predict.constant: Predictions of excess hazard and net Survival from an...

In xhaz: Excess Hazard Modelling Considering Inappropriate Mortality Rates

Predictions of excess hazard and net Survival from an constant object

Description

Function to predict excess hazard and net survival based on an object of class constant. The function allows the predictions at several time points but not exceeding the maximum time of follow-up from the baseline model.

Usage

## S3 method for class 'constant'
predict(object, new.data = NULL, times.pts = NULL, baseline = TRUE, ...)

Arguments

object	An object of class constant
new.data	new.data where is covariates
times.pts	time in year scale to calculate the excess hazard. The default value is NULL. In this case, time variable must be provided in the new.data
baseline	default is survival baseline; put baseline = FALSE to estimate the net survival with covariates
...	additional arguments affecting the predictions of excess hazard and net survival

Value

An object of class predxhaz. The return of this fonction can be used to produce graphics of excess hazard or net survival, when times.pts argument is provided. This object contains:

times.pts	the times value in year at which the excess hazard and or the net survival have been estimated
hazard	the excess hazard values based on the model of interest
survival	the net survival values based on the model of interest

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)

See Also

xhaz, print.bsplines, print.constant

Examples

# load the data set in the package
library("xhaz")
library("numDeriv")

# load the data sets 'simuData

data("simuData", package = "xhaz")

#define the levels of variable sex
levels(simuData$sex) <- c("male", "female")

# Esteve et al. model

set.seed(1980)
simuData2 <- simuData[sample(nrow(simuData), size = 500), ]

fit.estv2 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                  data = simuData2,
                  ratetable = survexp.us,
                  interval = c(0, NA, NA, NA, NA, NA, 6),
                  rmap = list(age = 'age', sex = 'sex', year = 'date'),
                  baseline = "constant", pophaz = "classic")


predict_est <- predict(object = fit.estv2,
                       new.data = simuData2,
                       times.pts = c(seq(0, 4, 1)),
                       baseline = TRUE)
predict_est

xhaz documentation built on June 30, 2024, 1:07 a.m.