October 16, 2017
This package contains a single function, survNRI
, which calculates the NRI for survival data using up to five different estimators. The different methods available are:
- KM = Kaplan- Meier estimator
- IPW = Inverse probability weighted estimator
- SmoothIPW = Smooth inverse probability weighted estimator
- SEM = Semi-parametric estimator
- Combined= Combined estimator as described (along with all other estimates) in the reference paper.
Install the package from Github using the devtools
package.
library(devtools)
install_github("mdbrown/survNRI")
library(survNRI)
#some simulated data for example.
data(SimData)
#take a look
head(SimData)
## stime status y1 y2
## 1 3.9739276 1 0.6941739 -1.3600205
## 2 4.2776989 0 -1.8042212 -0.4031856
## 3 6.3921888 0 -0.3541849 -1.0477879
## 4 7.8256953 1 -1.7860011 -0.7711128
## 5 0.7346158 0 -1.0067846 1.2007543
## 6 22.4002292 0 -0.6357343 -0.4936282
Calculate the NRI using all estimators at future time 2.
#bootstrap only 10 times to reduce computation for this example.
survNRI( time = "stime", event = "status", model1 = "y1", model2 = c("y1", "y2"), data = SimData,
predict.time = 2, method = "all", bootMethod = "normal", bootstraps = 10, alpha = .05)
##
## Net Reclassification Improvement at time t = 2
## with 95% bootstrap confidence intervals based on normal approximation.
##
## method | event NRI non-event NRI NRI
## -------------------------------------------------------------------------------
## KM | 0.058 (-0.140,0.255) 0.239 (0.121,0.415) 0.297 (-0.040,0.576)
## IPW | 0.056 (-0.141,0.253) 0.238 (0.121,0.412) 0.294 (-0.041,0.573)
## SmoothIPW | 0.054 (-0.146,0.253) 0.237 (0.122,0.406) 0.291 (-0.042,0.570)
## SEM | 0.131 (0.041,0.221) 0.270 (0.174,0.496) 0.401 (0.136,0.535)
## Combined | 0.088 (-0.086,0.261) 0.251 (0.132,0.458) 0.339 (-0.008,0.598)
## -------------------------------------------------------------------------------
Now only estimate using the smooth IPW, SEM and combined methods.
tmp <- survNRI( time = "stime", event = "status", model1 = "y1", model2 = c("y1", "y2"), data = SimData,
predict.time = 3, method = c("SmoothIPW", "SEM", "Combined"), bootMethod = "percentile", bootstraps = 10, alpha = .01)
#look at the results
tmp
##
## Net Reclassification Improvement at time t = 3
## with 99% bootstrap confidence intervals based on percentiles.
##
## method | event NRI non-event NRI NRI
## -------------------------------------------------------------------------------
## SmoothIPW | 0.130 (0.052,0.185) 0.243 (0.177,0.311) 0.373 (0.299,0.450)
## SEM | 0.164 (0.084,0.202) 0.264 (0.184,0.317) 0.428 (0.325,0.496)
## Combined | 0.145 (0.062,0.191) 0.252 (0.181,0.307) 0.397 (0.303,0.464)
## -------------------------------------------------------------------------------
#access estimates and ci's
tmp$estimates
## NRI.event NRI.nonevent NRI
## SmoothIPW 0.1304272 0.2425232 0.3729504
## SEM 0.1638181 0.2640453 0.4278633
## Combined 0.1450687 0.2519604 0.3970291
tmp$CI
## $NRI.event
## SmoothIPW SEM Combined
## lowerbound 0.05170142 0.0840699 0.06211513
## upperbound 0.18481643 0.2020702 0.19120167
##
## $NRI.nonevent
## SmoothIPW SEM Combined
## lowerbound 0.1772579 0.1838134 0.1807217
## upperbound 0.3106670 0.3168780 0.3069153
##
## $NRI
## SmoothIPW SEM Combined
## lowerbound 0.2994608 0.3254050 0.3025600
## upperbound 0.4501999 0.4964829 0.4636058
for more information see ?survNRI
.
Lifetime Data Analysis 2012 Dec 20. [Epub ahead of print] Evaluating incremental values from new predictors with net reclassification improvement in survival analysis. Zheng Y, Parast L, Cai T, Brown M. PMID: 23254468