summary.flexrsurv: Summarizing Flexible Relative Survival Model Fits

Description

summary methods for class flexrsurv. Produces and prints summaries of the results of a fitted Relative Survival Model

Usage

# S3 method for flexrsurv
summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)
# S3 method for summary.flexrsurv
print(x, digits = max(3L, getOption("digits") - 3L), 
      symbolic.cor = x$symbolic.cor,
      signif.stars = getOption("show.signif.stars"), ...)

Value

The function summary.flexrsurv computes and returns a list of summary statistics of the fitted flexible relative survival model given in object. The returned value is an object of class "summary.flexrsurv", which a list with components:

call: the "call" component from object.
terms: the "terms" component from object.
coefficients: the matrix of coefficients, standard errors, z-values and p-values.
cov: the estimated covariance matrix of the estimated coefficients.
correlation: (only if correlation is true.) the estimated correlations of the estimated coefficients.
symbolic.cor: (only if correlation is true.) the value of the argument symbolic.cor.
loglik: the "loglik" component from object.
df.residual: the "df.residual" component from object.

Arguments

object: an object of class "flexrsurv", usually, a result of a call to flexrsurv.
x: an object of class "summary.flexrsurv", usually, a result of a call to summary.flexrsurv.
correlation: logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.
symbolic.cor: logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.
digits: the number of significant digits to use when printing.
signif.stars: logical. If TRUE,'significance stars' are printed for each coefficient.
...: further arguments passed to or from other methods.

Details

print.summary.glm tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives `significance stars' if signif.stars is TRUE.

Correlations are printed to two decimal places (or symbolically): to see the actual correlations print summary(object)$correlation directly.

The dispersion of a GLM is not used in the fitting process, but it is needed to find standard errors. If dispersion is not supplied or NULL, the dispersion is taken as 1 for the binomial and Poisson families, and otherwise estimated by the residual Chisquared statistic (calculated from cases with non-zero weights) divided by the residual degrees of freedom.

Examples

Run this code

# \donttest{

if (requireNamespace("relsurv", quietly = TRUE)) {
	
	# data from package relsurv
	data(rdata, package="relsurv")
	# rate table from package relsurv
	data(slopop, package="relsurv")
	
	# get the death rate at event (or end of followup) from slopop for rdata
	rdata$iage <- findInterval(rdata$age*365.24+rdata$time, attr(slopop, "cutpoints")[[1]])
	rdata$iyear <- findInterval(rdata$year+rdata$time, attr(slopop, "cutpoints")[[2]])
	therate <- rep(-1, dim(rdata)[1])
	for( i in 1:dim(rdata)[1]){
	  therate[i] <- slopop[rdata$iage[i], rdata$iyear[i], rdata$sex[i]]
	}
	
	rdata$slorate <- therate
	
	
	# change sex coding
	rdata$sex01 <- rdata$sex -1
	
	# fit a relative survival model with a non linear effetc of age
	
	fit <- flexrsurv(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3), 
	                 rate=slorate, data=rdata,
	                 knots.Bh=1850,  # one interior knot at 5 years
	                 degree.Bh=3,
	                 Spline = "b-spline",
	                 initbyglm=TRUE, 
	                 initbands=seq(from=0, to=5400, by=200),
	                 int_meth= "CAV_SIM",
	                 step=50
	                 )
	
	summary(fit)
}
# }