Function for computing hazard ratio and risk ratio (ratio
of cumulative probabilities of failure) estimates from a model fitted
with the mexhaz function. Corresponding confidence intervals
are based on the Delta Method or Monte Carlo simulation (based on the
assumption of multivariate normality of the model parameter
estimates). This function allows the computation of estimates at one
time point for several vectors of covariates or for one vector of
covariates at several time points. When the model includes a random effect, three types of
predictions can be made: (i) marginal predictions (obtained by
integration over the random effect distribution), (ii) cluster-specific
posterior predictions for an existing cluster, or (iii) conditional
predictions for a given quantile of the random effect distribution (by
default, for the median value, that is, 0).
riskfunc(object, time.pts, data, data.0, marginal = TRUE, quant.rdm = 0.5,
cluster = NULL, quant.rdm.0 = 0.5, cluster.0 = NULL, type = c("hr", "rr"),
conf.int = c("delta", "simul"), level = 0.95, nb.sim = 10000, seed = NULL,
dataset = NULL)An object of class resMexhaz that can be used by the function
plot.resMexhaz to produce graphics
of the hazard ratio or risk ratio curve. It contains the following
elements:
a data.frame consisting of: the time points at
which values have been calculated; the
hazard ratio / risk ratio values with their confidence limits.
type of results returned by the function. The value is
used by plot.resMexhaz and lines.resMexhaz, and can take
the values "hr" (hazard ratio) or "rr" (risk ratio).
value used by
plot.resMexhaz and lines.resMexhaz, and set to
FALSE when estimates are computed at several time
points for one vector of covariates.
method used to compute confidence limits.
level of confidence used to compute confidence limits.
an object of class mexhaz, corresponding to a
hazard-based regression model fitted with the mexhaz function.
a vector of numerical values representing the time points at which predictions are requested. Time values greater than the maximum follow-up time on which the model estimation was based are discarded.
a data.frame containing the values of the
covariates of the population for which hazard ratios or risk ratios
are to be calculated.
a data.frame containing the values of the
covariates of the reference population. Each row of data.0 is
used as the reference for the corresponding row of data.
logical value controlling the type of predictions
returned by the function when the model includes a random
intercept. When TRUE, marginal predictions are computed. The
marginal survival is obtained by integrating the predicted survival
over the distribution of the random effect. When FALSE
(default value), conditional predictions depending on the value of
the cluster argument are calculated.
numerical value (between 0 and 1) specifying the
quantile of the random effect distribution that should be used when
requesting conditional predictions. The default value is set to 0.5
(corresponding to the median, that is a value of the random effect
of 0). This argument is ignored if the model is a fixed effect
model, when the marginal argument is set to TRUE, or
the cluster argument is not NULL.
a single value corresponding to the name of the cluster for
which posterior predictions should be calculated. These predictions
are obtained by integrating over the cluster-specific posterior
distribution of the random effect and thus require the original
dataset. The dataset can either be provided as part of the
mexhaz object given as argument or by specifying the name of
the dataset in the dataset argument (see below). The
cluster argument is not used if the model is a fixed effect
model. The default value is NULL: this corresponds to
marginal predictions (if marginal is set to TRUE, the preferred
option), or to conditional predictions for a given quantile (by
default, the median) of the distribution of the random
effect (if marginal is set to FALSE).
random effect distribution quantile value to be used with data.0 (see
argument quant.rdm for details).
cluster value to be used with data.0 (see
argument cluster for details).
argument specifying the type of predictions to be
calculated. Selection can be made between "hr" (hazard ratio)
and "rr" (risk ratio, i.e., ratio of cumulative failure
probabilities).
method to be used to compute confidence
limits. Selection can be made between the following options:
"delta" for the Delta Method (default value);
"simul" for Monte Carlo simulations (can be time-consuming,
especially for models using B-splines for the logarithm of the
baseline hazard).
a number in (0,1) specifying the level of confidence for
computing the confidence intervals of the hazard and the
survival. By default, this argument is set to 0.95.
integer value representing the number of simulations
used to estimate the confidence limits for the (excess) hazard
and the (net) survival. This argument is used only if
conf.int is set to "simul".
argument allowing the user to set a random seed for
simulations (only relevant when conf.int is set to
"simul"). The default value is set to NULL in which case
a random seed is automatically generated inside the function.
original dataset used to fit the mexhaz object
given as argument to the function. This argument is only necessary
if cluster-specific posterior predictions are requested (and if the
dataset is not already provided in the mexhaz object). The
default value is set to NULL.
Hadrien Charvat, Aurelien Belot
Charvat H, Remontet L, Bossard N, Roche L, Dejardin O, Rachet B, Launoy G, Belot A; CENSUR Working Survival Group. A multilevel excess hazard model to estimate net survival on hierarchical data allowing for non-linear and non-proportional effects of covariates. Stat Med 2016;35:3066-3084 (doi: 10.1002/sim.6881)
Skrondal A, Rabe-Hesketh S. Prediction in multilevel generalized linear models. J R Stat Soc A Stat Soc 2009;172(3):659-687 (doi: 10.1111/j.1467-985X.2009.00587.x).
plot.resMexhaz, lines.resMexhaz
data(simdatn1)
## Fit of a fixed-effect hazard model, with the baseline hazard
## described by a linear B-spline with two knots at 1 and 5 year and with
## effects of age (agecr), deprivation index (depindex) and sex (IsexH)
Mod_bs1_2 <- mexhaz(formula=Surv(time=timesurv,
event=vstat)~agecr+depindex+IsexH, data=simdatn1, base="exp.bs",
degree=1, knots=c(1,5), verbose=0)
## Risk ratio along time for agecr=0.2 compared to agecr=0.1
RR_Modbs1_2 <- riskfunc(Mod_bs1_2, time.pts=seq(0,10,le=101),
data=data.frame(agecr=0.2,depindex=0,IsexH=1),
data.0=data.frame(agecr=0.1,depindex=0,IsexH=1),type="rr",
conf.int="delta")
plot(RR_Modbs1_2)
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