anova.constant {xhaz}

anova.constant 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 class 'constant'
anova(object, ..., test = "LRT")

Arguments

object

an object of class constant

...

an object of class constant

test

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

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.

Note

As expected, the comparison between two or more models by anova or more excess hazard models will only be valid if they are fitted to the same dataset, and if the compared models are nested. This may be a problem if there are missing values.

Author(s)

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)

Giorgi R, Abrahamowicz M, Quantin C, Bolard P, Esteve J, Gouvernet J, Faivre J. A relative survival regression model using B-spline functions to model non-proportional hazards. Statistics in Medicine 2003; 22: 2767-84. (PubMed)

See Also

xhaz, summary.bsplines, print.constant

Examples

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


data("dataCancer")   # load the data set in the package

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



fit.ph2 <- xhaz(
      formula = Surv(obs_time_year, event) ~ ageCentre ,
      data = dataCancer,
      ratetable = survexp.fr::survexp.fr,
      interval = c(0, NA, NA, NA, max(dataCancer$obs_time_year)),
      rmap = list(age = 'age', sex = 'sexx', year = 'year_date'),
      baseline = "constant", pophaz  = "classic")

  anova(fit.ph2, fit.ph)