heemod package provides a number of ways to estimate transition probabilities from survival distributions. Survival distributions can come from at least three different sources:
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( 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) )
Fitted parametric models are created using
library(flexsurv) <- 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
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(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( 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
state_time) needs to be passed to the function as a
compute_surv(surv_dist_2, time = 1:5)
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)
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 ) 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 )
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 ) <- define_strategy( stratPS transition = ps, A = sA, B = sB, C = sC ) <- run_model( resPS stratPS,cycles = 15, cost = cost, effect = ut ) plot(resPS)