README.md

In zrmacc/Temporal: Parametric Time to Event Analysis

Temporal: Parametric Comparison of Time to Event Distributions

Zachary McCaw Updated: 2021-07-19

Description

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.

Installation

devtools::install_github(repo = 'zrmacc/Temporal')

Vignette

A detailed vignette including the setting, distribution parameterizations, and usage examples is available here.

Compact Example

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

zrmacc/Temporal documentation built on Sept. 29, 2023, 11:25 p.m.