A simulated dataset containing responses to a 10-item patient-reported outcome measure at two time points for 1000 subjects, together with a binary transition rating anchor.
Format
A data frame with 1000 rows and 21 variables:
- v1_1
Item 1 at Time 1.
- v1_2
Item 2 at Time 1.
- v1_3
Item 3 at Time 1.
- v1_4
Item 4 at Time 1.
- v1_5
Item 5 at Time 1.
- v1_6
Item 6 at Time 1.
- v1_7
Item 7 at Time 1.
- v1_8
Item 8 at Time 1.
- v1_9
Item 9 at Time 1.
- v1_10
Item 10 at Time 1.
- v2_1
Item 1 at Time 2.
- v2_2
Item 2 at Time 2.
- v2_3
Item 3 at Time 2.
- v2_4
Item 4 at Time 2.
- v2_5
Item 5 at Time 2.
- v2_6
Item 6 at Time 2.
- v2_7
Item 7 at Time 2.
- v2_8
Item 8 at Time 2.
- v2_9
Item 9 at Time 2.
- v2_10
Item 10 at Time 2.
- trat
Binary transition rating anchor, coded 0/1.
Source
Simulated example data generated for demonstrating MIC package
functions. See data-raw/R/example.R.
Details
Each item has four ordered response categories scored 0, 1, 2, and 3. Therefore, the summed score at each time point ranges from 0 to 30.
The Time 1 items are named v1_1 to v1_10, and the Time 2 items are named
v2_1 to v2_10. The variable trat is a dichotomous transition rating
indicator coded 0/1.
This dataset is useful for demonstrating MIC estimation functions such as
mic_roc(), mic_pred(), mic_adjust(), mic_iapm(), tr_reliability(),
and mic_lcfa().
See also
simdat() for generating new simulated datasets with user-specified
simulation parameters.
mic_roc(), mic_pred(), mic_adjust(), and mic_iapm() for predictive
modeling and adjusted predictive modeling MIC estimation.
tr_reliability() for estimating transition rating reliability.
mic_lcfa() for LCFA-based MIC estimation.
Examples
data(example)
nitems <- 10
example$score_t1 <- rowSums(example[, paste0("v1_", 1:nitems)])
example$score_t2 <- rowSums(example[, paste0("v2_", 1:nitems)])
example$change <- example$score_t2 - example$score_t1
head(example)
#> v1_1 v1_2 v1_3 v1_4 v1_5 v1_6 v1_7 v1_8 v1_9 v1_10 v2_1 v2_2 v2_3 v2_4 v2_5
#> 1 1 0 1 0 0 0 0 0 0 2 1 3 1 2 0
#> 2 3 2 0 2 2 0 1 1 3 0 2 0 2 2 2
#> 3 3 0 0 3 0 3 0 0 2 0 3 3 3 3 3
#> 4 2 0 0 0 2 1 0 0 0 3 0 3 1 2 2
#> 5 3 2 2 1 0 3 1 1 0 1 3 1 3 3 3
#> 6 3 3 2 0 0 0 0 3 0 0 3 0 3 0 0
#> v2_6 v2_7 v2_8 v2_9 v2_10 trat score_t1 score_t2 change
#> 1 0 0 2 0 0 1 4 9 5
#> 2 2 2 1 0 0 0 14 13 -1
#> 3 3 3 2 2 2 1 11 27 16
#> 4 1 2 3 0 1 0 8 15 7
#> 5 0 2 3 2 3 1 14 23 9
#> 6 1 3 0 0 0 0 11 10 -1
mic_roc(
data = example,
x = "score_t1",
y = "score_t2",
tr = "trat",
nboot = 0
)
#> Warning: 'transpose=TRUE' is deprecated. Only 'transpose=FALSE' will be allowed in a future version.
#> ROC-based MIC estimation
#> ------------------------
#> MIC ROC: -0.500