xhaz (version 2.1.0)

anova.bsplines: anova.bsplines function used for likelihood-ratio Test of two models from xhaz function

Description

This function compute an analysis of deviance table for two excess hazard models fitted using xhaz R package.

Usage

# S3 method for bsplines
anova(object, ..., test = "LRT")

Value

An object of class anova inheriting from class matrix. The different columns contain respectively the degrees of freedom and the log-likelihood values of the two nested models, the degree of freedom of the chi-square statistic, the chi-square statistic and the p-value of the likelihood ratio test.

Arguments

object

an object of class bsplines

...

an object of class bsplines

test

a character string. The appropriate test is a likelihood-ratio test, all other choices result in Not yet implemented test.

Author

Juste Goungounga, Robert Darlin Mba, Nathalie Graff\'eo and Roch Giorgi

References

Goungounga JA, Touraine C, Graff\'eo N, Giorgi R; CENSUR working survival group. Correcting for misclassification and selection effects in estimating net survival in clinical trials. BMC Med Res Methodol. 2019 May 16;19(1):104. doi: 10.1186/s12874-019-0747-3. PMID: 31096911; PMCID: PMC6524224. (PubMed)

Touraine C, Graff\'eo N, Giorgi R; CENSUR working survival group. More accurate cancer-related excess mortality through correcting background mortality for extra variables. Stat Methods Med Res. 2020 Jan;29(1):122-136. doi: 10.1177/0962280218823234. Epub 2019 Jan 23. PMID: 30674229. (PubMed)

Mba RD, Goungounga JA, Graff\'eo N, Giorgi R; CENSUR working survival group. Correcting inaccurate background mortality in excess hazard models through breakpoints. BMC Med Res Methodol. 2020 Oct 29;20(1):268. doi: 10.1186/s12874-020-01139-z. PMID: 33121436; PMCID: PMC7596976. (PubMed)

See Also

xhaz, summary.bsplines, print.constant

Examples

# load the data set in the package
# \donttest{
library("survival")
library("numDeriv")
library("survexp.fr")
library("statmod")

data("dataCancer", package = "xhaz")   # load the data set in the package

fit.phBS <- xhaz(
      formula = Surv(obs_time_year, event) ~ ageCentre + immuno_trt,
      data = dataCancer,
      ratetable = survexp.fr::survexp.fr,
      interval = c(0, NA, NA, max(dataCancer$obs_time_year)),
      rmap = list(age = 'age', sex = 'sexx', year = 'year_date'),
      baseline = "bsplines", pophaz  = "classic")



fit.nphBS <- xhaz(
      formula = Surv(obs_time_year, event) ~ ageCentre + qbs(immuno_trt),
      data = dataCancer,
      ratetable = survexp.fr::survexp.fr,
      interval = c(0, NA, NA, max(dataCancer$obs_time_year)),
      rmap = list(age = 'age', sex = 'sexx', year = 'year_date'),
      baseline = "bsplines", pophaz  = "classic")

anova(fit.phBS, fit.nphBS)
# }