FitParaSurv {Temporal}

Fit Parametric Survival Distribution

Description

Estimates parametric survival distributions using event times subject to non-informative right censoring. Available distributions include: exponential, gamma, generalized gamma, log-normal, and Weibull.

Usage

FitParaSurv(
  data,
  beta_lower = 0.1,
  beta_upper = 10,
  dist = "weibull",
  eps = 1e-06,
  init = NULL,
  maxit = 10,
  report = FALSE,
  sig = 0.05,
  status_name = "status",
  tau = NULL,
  time_name = "time"
)

Arguments

data

Data.frame containing the time to event and status.

beta_lower

If dist="gen-gamma", lower limit on possible values for beta.

beta_upper

If dist="gen-gamma", upper limit on possible values for beta.

dist

String, distribution to fit, selected from among: exp, gamma, gen-gamma log-normal, and weibull.

eps

Tolerance for Newton-Raphson iterations.

init

List of initial parameters. See individual distributions for the expected parameters.

maxit

Maximum number of NR iterations.

report

Report fitting progress?

sig

Significance level, for CIs.

status_name

Name of the status indicator, 1 if observed, 0 if censored.

tau

Optional truncation time for calculating RMSTs.

time_name

Name of column containing the time to event.

Value

An object of class fit containing the following:

Parameters

The estimated shape and rate parameters.

Information

The observed information matrix.

Outcome

The fitted mean, median, and variance.

RMST

The estimated RMSTs, if tau was specified.

See Also

• Between group comparison of survival experience CompParaSurv

• Exponential distribution FitExp

• Gamma distribution FitGamma

• Generalized gamma distribution FitGenGamma

• Log-normal distribution FitLogNormal

• Weibull distribution FitWeibull

Examples

# Generate Gamma data with 20% censoring.
data <- GenData(n = 1e3, dist = "gamma", theta = c(2, 2), p = 0.2)
# Fit gamma distribution.
fit <- FitParaSurv(data, dist = "gamma")

# Generate Weibull data with 10% censoring.
data <- GenData(n = 1e3, dist = "weibull", theta = c(2, 2), p = 0.1)
# Fit weibull distribution, calculate RMST at tau=0.5.
fit <- FitParaSurv(data, dist = "weibull", tau = 0.5)