Validate clinical metadata

Usage

validateClinicalMetadata(
  x,
  required_cols = c("subject_id", "visit_id", "scale_name", "scale_score"),
  allowed_source_system = c("EDC", "EHR", "paper_crf"),
  allowed_assessor_role = c("PT", "OT", "MD", "RN", "researcher"),
  strict = FALSE
)

Arguments

x: Data frame created from EDC/EHR exports.
required_cols: Required columns that must exist and be non-missing.
allowed_source_system: Allowed values for source_system when present.
allowed_assessor_role: Allowed values for assessor_role when present.
strict: Logical; stop when validation fails.

Value

A list with validation details and an overall valid flag.

References

Goldberger AL, et al. (2000). "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals." Circulation, 101(23), e215-e220. doi:10.1161/01.CIR.101.23.e215

Examples

df <- data.frame(
  subject_id = "S01",
  visit_id = "V01",
  scale_name = "FIM",
  scale_score = 88,
  assessment_date = "2026-01-01",
  stringsAsFactors = FALSE
)
validateClinicalMetadata(df)
#> $valid
#> [1] TRUE
#> 
#> $missing_columns
#> character(0)
#> 
#> $missing_required_rows
#> integer(0)
#> 
#> $invalid_date_rows
#> integer(0)
#> 
#> $invalid_scale_score_rows
#> integer(0)
#> 
#> $invalid_source_system_rows
#> integer(0)
#> 
#> $invalid_assessor_role_rows
#> integer(0)
#> 
#> $duplicate_rows
#> integer(0)
#>