This package estimates an ensemble of parametric survival models with different parametric families and uses Bayesian model averaging to combine them. The RoBSA ensemble uses Bayes factors to test for the presence or absence of effects of the individual predictors and evaluates the support for each parametric family. The resulting model-averaged parameter estimates are based on posterior model probabilities. The user can define a wide range of prior distributions for the effect size, intercepts, and auxiliary parameters. The package provides convenient functions for summary, visualizations, and fit diagnostics.
See Bartoš et al. (2022) (https://doi.org/10.1186/s12874-022-01676-9) introducing the methodology.
The package requires JAGS 4.3.1 to be installed. The development version of the package can be installed from GitHub:
devtools::install_github("FBartos/RoBSA")To illustrate the package’s functionality, we use the veteran data set
from the survival package containing 137 survival times of patients
from a randomized trial of two treatment regimens for lung cancer. We
start by loading the package and data set.
library("RoBSA")
#> Loading required namespace: runjags
data(cancer, package = "survival")
head(veteran)
#> trt celltype time status karno diagtime age prior
#> 1 1 squamous 72 1 60 7 69 0
#> 2 1 squamous 411 1 70 5 64 10
#> 3 1 squamous 228 1 60 3 38 0
#> 4 1 squamous 126 1 60 9 63 10
#> 5 1 squamous 118 1 70 11 65 10
#> 6 1 squamous 10 1 20 5 49 0Before we fit the RoBSA ensemble with five accelerated failure times
parametric families (exponential, Weibull, log-normal, log-logistic, and
gamma), we must specify prior distributions for the intercepts and
auxiliary parameters (governing the scales and shapes) of the competing
parametric families. Here, we assume that we would expect the median
survival type in the standard treatment group to be 5 years with an
interquartile range of 10 years. We set the standard deviation of the
prior distributions to 0.5, which provides us with a satisfactory degree
of uncertainty about the parameter values. We obtain the corresponding
prior distributions by using the calibrate_quartiles() function.
Subsequently, we print the list containing priors for the intercepts and
auxiliary parameters.
priors <- calibrate_quartiles(median_t = 5, iq_range_t = 10, prior_sd = 0.5)
priors
#> $intercept
#> $intercept$`exp-aft`
#> Normal(1.98, 0.5)
#> $intercept$`weibull-aft`
#> Normal(2.06, 0.5)
#> $intercept$`lnorm-aft`
#> Normal(1.61, 0.5)
#> $intercept$`llogis-aft`
#> Normal(1.61, 0.5)
#> $intercept$`gamma-aft`
#> Normal(2.46, 0.5)
#>
#> $aux
#> $aux$`weibull-aft`
#> Lognormal(-0.36, 0.57)
#> $aux$`lnorm-aft`
#> Lognormal(0.2, 0.37)
#> $aux$`llogis-aft`
#> Lognormal(0.15, 0.39)
#> $aux$`gamma-aft`
#> Lognormal(-0.53, 0.63)We create a new data.frame object containing a fit ready data set. We
(1) transform the survival times to years, (2) code the treatment
variable as a factor and set "standard" as the default level (to test
for effect of the "new" treatment effect; so the intercept of the
model corresponds to the standard treatment and the treatment estimate
to the improvement in the test treatment), and (3) scale the Karnofsky
performance score (karno) to range from 0-1 (i.e., the coefficient
estimate corresponds to the biggest possible difference).
df <- data.frame(
time = veteran$time / 12,
status = veteran$status,
treatment = factor(ifelse(veteran$trt == 1, "standard", "new"), levels = c("standard", "new")),
karno_scaled = veteran$karno / 100
)
head(df)
#> time status treatment karno_scaled
#> 1 6.0000000 1 standard 0.6
#> 2 34.2500000 1 standard 0.7
#> 3 19.0000000 1 standard 0.6
#> 4 10.5000000 1 standard 0.6
#> 5 9.8333333 1 standard 0.7
#> 6 0.8333333 1 standard 0.2We proceed by specifying a RoBSA model intended to test an informed
hypothesis of the presence of the treatment effect centered around the
log(AF) of 0.3 with the sd of 0.1,5 via the informed prior distribution
prior_factor() function. This
allows us to specify the type of contrast we want to use. Here, we use
"treatment" contrast to estimate differences from the default level
(we could also use orthonormal to test for a difference of the
individual levels from the grand mean). Furthermore, we adjust for the
Karnofsky performance score by setting a wider centered standard normal
prior distribution test_predictors = "treatment". Finally, we pass the appropriate prior
distributions to the priors, prior_intercept, prior_aux arguments,
set seed = 1 for reproducibility, and use parallel = TRUE for
speeding up the computation.
fit.test <- RoBSA(
Surv(time, status) ~ treatment + karno_scaled,
data = df,
priors = list(
treatment = prior_factor("normal", parameters = list(mean = 0.30, sd = 0.15),
truncation = list(0, Inf), contrast = "treatment"),
karno_scaled = prior("normal", parameters = list(mean = 0, sd = 1))
),
test_predictors = "treatment",
prior_intercept = priors[["intercept"]],
prior_aux = priors[["aux"]],
parallel = TRUE, seed = 1
) The summary() functions provides the main summary of the fitted model.
summary(fit.test)
#> Call:
#> RoBSA(formula = Surv(time, status) ~ treatment + karno_scaled,
#> data = df, priors = list(treatment = prior_factor("normal",
#> parameters = list(mean = 0.3, sd = 0.15), truncation = list(0,
#> Inf), contrast = "treatment"), karno_scaled = prior("normal",
#> parameters = list(mean = 0, sd = 1))), test_predictors = "treatment",
#> prior_intercept = priors[["intercept"]], prior_aux = priors[["aux"]],
#> parallel = TRUE, seed = 1)
#>
#> Robust Bayesian survival analysis
#> Distributions summary:
#> Models Prior prob. Post. prob. Inclusion BF
#> exp-aft 2/10 0.200 0.321 1.894
#> weibull-aft 2/10 0.200 0.026 0.105
#> lnorm-aft 2/10 0.200 0.149 0.699
#> llogis-aft 2/10 0.200 0.499 3.991
#> gamma-aft 2/10 0.200 0.005 0.019
#>
#> Components summary:
#> Models Prior prob. Post. prob. Inclusion BF
#> treatment 5/10 0.500 0.129 0.148
#>
#> Model-averaged estimates:
#> Mean Median 0.025 0.975
#> treatment[new] 0.019 0.000 0.000 0.224
#> karno_scaled 2.499 2.508 1.647 3.336In the first table, we see that most posterior model probability is retained by the log-logistic (0.499), exponential (0.321), and log-normal (0.149) family, with the inclusion Bayes factors quantifying the change from prior to posterior model probabilities.
The second table then summarizes information about hypothesis tests of the model components. Here, in the RoBSA ensemble intended for testing, we are interested in the inclusion Bayes factor for the treatment effect. We find Bayes factor 0.148, indicating that there is moderate evidence in favor of no treatment effect (1/0.148 = 6.76) in comparison to our informed hypothesis of a positive treatment effect.
Now we try to estimate the model-averaged estimate of the difference
between the two treatments. We change the prior distribution from the
informed positive treatment effect to a neutral standard normal prior
distribution allowing for both positive and negative treatment effect.
We further set test_predictors to "" in order to omit models
assuming zero treatment effect.
fit.est <- RoBSA(
Surv(time, status) ~ treatment + karno_scaled,
data = df,
priors = list(
treatment = prior_factor("normal", parameters = list(mean = 0, sd = 1),
contrast = "treatment"),
karno_scaled = prior("normal", parameters = list(mean = 0, sd = 1))
),
test_predictors = "",
prior_intercept = priors[["intercept"]],
prior_aux = priors[["aux"]],
parallel = TRUE, seed = 1
) summary(fit.est)
#> Call:
#> RoBSA(formula = Surv(time, status) ~ treatment + karno_scaled,
#> data = df, priors = list(treatment = prior_factor("normal",
#> parameters = list(mean = 0, sd = 1), contrast = "treatment"),
#> karno_scaled = prior("normal", parameters = list(mean = 0,
#> sd = 1))), test_predictors = "", prior_intercept = priors[["intercept"]],
#> prior_aux = priors[["aux"]], parallel = TRUE, seed = 1)
#>
#> Robust Bayesian survival analysis
#> Distributions summary:
#> Models Prior prob. Post. prob. Inclusion BF
#> exp-aft 1/5 0.200 0.254 1.360
#> weibull-aft 1/5 0.200 0.021 0.086
#> lnorm-aft 1/5 0.200 0.206 1.036
#> llogis-aft 1/5 0.200 0.515 4.252
#> gamma-aft 1/5 0.200 0.004 0.016
#>
#> Model-averaged estimates:
#> Mean Median 0.025 0.975
#> treatment[new] -0.179 -0.176 -0.561 0.193
#> karno_scaled 2.539 2.544 1.715 3.336We again use the summary() function to obtain information about the
fitted model. We find out that the experimental treatment led to notably
shorter survival times with the mean model-averaged log(AF) = -0.19, 95%
CI[-0.56, -0.19]. Furthermore, we observe an enormous effect of the
scaled Karnofsky performance score, showing that moving from 0 to 1
increases the survival times with the mean model-averaged log(AF) =
2.54, 95% CI [1.72, 3.34].
To assess the convergence of the MCMC model, we can use the
diagnostics = TRUE argument in the summary() function. The resulting
table shows the maximum MCMC error, min effective sample size and
maximum R-hat for each of the models.
summary(fit.est, type = "diagnostics")
#> Call:
#> RoBSA(formula = Surv(time, status) ~ treatment + karno_scaled,
#> data = df, priors = list(treatment = prior_factor("normal",
#> parameters = list(mean = 0, sd = 1), contrast = "treatment"),
#> karno_scaled = prior("normal", parameters = list(mean = 0,
#> sd = 1))), test_predictors = "", prior_intercept = priors[["intercept"]],
#> prior_aux = priors[["aux"]], parallel = TRUE, seed = 1)
#>
#> Robust Bayesian survival analysis
#> Diagnostics overview:
#> Model Distribution Prior treatment Prior karno_scaled
#> 1 exp-aft treatment contrast: Normal(0, 1) Normal(0, 1)
#> 2 weibull-aft treatment contrast: Normal(0, 1) Normal(0, 1)
#> 3 lnorm-aft treatment contrast: Normal(0, 1) Normal(0, 1)
#> 4 llogis-aft treatment contrast: Normal(0, 1) Normal(0, 1)
#> 5 gamma-aft treatment contrast: Normal(0, 1) Normal(0, 1)
#> max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)
#> 0.01473 0.037 712 1.002
#> 0.01706 0.040 625 1.002
#> 0.01486 0.037 745 1.001
#> 0.01486 0.038 694 1.003
#> 0.01500 0.041 589 1.008We find that while the R-hat and max MCMC error is satisfactory for all
models from the RoBSA estimation ensemble, we might wish for a larger
ESS. To achieve that, we would simply increase the number of sampling
MCMC iterations by using the iter argument in the RoBSA() function.
Furthermore detailed diagnostics such as trace plot, autocorrelation
plots, and density plot for each parameter/model are provided via the
diagnostics_trace(), diagnostics_autocorrelation(), and
diagnostics_density() functions.
We can also visualize the model ensemble predictions for survival or
hazard. We visualize the model-averaged survival for each treatment
group via the plot_survival() and using the predictor = "treatment"
argument. By default, the remaining predictors are set to their
mean/default level, but predictions for different values can be obtained
by specifying the covariates_data argument.
plot_survival(fit.est, predictor = "treatment")
In alignment with the previous summary, we see that the model-averaged
survival for the new treatment group is below the estimated treatment of
the standard treatment group. Similarly, we could also visualize the
model-averaged hazard plot_hazard() or obtain numerical estimates for
the survival, hazard, density, mean survival, and standard deviation of
the survival at different levels of detail via the predict() function.
Bartoš, F., Aust, F., & Haaf, J. M. (2022). Informed Bayesian survival analysis. BMC Medical Research Methodology. https://doi.org/10.1186/s12874-022-01676-9