A simulated cohort (no real patient) in wide format, used in the examples,
tests and vignettes: 120 subjects randomised to two arms, measured at
baseline (_V0) and at follow-up (_V3). It reproduces the usual
difficulties of cohort data: skewed biomarkers, values piled at a limit of
detection, gross outliers, bounded integer scores, a bilateral measure and
missing values, more frequent at follow-up.
Format
A data frame with 120 rows and 21 columns:
- ID
Subject identifier (
P001toP120).- GROUP
Treatment arm, factor (
Placebo,Active).- SEX
Factor (
Female,Male).- AGE
Age at baseline, years.
- BMI
Body mass index, kg/m2 (a few missing values).
- NFL_V0, NFL_V3
Neurofilament light chain, pg/mL (log-normal, increasing with age).
- IL6_V0, IL6_V3
Interleukin-6, pg/mL; values below the limit of detection are reported at 0.5.
- CRP_V0, CRP_V3
C-reactive protein, mg/L (skewed; two gross outliers at baseline).
- MOCA_V0, MOCA_V3
Montreal Cognitive Assessment, integer 10-30.
- UPDRS_V0, UPDRS_V3
Motor score (UPDRS part III), integer.
- FALL_V0, FALL_V3
Falls in the last year, factor (
No,Yes).- PUT_L_V0, PUT_R_V0, PUT_L_V3, PUT_R_V3
Left and right putamen volume, mL.
Examples
str(cohort_sim)
#> 'data.frame': 120 obs. of 21 variables:
#> $ ID : chr "P001" "P002" "P003" "P004" ...
#> $ GROUP : Factor w/ 2 levels "Placebo","Active": 1 1 1 2 1 1 1 2 2 2 ...
#> $ SEX : Factor w/ 2 levels "Female","Male": 2 2 1 1 2 2 2 2 2 2 ...
#> $ AGE : num 80 82 67 72 51 69 61 64 69 73 ...
#> $ BMI : num 27.1 26.4 30.8 28.9 26 25.9 26.6 21.7 30.9 23.8 ...
#> $ NFL_V0 : num 26.8 6.6 4.9 7.7 14.7 19.4 7.6 12 17.2 17.5 ...
#> $ NFL_V3 : num NA 8.5 5.2 7.2 21 27.1 9.6 14.7 15.6 14 ...
#> $ IL6_V0 : num 2.59 1.04 1.2 1.81 0.83 1.01 0.76 0.9 0.52 2.25 ...
#> $ IL6_V3 : num NA 0.68 2.13 2.31 1.31 0.5 0.5 0.71 0.54 2.08 ...
#> $ CRP_V0 : num 2.8 1.21 5.63 0.97 2.37 ...
#> $ CRP_V3 : num NA 0.73 4.09 1.01 1.64 4.28 9.22 1.26 1.62 0.88 ...
#> $ MOCA_V0 : num 24 26 28 30 25 26 27 29 27 25 ...
#> $ MOCA_V3 : num NA 27 27 29 26 24 28 29 23 24 ...
#> $ UPDRS_V0: num 20 7 15 4 34 22 1 33 29 5 ...
#> $ UPDRS_V3: num NA 12 21 1 36 25 5 37 32 6 ...
#> $ FALL_V0 : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 2 1 1 ...
#> $ FALL_V3 : Factor w/ 2 levels "No","Yes": NA 2 1 1 2 1 1 1 1 1 ...
#> $ PUT_L_V0: num 4.05 3.88 4.52 4.8 5.05 5.02 4.58 4.34 4.51 4.78 ...
#> $ PUT_R_V0: num 3.74 3.77 4.5 4.65 5.32 5.05 4.41 4.24 4.29 4.68 ...
#> $ PUT_L_V3: num NA 3.88 4.44 4.76 5.03 5 4.56 4.32 4.53 4.76 ...
#> $ PUT_R_V3: num NA 3.74 4.47 4.62 5.22 5.03 4.37 4.23 4.32 4.54 ...