The heemod
package provides a number of ways to estimate
transition probabilities from survival distributions. Survival
distributions can come from at least three different sources:
flexsurv::flexsurvreg()
.survival::survfit()
.Once defined, each of these types of distributions can be combined and modified using a standard set of operations.
User-defined parametric distributions are created using the
define_survival()
and define_spline_survival()
functions:
<- define_survival(
surv_dist_1 distribution = "exp",
rate = .5
)
<- define_spline_survival(
surv_dist_2 scale = "odds",
gamma = c(-11.643, 1.843, 0.208),
knots = c(4.077537, 5.883183, 6.458338)
)
## Le chargement a nécessité le package : flexsurv
Fitted parametric models are created using
flexsurv::flexsurvreg()
and
flexsurv::flexsurvspline()
:
library(flexsurv)
## Le chargement a nécessité le package : survival
<- flexsurvreg(
fit_w formula = Surv(futime, fustat) ~ 1,
data = ovarian, dist = "weibull"
)plot(fit_w)
<- flexsurvspline(
fit_spl formula = Surv(futime, fustat) ~ 1,
data = ovarian,
scale = "odds",
k=1
)plot(fit_spl)
Fitted models can include covariates. In order to use a model with
covariates in heemod, you can use the set_covariates()
function on the fitted model and provide as additional arguments the
covariate values you want to model. You can also provide a data frame of
covariate levels to aggregate survival probabilites over different
groups. By default, heemod will aggregate over predicted survival
probabilities for each subject in the dataset to which the model was
fit.
<- flexsurvreg(
fit_cov formula = Surv(rectime, censrec) ~ group,
dist = "weibull",
data = bc
)plot(fit_cov)
<- set_covariates(fit_cov, group = "Good")
fitcov_good <- set_covariates(fit_cov, group = "Medium")
fitcov_medium <- set_covariates(fit_cov, group = "Poor") fitcov_poor
Similar functionality is also available for Kaplan-Meiers created
using survival::survfit()
library(survival)
<- survfit(
km_1 formula = Surv(futime, fustat) ~ 1,
data = ovarian
)<- survfit(
km_cov formula = Surv(rectime, censrec) ~ group,
data = bc
)plot(km_cov)
<- set_covariates(km_cov, group = "Good")
km_good <- set_covariates(km_cov, group = "Medium")
km_medium <- set_covariates(km_cov, group = "Poor") km_poor
Once defined, treatment effects of various types can be applied to any survival distribution:
apply_hr()
.apply_or()
.apply_af()
.<- apply_hr(km_poor, hr = 0.5)
km_poor_ph <- apply_af(km_medium, af = 1.2) km_medium_af
In addition, distributions can be combined using a variety of operations:
join()
.mix()
.add_hazards()
.<- join(
km_poor_join
km_poor,
fitcov_poor,at = 365
)<- mix(
models_all
fitcov_good, fitcov_medium, fitcov_poor,weights = c(0.25, 0.25, 0.5)
)<- add_hazards(
combined_risks
fit_w, fitcov_good )
The transition or survival probabilities are computed with
compute_surv()
. Time (usually model_time
or
state_time
) needs to be passed to the function as a
time
argument.
compute_surv(surv_dist_2, time = 1:5)
## [1] 8.780223e-06 2.271877e-05 3.500128e-05 4.649850e-05 5.747782e-05
All these operations can be chained with the %>%
piping operator.
%>%
fit_cov set_covariates(group = "Good") %>%
apply_hr(hr = 2) %>%
join(
fitcov_poor,at = 3
%>%
) mix(
fitcov_medium,weights = c(0.25, 0.75)
%>%
) add_hazards(
fit_w%>%
) compute_surv(time = 1:5)
## [1] 0.0004011356 0.0004736851 0.0005069766 0.0005490092 0.0005692261
For the example we define a simple model with only 1 strategy.
<- define_parameters(
param p1 = compute_surv(
surv_dist_1,time = model_time # can also be state_time
),p2 = km_1 %>%
join(fit_w, at = 730) %>%
compute_surv(
time = model_time,
cycle_length = 365 # time is in days in km_medium, in years in model_time
)
)
<- define_transition(
tm - p2, p2,
C, p1 0, C, p2,
0, 0, C
)
## No named state -> generating names.
plot(tm)
<- define_state(
sA cost = 10, ut = 1
)<- define_state(
sB cost = 20, ut = .5
)<- define_state(
sC cost = 0, ut = 0
)
<- define_strategy(
stratTM transition = tm,
A = sA, B = sB, C = sC
)
<- run_model(
resTM parameters = param,
stratTM,cycles = 15,
cost = cost, effect = ut
)
## No named model -> generating names.
plot(resTM)
A partitioned survival model can also be computed:
<- define_part_surv(
ps pfs = surv_dist_1,
os = km_1 %>%
join(fit_w, at = 730),
cycle_length = c(1, 365) # 1 for pfs, 365 for os
)
## No named state -> generating names.
<- define_strategy(
stratPS transition = ps,
A = sA, B = sB, C = sC
)
<- run_model(
resPS
stratPS,cycles = 15,
cost = cost, effect = ut
)
## No named model -> generating names.
plot(resPS)