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Standardize data

Source: R/hb_data.R
hb_data.Rd

Standardize a tidy input dataset.

Usage

hb_data(
  data,
  response,
  study,
  study_reference,
  group,
  group_reference,
  patient,
  covariates
)

Arguments

data

A tidy data frame or tibble with the data.

response

Character of length 1, name of the column in data with the response/outcome variable. data[[response]] must be a continuous variable, and it should be the change from baseline of a clinical endpoint of interest, as opposed to just the raw response. Treatment differences are computed directly from this scale, please supply change from baseline unless you are absolutely certain that treatment differences computed directly from this quantity are clinically meaningful.

study

Character of length 1, name of the column in data with the study ID.

study_reference

Atomic of length 1, element of the study column that indicates the current study. (The other studies are historical studies.)

group

Character of length 1, name of the column in data with the group ID.

group_reference

Atomic of length 1, element of the group column that indicates the control group. (The other groups may be treatment groups.)

patient

Character of length 1, name of the column in data with the patient ID.

covariates

Character vector of column names in data with the columns with baseline covariates. These can be continuous, categorical, or binary. Regardless, historicalborrow derives the appropriate model matrix.

Value

A standardized tidy data frame with one row per patient and the following columns:

  • response: continuous response/outcome variable. (Should be change from baseline of an outcome of interest.)

  • study_label: human-readable label of the study.

  • study: integer study index with the max index equal to the current study (at study_reference).

  • group_label: human-readable group label (e.g. treatment arm name).

  • group: integer group index with an index of 1 equal to the control group (at group_reference).

  • patient_label: original patient ID.

  • patient: integer patient index.

  • covariate_*: baseline covariate columns.

Details

Users do not normally need to call this function. It mainly serves exposes the indexing behavior of studies and group levels to aid in interpreting summary tables.

Examples

data <- hb_sim_independent(n_continuous = 1, n_study = 2)$data
data <- dplyr::select(
  data,
  study,
  group,
  patient,
  response,
  tidyselect::everything()
)
colnames(data) <- c("trial", "arm", "subject", "change", "cov1", "cov2")
data$trial <- paste0("trial", data$trial)
data$arm <- paste0("arm", data$arm)
hb_data(
  data = data,
  response = "change",
  study = "trial",
  study_reference = "trial1",
  group = "arm",
  group_reference = "arm1",
  patient = "subject",
  covariates = c("cov1", "cov2")
)
#> # A tibble: 400 × 9
#>    response study_label group_label patient_label study group patient
#>       <dbl> <chr>       <chr>               <int> <int> <int>   <int>
#>  1    1.91  trial2      arm1                  101     1     1     101
#>  2    3.27  trial2      arm1                  102     1     1     102
#>  3    3.04  trial2      arm1                  103     1     1     103
#>  4    2.01  trial2      arm1                  104     1     1     104
#>  5    1.61  trial2      arm1                  105     1     1     105
#>  6    0.912 trial2      arm1                  106     1     1     106
#>  7    0.885 trial2      arm1                  107     1     1     107
#>  8    1.33  trial2      arm1                  108     1     1     108
#>  9    1.04  trial2      arm1                  109     1     1     109
#> 10   -1.81  trial2      arm1                  110     1     1     110
#> # ℹ 390 more rows
#> # ℹ 2 more variables: covariate_cov1 <dbl>, covariate_cov2 <dbl>