Learn regression-based Reliable Change Index (RCI) models relative to a
user-defined reference visit and calculate projected RCI values.
Usage
CreateRCIObject(
Data,
Variables,
DataFormat = c("wide", "long"),
ID,
Method = "regression",
BaselineSpecifier = NULL,
FollowupSpecifier = NULL,
SpecifierPosition = c("suffix", "prefix"),
VisitColumn = NULL,
VisitOrder = NULL,
BaselineVisit = NULL,
Confidence = 0.95,
Relabel = TRUE
)
Arguments
- Data
A data frame.
- Variables
Character vector of canonical variable names.
- DataFormat
Either "wide" or "long".
- ID
ID column.
- Method
Currently only "regression" is supported.
- BaselineSpecifier
Baseline visit identifier for wide data.
- FollowupSpecifier
Follow-up visit identifier for wide data.
- SpecifierPosition
Either "suffix" or "prefix".
- VisitColumn
Visit column for long data.
- VisitOrder
Optional ordering of visits.
- BaselineVisit
Reference visit used for RCI calculations.
- Confidence
Confidence interval threshold.
- Relabel
Logical; use variable labels when available.
#'
Interpretation guide
egression-based RCI values are interpreted similarly to z-scores.
| RCI cutoff | Approximate confidence interval |
| +/-0.50 | ~38% |
| +/-1.00 | ~68% |
| +/-1.645 | ~90% |
| +/-1.96 | ~95% |
| +/-2.58 | ~99% |
Traditional Jacobson-Truax RCI thresholds typically use +/-1.96,
corresponding to approximately 95% confidence.
Value
A SciDataReportR_RCI object.
Details
Supports both wide and long longitudinal data structures.
Long format is recommended for datasets with more than two visits.
