Standardize data

Standardize a tidy input dataset.

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.

See also

Other data: hb_s_tau()

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 patien…¹ study group patient covar…² covar…³
#>       <dbl> <chr>       <chr>          <int> <int> <int>   <int>   <dbl>   <dbl>
#>  1   -1.27  trial2      arm1             101     1     1     101       0   0.298
#>  2    0.495 trial2      arm1             102     1     1     102       0  -0.605
#>  3   -3.08  trial2      arm1             103     1     1     103       0   1.36 
#>  4   -1.38  trial2      arm1             104     1     1     104       0   0.519
#>  5   -1.19  trial2      arm1             105     1     1     105       0   0.234
#>  6   -2.72  trial2      arm1             106     1     1     106       0   1.16 
#>  7    0.167 trial2      arm1             107     1     1     107       0  -0.538
#>  8   -3.78  trial2      arm1             108     1     1     108       0   1.74 
#>  9    0.744 trial2      arm1             109     1     1     109       0  -1.01 
#> 10   -1.58  trial2      arm1             110     1     1     110       0   0.316
#> # … with 390 more rows, and abbreviated variable names ¹​patient_label,
#> #   ²​covariate_cov1, ³​covariate_cov2