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Prepare longitudinal data for PDRobust analyses

Source: R/DataStandard.R
DataStandard.Rd

Converts supported binary values to 0 and 1, replaces subject IDs and analysis times with consecutive integers, sorts the data by subject and time, and stores the information needed by other PDRobust functions.

Usage

DataStandard(data, mapping, drop = FALSE)

Arguments

data

A long-format data frame.

mapping

A pd_mapping object returned by Mapping().

drop

If TRUE, remove rows that cannot be assigned to a subject and remove subjects with missing visits or required values between baseline and cutoff. The returned report records what was removed. If FALSE, these problems stop standardization.

Value

A data frame inheriting from pd_data, retaining the input column names and additional unmapped columns. Rows outside the mapped time window are removed; retained rows are sorted by recoded ID and time. Attributes are:

pd_mapping

The mapping updated to use the standardized baseline and cutoff times. Column names remain unchanged.

pd_original_mapping

The mapping supplied for the raw data.

pd_check

The final pd_data_check report, including attrition. A warning is issued if the returned data are not ready for analysis, for example if deletion removes one treatment group.

pd_standardization

A list containing time_map, id_map, attrition, and initial_check. The maps link raw values to their standardized values; the initial check describes the input data.

The function checks the data both before and after preparation. The stored reports describe this call and are not recalculated if the returned data are later edited or subsetted; see pd_methods. The ID map has one row per retained subject, so its size increases with the number of subjects. Analysis values retain full precision; only summaries shown to users and percentages describing removed data are rounded.

Examples

data("BiSample", package = "PDRobust")
map <- Mapping(
  id = "id", time = "time", treatment = "A",
  survival = "S", outcome = "Y",
  baseline_time = 0, cutoff_time = 2,
  covariates = c("X1", "X2", "X4"),
  interest_vars = c("X1", "X2"), y_type = "B"
)
pd_dat <- DataStandard(BiSample, map)
attr(pd_dat, "pd_mapping")
#> PDRobust data mapping and analysis settings.
#>   ID: id
#>   Time: time
#>   Treatment: A
#>   Survival: S
#>   Outcome: Y
#>   Baseline time: 0
#>   Cutoff time: 2
#>   Mapped covariates: X1, X2, X4
#>   Interest variables: X1, X2
#>   Outcome type: B (binary)