shr: Fit a survival model
In SmoothHazard: Estimation of Smooth Hazard Models for Interval-Censored Data

View source: R/shr.R

shr	R Documentation

Fit a survival model

Description

Fit a survival model using either a semi-parametric approach (penalized likelihood with an approximation of the hazard function by linear combination of M-splines) or a parametric approach (specifying a Weibull distribution on the hazard function). Left-truncated, right-censored, and interval-censored data are allowed.

Usage

shr(
  formula,
  data,
  eps = c(5, 5, 3),
  n.knots = 7,
  knots = "equidistant",
  CV = FALSE,
  kappa = 10000,
  conf.int = 0.95,
  maxiter = 200,
  method = "Weib",
  print.iter = FALSE,
  na.action = na.omit
)

Arguments

`formula`	a formula object with the response on the left hand side and the terms on the right hand side. The response must be a survival object or Hist object as returned by the `'Surv'` or `'Hist'` function.
`data`	a data frame in which to interpret the variables named in the `formula`.
`eps`	a vector of length 3 for the convergence criteria (criterion for parameters, criterion for likelihood, criterion for second derivatives). The default is `c(5,5,3)` and corresponds to criteria equals to `10^{-5}`, `10^{-5}` and `10^{-3}`.
`n.knots`	Argument only active for the penalized likelihood approach `method="splines"`. Number of knots for the splines to use to approximate the hazard function. The default is 7. If `knots` are given as a vector this argument is ignored. The algorithm needs least 5 knots and at most 20 knots.
`knots`	Argument only active for the penalized likelihood approach `method="splines"`. There are three ways to control the placement of the knots between the smallest and the largest of all time points: `knots="equidistant"` Knots are placed with same distance on the time scale. `knots="quantiles"` Knots are placed such that the number of observations is roughly the same between knots. knots=list() List of length 3. The list elements are the actual placements (timepoints) of the knots for the M-spline. The algorithm reuqires at least 5 knots and allows no more than 20 knots.
`CV`	binary variable equals to 1 when search (by approximated cross validation) of the smoothing parameter kappa and 0 otherwise. Argument for the penalized likelihood approach. The default is 0.
`kappa`	Argument only active for the penalized likelihood approach `method="splines"`. A positive number (smoothing parameter) If CV=1 the value is used as a starting value for a cross validation search to optimize `kappa`.
`conf.int`	Level of confidence pointwise confidence intervals of the survival and hazard functions, i.e., a value between 0 and 1, the default is `0.95`. The default is also used when `conf.int=TRUE`. To avoid computation of confidence intervals, set `conf.int` to FALSE or NULL.
`maxiter`	maximum number of iterations. The default is 200.
`method`	type of estimation method: "Splines" for a penalized likelihood approach with approximation of the hazard function by M-splines, "Weib" for a parametric approach with a Weibull distribution on the hazard function. Default is `"Weib"`.
`print.iter`	boolean parameter. Equals to `TRUE` to print the likelihood during the iteration process, `FALSE` otherwise. Default is `FALSE`. This option is not running on Windows.
`na.action`	how NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third 'na.fail' if that is unset. The 'factory-fresh' default is na.omit. Another possible value is NULL.

Details

The estimated parameters are obtained using the robust Marquardt algorithm (Marquardt, 1963) which is a combination between a Newton-Raphson algorithm and a steepest descent algorithm.

Value

call
coef: regression parameters.
loglik: vector containing the log-likelihood without and with covariate.
modelPar: Weibull parameters.
N: number of subjects.
NC: number of covariates.
nevents: number of events.
modelResponse: model response: Hist or Surv object.
converged: integer equal to 1 when the model converged, 2, 3 or 4 otherwise.
time: times for which survival and hazard functions have been evaluated for plotting.
hazard: matched values of the hazard function.
lowerHazard: lower confidence limits for hazard function.
upperHazard: upper confidence limits for hazard function.
surv: matched values of the survival function.
lowerSurv: lower confidence limits for survival function.
upperSurv: upper confidence limits for survival function.
RR: vector of relative risks.
V: variance-covariance matrix.
se: standard errors.
knots: knots of the M-splines estimate of the hazard function.
nknots: number of knots.
CV: a binary variable equals to 1 when search of the smoothing parameter kappa by approximated cross-validation, 1 otherwise. The default is 0.
niter: number of iterations.
cv: vector containing the convergence criteria.
na.action: observations deleted if missing values.

Author(s)

R: Celia Touraine celia.touraine@icm.unicancer.fr Fortran: Pierre Joly Pierre.Joly@isped.u-bordeaux2.fr

References

D. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431-441.

Examples


# Weibull survival model
library(prodlim)
data(testdata)
fit.su <- shr(Hist(time=list(l,r),id)~cov,data=testdata) 
fit.su
summary(fit.su)

shr.spline <- shr(Hist(time=list(l,r),id)~cov,data=testdata,method="splines",n.knots=6)
shr.spline
shr.spline.q <- shr(Hist(time=list(l,r),id)~cov,data=testdata,
                    method="splines",n.knots=6,knots="quantiles")
plot(shr.spline.q)

## manual placement of knots
shr.spline.man <- shr(Hist(time=list(l,r),id)~cov,data=testdata,method="splines",knots=seq(0,7,1))

SmoothHazard documentation built on May 29, 2024, 7:54 a.m.