 CDISC01 01-701-1015    NA PULSE        1    65       2 WEEK 2   LOCF
#>  9 CDISC01 01-701-1015    NA PULSE        1    65       4 WEEK 4   LOCF
#> 10 CDISC01 01-701-1015    NA PULSE        1    65       6 WEEK 6   LOCF
#> 11 CDISC01 01-701-1015     6 SYSBP        3   130       0 BASELINE <NA>
#> 12 CDISC01 01-701-1015     7 SYSBP        3   132       2 WEEK 2   <NA>
#> 13 CDISC01 01-701-1015    NA SYSBP        3   132       4 WEEK 4   LOCF
#> 14 CDISC01 01-701-1015    NA SYSBP        3   132       6 WEEK 6   LOCF

Update records for missing analysis variable

When the imputation mode is set to update, missing analysis_var values are updated using values from the last record after the dataset is sorted by by_vars and order. Imputed records are added for missing timepoints (from dataset_ref).

derive_locf_records(
  dataset = advs,
  dataset_ref = advs_expected_obsv,
  by_vars = exprs(STUDYID, USUBJID, PARAMCD),
  imputation = "update",
  order = exprs(AVISITN, AVISIT),
) |>
  arrange(USUBJID, PARAMCD, AVISIT)
#> # A tibble: 12 × 9
#>    STUDYID USUBJID     VSSEQ PARAMCD PARAMN  AVAL AVISITN AVISIT   DTYPE
#>    <chr>   <chr>       <dbl> <chr>    <dbl> <dbl>   <dbl> <chr>    <chr>
#>  1 CDISC01 01-701-1015     2 DIABP        2    79       0 BASELINE <NA>
#>  2 CDISC01 01-701-1015     3 DIABP        2    80       2 WEEK 2   <NA>
#>  3 CDISC01 01-701-1015     4 DIABP        2    80       4 WEEK 4   LOCF
#>  4 CDISC01 01-701-1015     5 DIABP        2    80       6 WEEK 6   LOCF
#>  5 CDISC01 01-701-1015     1 PULSE        1    65       0 BASELINE <NA>
#>  6 CDISC01 01-701-1015    NA PULSE       NA    65       2 WEEK 2   LOCF
#>  7 CDISC01 01-701-1015    NA PULSE       NA    65       4 WEEK 4   LOCF
#>  8 CDISC01 01-701-1015    NA PULSE       NA    65       6 WEEK 6   LOCF
#>  9 CDISC01 01-701-1015     6 SYSBP        3   130       0 BASELINE <NA>
#> 10 CDISC01 01-701-1015     7 SYSBP        3   132       2 WEEK 2   <NA>
#> 11 CDISC01 01-701-1015    NA SYSBP       NA   132       4 WEEK 4   LOCF
#> 12 CDISC01 01-701-1015    NA SYSBP       NA   132       6 WEEK 6   LOCF

Update records for missing analysis variable while keeping the original records

When the imputation mode is set to update_add, the missing analysis_var values are updated using values from the last record after the dataset is sorted by by_vars and order. The updated values are added as new records, while the original records with missing analysis_var are retained. Imputed records are added for missing timepoints (from dataset_ref).

derive_locf_records(
  dataset = advs,
  dataset_ref = advs_expected_obsv,
  by_vars = exprs(STUDYID, USUBJID, PARAMCD),
  imputation = "update_add",
  order = exprs(AVISITN, AVISIT),
) |>
  arrange(USUBJID, PARAMCD, AVISIT)
#> # A tibble: 14 × 9
#>    STUDYID USUBJID     VSSEQ PARAMCD PARAMN  AVAL AVISITN AVISIT   DTYPE
#>    <chr>   <chr>       <dbl> <chr>    <dbl> <dbl>   <dbl> <chr>    <chr>
#>  1 CDISC01 01-701-1015     2 DIABP        2    79       0 BASELINE <NA>
#>  2 CDISC01 01-701-1015     3 DIABP        2    80       2 WEEK 2   <NA>
#>  3 CDISC01 01-701-1015     4 DIABP        2    80       4 WEEK 4   LOCF
#>  4 CDISC01 01-701-1015     4 DIABP        2    NA       4 WEEK 4   <NA>
#>  5 CDISC01 01-701-1015     5 DIABP        2    80       6 WEEK 6   LOCF
#>  6 CDISC01 01-701-1015     5 DIABP        2    NA       6 WEEK 6   <NA>
#>  7 CDISC01 01-701-1015     1 PULSE        1    65       0 BASELINE <NA>
#>  8 CDISC01 01-701-1015    NA PULSE       NA    65       2 WEEK 2   LOCF
#>  9 CDISC01 01-701-1015    NA PULSE       NA    65       4 WEEK 4   LOCF
#> 10 CDISC01 01-701-1015    NA PULSE       NA    65       6 WEEK 6   LOCF
#> 11 CDISC01 01-701-1015     6 SYSBP        3   130       0 BASELINE <NA>
#> 12 CDISC01 01-701-1015     7 SYSBP        3   132       2 WEEK 2   <NA>
#> 13 CDISC01 01-701-1015    NA SYSBP       NA   132       4 WEEK 4   LOCF
#> 14 CDISC01 01-701-1015    NA SYSBP       NA   132       6 WEEK 6   LOCF