xhaz-package {xhaz}R Documentation

Excess Hazard Modelling Considering Inappropriate Mortality Rates

Description

Contains functions to fit excess hazard models, with or without proportional population hazards assumption. The baseline excess hazard could be a piecewise constant function or a B-splines. When B-splines is choosen for the baseline excess hazard, the user can specify some covariates which have a time-dependent effect (using "bsplines") on the baseline excess hazard. The user can also specify if the framework corresponds to the classical excess hazard modeling, i.e. assuming that the expected mortality of studied individuals is appropriate. He can also consider two other frameworks: first, the expected mortality available in the life table is not accurate and requires taking into account an additional variable in the life table by allowing the latter acts on the general population morality with a proportional effect. This approach is presented by Touraine et al. (2020) doi:10.1177/0962280218823234. The user can also fit a model that relax the proportional expected hazards assumption considered in the latter excess hazard model. This extension was proposed by Mba et al. (2020) doi:10.1186/s12874-020-01139-z allows non-proportional effect of the additional variable on the general population mortality; second, there is a non-comparability source of bias in terms of expected mortality of selected individuals in a non-population-based studies such as clinical trials. The related excess hazard model correcting this source of bias is presented in Goungounga et al. (2019) doi:10.1186/s12874-019-0747-3. The optimization process in these presented models uses the maximum likelihood method through the routine optim or an internal function of the xhaz-package.

Details

Package: xhaz
Type: Package
Version: 2.0.1
Date: 2022-09-12
License: GPL-3

Author(s)

Maintainer: Juste Goungounga juste.goungounga@univ-amu.fr (ORCID)

Authors:

References

Goungounga JA, Touraine C, Grafféo 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, Grafféo 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, Grafféo 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)

Examples



library("numDeriv")
library("survexp.fr")
library("splines")
data("simuData", "dataCancer", package = "xhaz")
# load the data sets 'simuData' and 'dataCancer'.

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

# Esteve et al. model: baseline excess hazard is a piecewise function
#                      linear and proportional effects for the covariates on
#                      baseline excess hazard.


fit.estv1 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                  data = simuData,
                  ratetable = survexp.us,
                  interval = c(0, NA, NA, NA, NA, NA, max(simuData$time_year)),
                  rmap = list(age = 'age', sex = 'sex', year = 'date'),
                  baseline = "constant",
                  pophaz = "classic")


fit.estv1


# Touraine et al. model: baseline excess hazard is a piecewise function
#                        with a linear and proportional effects for the
#                        covariates on the baseline excess hazard.
# An additionnal cavariate (here race) missing in the life table is
# considered by the model.


fit.corrected1 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                       data = simuData,
                       ratetable = survexp.us,
                       interval = c(0, NA, NA, NA, NA, NA,
                                    max(simuData$time_year)),
                       rmap = list(age = 'age', sex = 'sex', year = 'date'),
                       baseline = "constant", pophaz = "corrected",
                       add.rmap = "race")



fit.corrected1



 # An additionnal cavariate (here race) missing in the life table is
 # considered by the model with a breakpoint at 75 years

 fit.corrected2 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                        data = simuData, ratetable = survexp.us,
                        interval = c(0, NA, NA, NA, NA, 6),
                        rmap = list(age = 'age', sex = 'sex', year = 'date'),
                        baseline = "constant", pophaz = "corrected",
                        add.rmap = "race",
                        add.rmap.cut = list(breakpoint = TRUE, cut = 75))


 fit.corrected2

#Giorgi et al model: baseline excess hazard is a quadratic Bsplines
#                    function with two interior knots and allow here a
#                    linear and proportional effects for the covariates on
#                    baseline excess hazard.


fitphBS <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                data = simuData, baseline = "bsplines",
                pophaz = "classic", ratetable = survexp.us,
                interval = c(0, NA, NA, max(simuData$time_year)),
                rmap = list(age = 'age', sex = 'sex', year = 'date'))

fitphBS







[Package xhaz version 2.0.1 Index]