FlexrsurvLT: Fit Relative Survival Model and Correct Life Tables
In flexrsurv: Flexible Relative Survival Analysis
flexrsurvclt R Documentation
Fit Relative Survival Model and Correct Life Tables
Description
flexrsurvclt is used to fit relative survival regression model.
transition package.
Usage
flexrsurvclt(formula=formula(data),
formula.table=NULL,
data=parent.frame(),
Id,
baselinehazard=TRUE,
firstWCEIadditive=FALSE,
knots.Bh,
degree.Bh=3,
intercept.Bh=TRUE,
Spline=c("b-spline", "tp-spline", "tpi-spline"),
log.Bh=FALSE,
bhlink=c("log", "identity"),
Min_T=0,
Max_T=NULL,
model=c("additive","multiplicative"),
rate,
logit_start,
logit_end,
logit_start_byperiod = NULL,
logit_end_byperiod = NULL,
weights_byperiod = NULL,
Id_byperiod = NULL,
contrasts.table = NULL,
knots.table=c(-2.5,0,2.5),
degree.table=3,
Spline.table=c("restricted B-splines"),
Spline.CLT=R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
model_correction = c("cohort", "period"),
weights=NULL,
na.action=NULL,
datacontrol=NULL,
Idcontrol,
ratecontrol,
logit_startcontrol,
logit_endcontrol,
logit_start_byperiodcontrol = NULL,
logit_end_byperiodcontrol = NULL,
weights_byperiodcontrol = NULL,
Id_byperiodcontrol = NULL,
weightscontrol=NULL,
int_meth=c("GL", "CAV_SIM", "SIM_3_8", "BOOLE", "GLM", "BANDS"),
bands=NULL,
npoints=20,
stept=NULL,
init=NULL,
initbyglm=TRUE,
initbands=bands,
optim.control=list(trace=100, REPORT=1, fnscale=-1, maxit=25),
optim_meth=c("BFGS", "CG", "Nelder-Mead", "L-BFGS-B", "SANN", "Brent"),
Coptim.control=list(),
lower = -Inf,
upper = Inf,
control.glm=list(epsilon=1e-8, maxit=100, trace=FALSE,
epsilon.glm=.1, maxit.glm=25),
vartype = c("oim", "opg", "none"),
varmethod = c("optim", "numDeriv.hessian", "numDeriv.jacobian"),
numDeriv.method.args=list(eps=5e-7, d=0.001,
zero.tol=sqrt(.Machine$double.eps/7e-4), r=4, v=2),
debug=FALSE
)
flexrsurvclt.ll(formula=formula(data),
formula.table=NULL,
data=parent.frame(),
Id,
baselinehazard=TRUE,
firstWCEIadditive=FALSE,
knots.Bh,
degree.Bh=3,
Spline=c("b-spline", "tp-spline", "tpi-spline"),
log.Bh=FALSE,
bhlink=c("log", "identity"),
intercept.Bh=TRUE,
Min_T=0,
Max_T=NULL,
model=c("additive","multiplicative"),
rate,
logit_start,
logit_end,
logit_start_byperiod = NULL,
logit_end_byperiod = NULL,
weights_byperiod = NULL,
Id_byperiod = NULL,
contrasts.table = NULL,
knots.table=c(-2.5,0,2.5),
degree.table=3,
Spline.table=c("restricted B-splines"),
Spline.CLT=R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
model_correction = c("cohort", "period"),
weights=NULL,
na.action=NULL,
datacontrol=NULL,
Idcontrol,
ratecontrol,
logit_startcontrol,
logit_endcontrol,
logit_start_byperiodcontrol = NULL,
logit_end_byperiodcontrol = NULL,
weights_byperiodcontrol = NULL,
Id_byperiodcontrol = NULL,
weightscontrol=NULL,
int_meth=c("GL", "CAV_SIM", "SIM_3_8", "BOOLE", "GLM", "BANDS"),
bands=NULL,
npoints=20,
stept=NULL,
init=NULL,
optim.control=list(trace=100, REPORT=1, fnscale=-1, maxit=25),
Coptim.control= list(),
optim_meth=c("BFGS", "CG", "Nelder-Mead", "L-BFGS-B", "SANN", "Brent"),
lower = -Inf,
upper = Inf,
vartype = c("oim", "opg", "none"),
varmethod = c("optim", "numDeriv.hessian", "numDeriv.jacobian"),
numDeriv.method.args=list(eps=5e-7, d=0.001,
zero.tol=sqrt(.Machine$double.eps/7e-4), r=4, v=2),
debug=FALSE
)
Arguments
formula
a formula object, with the response on the left of a ~ operator, and the terms on the
right. The response must be a survival object as returned by the Surv function.
formula.table
a formula object, with empty left hand side, and the terms on the
right. This is the formula of the proportional part of the correction
model for the table table
data
a data.frame in which to interpret the variables named in the formulas.
Id
vector whose unique values defines the Ids of the subjects.
baselinehazard
if FALSE, no baseline hazard in the model
firstWCEIadditive
if TRUE, the first WCEI term in the formula is considered as the baseline
knots.Bh
the internal breakpoints that define the spline used to estimate the baseline hazard.
Typical values are the mean or median for one knot, quantiles for more knots.
degree.Bh
degree of the piecewise polynomial of the baseline hazard. Default is 3 for cubic splines.
intercept.Bh
TRUE if the first bases is included in the baseline hazard. Default is TRUE.
Spline
a character string specifying the type of spline basis. "b-spline" for B-spline basis,
"tp-spline" for truncated power basis and "tpi-spline" for monotone (increasing) truncated power basis.
log.Bh
logical value: if TRUE, an additional basis equal to log(time) is added to the spline bases of time.
bhlink
character string specifying the link function of the baseline hazard:
Default is bhlink="log" for including the baseline in the exponential;
if bhlink="identity", the baseline hazard is out of the exponential.
Min_T
minimum of time period which is analysed. Default is max(0.0, min(bands) ).
Max_T
maximum of time period which is analysed. Default is max(c(bands, timevar))
model
character string specifying the type of model for both non-proportional and non linear effects.
The model method=="additive" assumes effects as explained in Remontet et al.(2007), the model method=="multiplicative"
assumes effects as explained in Mahboubi et al. (2011).
rate
a vector of the background rate for a relevant comparative population to be used in the fitting process.
Should be a numeric vector (for relative survival model).
rate is evaluated in the same way as variables in formula, that is first
in data and then in the environment of formula.
logit_start
a vector of the logit of the cumulative hazard at the start of the
interval in the life table.
logit_start is evaluated in the same way as variables in formula, that is first
in data and then in the environment of formula.
logit_end
a vector of the logit of the cumulative hazard at the end of the
interval in the life table.
logit_end is evaluated in the same way as variables in formula, that is first
in data and then in the environment of formula.
logit_start_byperiod, logit_end_byperiod, weights_byperiod, Id_byperiod
A REMPLIR
knots.table
the internal breakpoints on the logit scale that define the knots
of the spline used to estimate the correction model of the life table.
degree.table
degree of the piecewise polynomial of the spline used to estimate
the correction model of the life table.
Default is 3 for cubic splines.
contrasts.table
an optional list. See the contrasts.arg of model.matrix().
Spline.table
a character string specifying the type of spline basis of the the
correction model of the life table. In this version, only "restricted B-splines" is available.
"restricted B-splines" are B-spline basis with linear extrapolation + 2nd derivative at boundaries == 0.
Spline.CLT
a S4 object with method deriv() and evaluate().
The spline basis of the correction of the life table can be specified either by the parameters (knots.table, degree.table)
or an S4 object that ca be used for this purpose.
IMPORTANT : the coef of the first basis is constraints to one and evaluate(deriv(spline_B), left_boundary_knots) == 1
model_correction
character string specifying A COMPLETER.
method_correction="cohort" when the provided logit are those of the survival of individuals;
method_correction="period" when the provided logit are those of the survival fuction of age distribution by period.
weights
an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
If not null, the total likelihood is the weighted sum of individual likelihood.
na.action
a missing-data filter function, applied to the model.frame. If NULL, default is options()$na.action.
datacontrol
a data.frame in which to interpret the variables named in the formula for the control group.
Idcontrol, ratecontrol, logit_startcontrol, logit_endcontrol, weightscontrol
Id, rate, logit of the cumulative hazard at the start and the end of
the intervalle in the life table, and weights for the control group
logit_start_byperiodcontrol, logit_end_byperiodcontrol, weights_byperiodcontrol, Id_byperiodcontrol
A REMPLIR
int_meth
character string specifying the the numerical integration method. Possible values are
"GL" for Gauss-Legendre method, "CAV_SIM" for Cavalieri-Simpson's rule, "SIM_3_8" for the Simpson's 3/8 rule,
"BOOLE" for the Boole's rule, or "BANDS" for the midpoint rule with specified bands.
bands
bands used to split data in the numerical integration when int_meth="BANDS").
npoints
number of points used in the numerical integration when int_meth="GL").
stept
scalar value of the time-step in numerical integration. It is required only when int_meth="CAV_SIM" or "SIM_3_8" or "BOOLE".
If no value is supplied, Max_T/500 is used.
init
starting values of the parameters.
initbyglm
a logical value indicating indicating how are found or refined init values. If TRUE, the fitting method described in Remontet et al.(2007)
is ued to find or refine starting values. This may speedup the fit. If FALSE, the maximisation of the likelihood starts at values
given in init. If init=NULL, the starting values correspond to a constant net hazard equal to the ratio of the number
of event over the total number of person-time.
initbands
bands used to split data when initbyglm=TRUE.
optim.control
a list of control parameters passed to the optim() function.
optim_meth
method to be used to optimize the likelihood.
See optim.
Coptim.control
a list of control parameters passed to the constrOptim() function
See constrOptim.
lower, upper
Bounds on the variables for the "L-BFGS-B" method, or bounds in which to search for method "Brent".
See optim.
control.glm
a list of control parameters passed to the glm() function when method="glm".
vartype
character string specifying the type of variance matrix computed by flexrsurv: the inverse of the hessian matrix computed
at the MLE estimate (ie. the inverse of the observed information matrix) if vartype="oim", the inverse of the outer product of
the gradients if vartype="opg". The variance is not computed when vartype="none".
varmethod
character string specifying the method to compute the hessian matrix when vartype="oim". If varmethod="oim",
the hessian matrixe is computed by optim. If varmethod="numDeriv.hessian",
the hessian matrix is computed by numDeriv:hessian with method="Richardson".
If varmethod="numDeriv.jacobian", the hessian matrixe is computed by numDeriv:jacobian
with method="Richardson".
numDeriv.method.args
arguments passed to numDeriv:hessian or numDeriv:jacobian
when varmethod="numDeriv.hessian" or varmethod="numDeriv.jacobian". Arguments not specified remain with their
default values as specified in details. See numDeriv:grad for details about these parameters.
debug
control the volum of intermediate output
Details
A full description of the additive and the multiplicative both non-linear and non-proportional models is given respectively
in Remontet (2007) and Mahboubi (2011).
flexrsurv.ll is the workhorse function: it is not normally called
directly.
Value
flexrsurv returns an object of class "flexrsurv".
An object of class "flexrsurv" is a list containing at least the following components:
coefficients
a named vector of coefficients
loglik
the log-likelihood
var
estimated covariance matrix for the estimated coefficients
informationMatrix
estimated information matrix
bhlink
the linkk of baseline hazard:
if "identity" baseline = sum g0_i b_i(t);
if "log" log(baseline) = sum g0_i b_i(t);
init
vector of the starting values supplied
converged
logical, Was the optimlizer algorithm judged to have converged?
linear.predictors
the linear fit on link scale (not including the baseline hazard term if bhlink = "identity")
fitted.values
the estimated value of the hazard rate at each event time, obtained by transforming the linear predictors by the inverse of the link function
cumulative.hazard
the estimated value of the cumulative hazard in the time interval
call
the matched call
formula
the formula supplied
terms
the terms object used
data
the data argument
rate
the rate vector used
time
the time vector used
workingformula
the formula used by the fitter
optim.control
the value of the optim.control argument supplied
control.glm
the value of the control.glm argument supplied
method
the name of the fitter function used
References
Mahboubi, A., M. Abrahamowicz, et al. (2011). "Flexible modeling of the effects of continuous prognostic factors in relative survival." Stat Med 30(12): 1351-1365. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.4208")}
Remontet, L., N. Bossard, et al. (2007). "An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies." Stat Med 26(10): 2214-2228. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.2656")}
See Also
print.flexrsurv,
summary.flexrsurv,
logLik.flexrsurv,
predict.flexrsurv,
NPH,
NLL, and
NPHNLL.
Examples
if (requireNamespace("relsurv", quietly = TRUE) & requireNamespace("date", quietly = TRUE)) {
library(date)
# 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$slorate <- therate
# get the logit_start and logit_end
# logit start at age 18
tmpsurv <- Surv(rep(0, length(rdata$time)), rdata$time, rdata$cens)
HH <- getHazardFromTable(tmpsurv, startdate=rdata$year,
startage=rdata$age*365.25 , matchdata=rdata, ratetable=slopop,
age="age", year="year",
rmap=list(sex=sex),
agemin=18,
ratename = "poprate", cumrateendname ="cumrateend", cumrateentername ="cumrateenter"
)
rdata$slorate <- HH$poprate
rdata$logit_start <- log(exp(HH$cumrateenter)-1)
rdata$logit_end <- log(exp(HH$cumrateend)-1)
rdata$Id <- 1:dim(rdata)[1]
# change sex coding
rdata$sex01 <- rdata$sex -1
# fit a relative survival model with a non linear effect of age
# without correction of life table
# partial likelihood
fit00 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
data=rdata,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit00)
# fit a relative survival model with a non linear effect of age
# without correction of life table
# full likelihood
fit0 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit0)
# fit a relative survival model with a non linear effect of age
# with correction of life table
# full likelihood
fit1 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
Spline.CLT=flexrsurv:::R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit1)
print(coef(fit1))
# fit a relative survival model with a non linear effect of age
# with correction of life table, strabified by sex
# full likelihood
fit2 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
formula.table= ~sex,
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
Spline.CLT=flexrsurv:::R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit2)
AIC(fit0, fit1, fit2)
}
flexrsurv documentation built on June 7, 2023, 5:09 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| flexrsurvclt | R Documentation |
Fit Relative Survival Model and Correct Life Tables
Description
flexrsurvclt is used to fit relative survival regression model.
transition package.
Usage
flexrsurvclt(formula=formula(data),
formula.table=NULL,
data=parent.frame(),
Id,
baselinehazard=TRUE,
firstWCEIadditive=FALSE,
knots.Bh,
degree.Bh=3,
intercept.Bh=TRUE,
Spline=c("b-spline", "tp-spline", "tpi-spline"),
log.Bh=FALSE,
bhlink=c("log", "identity"),
Min_T=0,
Max_T=NULL,
model=c("additive","multiplicative"),
rate,
logit_start,
logit_end,
logit_start_byperiod = NULL,
logit_end_byperiod = NULL,
weights_byperiod = NULL,
Id_byperiod = NULL,
contrasts.table = NULL,
knots.table=c(-2.5,0,2.5),
degree.table=3,
Spline.table=c("restricted B-splines"),
Spline.CLT=R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
model_correction = c("cohort", "period"),
weights=NULL,
na.action=NULL,
datacontrol=NULL,
Idcontrol,
ratecontrol,
logit_startcontrol,
logit_endcontrol,
logit_start_byperiodcontrol = NULL,
logit_end_byperiodcontrol = NULL,
weights_byperiodcontrol = NULL,
Id_byperiodcontrol = NULL,
weightscontrol=NULL,
int_meth=c("GL", "CAV_SIM", "SIM_3_8", "BOOLE", "GLM", "BANDS"),
bands=NULL,
npoints=20,
stept=NULL,
init=NULL,
initbyglm=TRUE,
initbands=bands,
optim.control=list(trace=100, REPORT=1, fnscale=-1, maxit=25),
optim_meth=c("BFGS", "CG", "Nelder-Mead", "L-BFGS-B", "SANN", "Brent"),
Coptim.control=list(),
lower = -Inf,
upper = Inf,
control.glm=list(epsilon=1e-8, maxit=100, trace=FALSE,
epsilon.glm=.1, maxit.glm=25),
vartype = c("oim", "opg", "none"),
varmethod = c("optim", "numDeriv.hessian", "numDeriv.jacobian"),
numDeriv.method.args=list(eps=5e-7, d=0.001,
zero.tol=sqrt(.Machine$double.eps/7e-4), r=4, v=2),
debug=FALSE
)
flexrsurvclt.ll(formula=formula(data),
formula.table=NULL,
data=parent.frame(),
Id,
baselinehazard=TRUE,
firstWCEIadditive=FALSE,
knots.Bh,
degree.Bh=3,
Spline=c("b-spline", "tp-spline", "tpi-spline"),
log.Bh=FALSE,
bhlink=c("log", "identity"),
intercept.Bh=TRUE,
Min_T=0,
Max_T=NULL,
model=c("additive","multiplicative"),
rate,
logit_start,
logit_end,
logit_start_byperiod = NULL,
logit_end_byperiod = NULL,
weights_byperiod = NULL,
Id_byperiod = NULL,
contrasts.table = NULL,
knots.table=c(-2.5,0,2.5),
degree.table=3,
Spline.table=c("restricted B-splines"),
Spline.CLT=R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
model_correction = c("cohort", "period"),
weights=NULL,
na.action=NULL,
datacontrol=NULL,
Idcontrol,
ratecontrol,
logit_startcontrol,
logit_endcontrol,
logit_start_byperiodcontrol = NULL,
logit_end_byperiodcontrol = NULL,
weights_byperiodcontrol = NULL,
Id_byperiodcontrol = NULL,
weightscontrol=NULL,
int_meth=c("GL", "CAV_SIM", "SIM_3_8", "BOOLE", "GLM", "BANDS"),
bands=NULL,
npoints=20,
stept=NULL,
init=NULL,
optim.control=list(trace=100, REPORT=1, fnscale=-1, maxit=25),
Coptim.control= list(),
optim_meth=c("BFGS", "CG", "Nelder-Mead", "L-BFGS-B", "SANN", "Brent"),
lower = -Inf,
upper = Inf,
vartype = c("oim", "opg", "none"),
varmethod = c("optim", "numDeriv.hessian", "numDeriv.jacobian"),
numDeriv.method.args=list(eps=5e-7, d=0.001,
zero.tol=sqrt(.Machine$double.eps/7e-4), r=4, v=2),
debug=FALSE
)
Arguments
formula |
a formula object, with the response on the left of a ~ operator, and the terms on the
right. The response must be a survival object as returned by the |
formula.table |
a formula object, with empty left hand side, and the terms on the right. This is the formula of the proportional part of the correction model for the table table |
data |
a data.frame in which to interpret the variables named in the formulas. |
Id |
vector whose unique values defines the Ids of the subjects. |
baselinehazard |
if FALSE, no baseline hazard in the model |
firstWCEIadditive |
if TRUE, the first WCEI term in the formula is considered as the baseline |
knots.Bh |
the internal breakpoints that define the spline used to estimate the baseline hazard. Typical values are the mean or median for one knot, quantiles for more knots. |
degree.Bh |
degree of the piecewise polynomial of the baseline hazard. Default is 3 for cubic splines. |
intercept.Bh |
TRUE if the first bases is included in the baseline hazard. Default is TRUE. |
Spline |
a character string specifying the type of spline basis. "b-spline" for B-spline basis, "tp-spline" for truncated power basis and "tpi-spline" for monotone (increasing) truncated power basis. |
log.Bh |
logical value: if TRUE, an additional basis equal to log(time) is added to the spline bases of time. |
bhlink |
character string specifying the link function of the baseline hazard:
Default is |
Min_T |
minimum of time period which is analysed. Default is |
Max_T |
maximum of time period which is analysed. Default is |
model |
character string specifying the type of model for both non-proportional and non linear effects.
The model |
rate |
a vector of the background rate for a relevant comparative population to be used in the fitting process.
Should be a numeric vector (for relative survival model).
|
logit_start |
a vector of the logit of the cumulative hazard at the start of the
interval in the life table.
|
logit_end |
a vector of the logit of the cumulative hazard at the end of the
interval in the life table.
|
logit_start_byperiod, logit_end_byperiod, weights_byperiod, Id_byperiod |
A REMPLIR |
knots.table |
the internal breakpoints on the logit scale that define the knots of the spline used to estimate the correction model of the life table. |
degree.table |
degree of the piecewise polynomial of the spline used to estimate the correction model of the life table. Default is 3 for cubic splines. |
contrasts.table |
an optional list. See the |
Spline.table |
a character string specifying the type of spline basis of the the correction model of the life table. In this version, only "restricted B-splines" is available. "restricted B-splines" are B-spline basis with linear extrapolation + 2nd derivative at boundaries == 0. |
Spline.CLT |
a S4 object with method deriv() and evaluate().
The spline basis of the correction of the life table can be specified either by the parameters ( |
model_correction |
character string specifying A COMPLETER.
|
weights |
an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If not null, the total likelihood is the weighted sum of individual likelihood. |
na.action |
a missing-data filter function, applied to the model.frame. If |
datacontrol |
a data.frame in which to interpret the variables named in the formula for the control group. |
Idcontrol, ratecontrol, logit_startcontrol, logit_endcontrol, weightscontrol |
Id, rate, logit of the cumulative hazard at the start and the end of the intervalle in the life table, and weights for the control group |
logit_start_byperiodcontrol, logit_end_byperiodcontrol, weights_byperiodcontrol, Id_byperiodcontrol |
A REMPLIR |
int_meth |
character string specifying the the numerical integration method. Possible values are "GL" for Gauss-Legendre method, "CAV_SIM" for Cavalieri-Simpson's rule, "SIM_3_8" for the Simpson's 3/8 rule, "BOOLE" for the Boole's rule, or "BANDS" for the midpoint rule with specified bands. |
bands |
bands used to split data in the numerical integration when |
npoints |
number of points used in the numerical integration when |
stept |
scalar value of the time-step in numerical integration. It is required only when |
init |
starting values of the parameters. |
initbyglm |
a logical value indicating indicating how are found or refined init values. If TRUE, the fitting method described in Remontet et al.(2007)
is ued to find or refine starting values. This may speedup the fit. If FALSE, the maximisation of the likelihood starts at values
given in |
initbands |
bands used to split data when |
optim.control |
a list of control parameters passed to the |
optim_meth |
method to be used to optimize the likelihood.
See |
Coptim.control |
a list of control parameters passed to the |
lower, upper |
Bounds on the variables for the "L-BFGS-B" method, or bounds in which to search for method "Brent".
See |
control.glm |
a list of control parameters passed to the |
vartype |
character string specifying the type of variance matrix computed by |
varmethod |
character string specifying the method to compute the hessian matrix when |
numDeriv.method.args |
arguments passed to |
debug |
control the volum of intermediate output |
Details
A full description of the additive and the multiplicative both non-linear and non-proportional models is given respectively in Remontet (2007) and Mahboubi (2011).
flexrsurv.ll is the workhorse function: it is not normally called
directly.
Value
flexrsurv returns an object of class "flexrsurv".
An object of class "flexrsurv" is a list containing at least the following components:
coefficients |
a named vector of coefficients |
loglik |
the log-likelihood |
var |
estimated covariance matrix for the estimated coefficients |
informationMatrix |
estimated information matrix |
bhlink |
the linkk of baseline hazard:
if |
init |
vector of the starting values supplied |
converged |
logical, Was the optimlizer algorithm judged to have converged? |
linear.predictors |
the linear fit on link scale (not including the baseline hazard term if |
fitted.values |
the estimated value of the hazard rate at each event time, obtained by transforming the linear predictors by the inverse of the link function |
cumulative.hazard |
the estimated value of the cumulative hazard in the time interval |
call |
the matched call |
formula |
the formula supplied |
terms |
the |
data |
the |
rate |
the rate vector used |
time |
the time vector used |
workingformula |
the formula used by the fitter |
optim.control |
the value of the |
control.glm |
the value of the |
method |
the name of the fitter function used |
References
Mahboubi, A., M. Abrahamowicz, et al. (2011). "Flexible modeling of the effects of continuous prognostic factors in relative survival." Stat Med 30(12): 1351-1365. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.4208")}
Remontet, L., N. Bossard, et al. (2007). "An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies." Stat Med 26(10): 2214-2228. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.2656")}
See Also
print.flexrsurv,
summary.flexrsurv,
logLik.flexrsurv,
predict.flexrsurv,
NPH,
NLL, and
NPHNLL.
Examples
if (requireNamespace("relsurv", quietly = TRUE) & requireNamespace("date", quietly = TRUE)) {
library(date)
# 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$slorate <- therate
# get the logit_start and logit_end
# logit start at age 18
tmpsurv <- Surv(rep(0, length(rdata$time)), rdata$time, rdata$cens)
HH <- getHazardFromTable(tmpsurv, startdate=rdata$year,
startage=rdata$age*365.25 , matchdata=rdata, ratetable=slopop,
age="age", year="year",
rmap=list(sex=sex),
agemin=18,
ratename = "poprate", cumrateendname ="cumrateend", cumrateentername ="cumrateenter"
)
rdata$slorate <- HH$poprate
rdata$logit_start <- log(exp(HH$cumrateenter)-1)
rdata$logit_end <- log(exp(HH$cumrateend)-1)
rdata$Id <- 1:dim(rdata)[1]
# change sex coding
rdata$sex01 <- rdata$sex -1
# fit a relative survival model with a non linear effect of age
# without correction of life table
# partial likelihood
fit00 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
data=rdata,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit00)
# fit a relative survival model with a non linear effect of age
# without correction of life table
# full likelihood
fit0 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit0)
# fit a relative survival model with a non linear effect of age
# with correction of life table
# full likelihood
fit1 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
Spline.CLT=flexrsurv:::R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit1)
print(coef(fit1))
# fit a relative survival model with a non linear effect of age
# with correction of life table, strabified by sex
# full likelihood
fit2 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3,
Boundary.knots = c(24, 95)),
formula.table= ~sex,
rate=slorate,
logit_start=logit_start,
logit_end=logit_end,
data=rdata,
Id=Id,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Max_T=5400,
Spline = "b-spline",
Spline.CLT=flexrsurv:::R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3),
initbyglm=TRUE,
initbands=seq(0, 5400, 100),
int_meth= "BANDS",
bands=seq(0, 5400, 50)
)
summary(fit2)
AIC(fit0, fit1, fit2)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.