A deliberately imperfect continuous-outcome longitudinal data set derived
from a simulated continuous-outcome panel. The data mimic common issues
encountered in raw clinical data exports while remaining recoverable using
DataCheck and DataStandard with
drop = TRUE.
Format
A data frame with 599 rows and 11 variables in long format, with one row per recorded subject and visit:
patient_idNoncanonical character subject identifier.
visit_monthCharacter-encoded visit time in months.
treatmentCharacter-encoded binary treatment assignment.
alive_statusCharacter-encoded binary survival or intermediate status.
- X1, X2, X3
Continuous baseline covariates.
- X4, X5, X6
Binary baseline covariates.
clinical_outcomeContinuous longitudinal clinical outcome.
Details
The data include nonstandard subject identifiers, character-encoded visit
times and binary variables, unsorted records, an incomplete longitudinal
record, missing required covariate values, a missing outcome among survivors,
and a record with a missing subject identifier. Structural outcome
missingness for records with alive_status = 0 is retained.
Examples
data("ImperfectConSample", package = "PDRobust")
head(ImperfectConSample)
#> patient_id visit_month alive_status treatment clinical_outcome X1 X2
#> 1 PT-0171 0 1 1 4.598 1.452 -2.075
#> 2 PT-0100 6 0 1 NA 1.473 -0.758
#> 3 PT-0056 0 1 0 8.806 -2.722 -0.735
#> 4 PT-0034 6 1 0 13.851 -1.471 0.278
#> 5 PT-0164 12 1 1 9.643 -1.272 -1.881
#> 6 PT-0058 0 1 1 10.341 -0.534 -0.842
#> X3 X4 X5 X6
#> 1 -0.147 0 1 1
#> 2 0.608 0 1 1
#> 3 0.424 1 1 0
#> 4 -0.158 0 0 0
#> 5 -3.333 0 1 0
#> 6 -0.092 0 1 0