Zachary McCaw
Updated: 2021-07-19
This package performs maximum likelihood based estimation and inference on time to event data subject to non-informative right censoring. FitParaSurv provides maximum likelihood estimation of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv compares the mean, median, and restricted mean survival experiences of two treatment groups. Available distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.
devtools::install_github(repo = 'zrmacc/Temporal')A detailed vignette including the setting, distribution parameterizations, and usage examples is available here.
The follow example compares data from two Weibull distributions with the same shape but different rate parameters.
library(Temporal)
set.seed(100)
n <- 1000
# Weibull data with shape = 1, rate = 1, and 20% censoring.
df1 <- GenData(n = n, dist = "weibull", theta = c(1, 1), p = 0.20)
df1$arm <- 1
fit <- FitParaSurv(df1, dist = "weibull")
show(fit)## Fitted Weibull Distribution.
## Estimated Parameters:
## Aspect Estimate SE L U
## 1 Shape 0.968 0.026 0.917 1.019
## 2 Rate 1.086 0.040 1.008 1.164
##
## Distribution Properties:
## Aspect Estimate SE L U
## 1 Mean 0.934 0.034 0.867 1.000
## 2 Median 0.630 0.025 0.581 0.679
## 3 Variance 0.931 0.090 0.754 1.107
# Weibull data with shape = 1, rate = 2, and 25% censoring.
df0 <- GenData(n = n, dist = "weibull", theta = c(1, 2), p = 0.25)
df0$arm <- 0
fit <- FitParaSurv(df0, dist = "weibull")
show(fit)## Fitted Weibull Distribution.
## Estimated Parameters:
## Aspect Estimate SE L U
## 1 Shape 1.033 0.030 0.975 1.091
## 2 Rate 1.995 0.071 1.856 2.133
##
## Distribution Properties:
## Aspect Estimate SE L U
## 1 Mean 0.495 0.018 0.460 0.530
## 2 Median 0.352 0.013 0.325 0.378
## 3 Variance 0.230 0.023 0.184 0.275
# Comparison of Weibulls.
data <- rbind(df1, df0)
comp <- CompParaSurv(data, dist1 = "weibull")
show(comp)## Contrast of Fitted Weibull Distributions.
##
## Fitted Characteristics for Group 1:
## Aspect Estimate SE L U
## 1 Mean 0.934 0.034 0.867 1.000
## 2 Median 0.630 0.025 0.581 0.679
## 3 Variance 0.931 0.090 0.754 1.107
##
## Fitted Characteristics for Group 0:
## Aspect Estimate SE L U
## 1 Mean 0.495 0.018 0.460 0.530
## 2 Median 0.352 0.013 0.325 0.378
## 3 Variance 0.230 0.023 0.184 0.275
##
## Location:
## Contrast Point SE L U P
## 1 Mean1-Mean0 0.439 0.038 0.364 0.514 0
## 2 Mean1/Mean0 1.887 0.097 1.707 2.086 0
## 3 Med1-Med0 0.279 0.028 0.223 0.334 0
## 4 Med1/Med0 1.793 0.099 1.610 1.997 0