The goal of DeltaTools is to provide functions to conduct parametric estimation of learning curves and device effects with modified versions of propensity score matching (PSM) and inverse probability of treatment weighting (IPTW).
You can install the released version of DeltaTools from CRAN with:
install.packages("DeltaTools")This is a basic example which shows you how to conduct a retrospective parametric learning curve analysis - where a learning effect is not detected, but an unadjusted device signal is detected:
library(DeltaTools)
# basic example code
plc <- PLCAnalysis(data=data.plc,
datasetIdentifier="Dataset1",
caseIDFieldNM="Patient",
caseDateFieldNM="ProcDate",
outcomeFieldNM="Outcome_Final",
orderFieldNM="CaseOrder_All",
operatorFieldNM="Operator",
covariateFieldNMs=c("Pt_F1", "Pt_F2", "Pt_F3", "Pt_F4", "Pt_F5",
"Pt_F6", "Pt_F7", "Pt_F8", "Pt_F9", "Pt_F10",
"Pt_F11", "Pt_F12", "Pt_F13", "Pt_F14", "Pt_F15",
"Pt_F16", "Pt_F17", "Pt_F18", "Pt_F19", "Pt_F20",
"Pt_F21", "Op_F1", "Op_F2", "Inst_F1", "Inst_F2",
"Inst_F3"),
exposureFieldNM="Device",
exposureOfInterestNM="B",
exposureOfInterestOperatorCaseSeriesFieldNM="CaseOrder_Op_DevB")
print(plc)The data included in DeltaTools was simulated using a specific data generating process for code testing purposes.
If you encounter a bug, have a feature request, or need usage help, please email Amy.Perkins@vumc.org.