| Type: | Package |
| Title: | Flexible Evaluation of Surrogate Markers with Bayesian Model Averaging |
| Version: | 1.0 |
| Date: | 2024-01-21 |
| Description: | Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) <doi:10.1002/sim.9986>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | Rcpp (≥ 1.0.9), MCMCpack, mvtnorm, Rsurrogate |
| LinkingTo: | Rcpp, RcppEigen, RcppNumerical |
| Depends: | R (≥ 3.5.0) |
| LazyData: | true |
| NeedsCompilation: | yes |
| Packaged: | 2024-01-29 21:01:37 UTC; yunshanduan |
| Author: | Yunshan Duan [aut, cre], Layla Parast [aut] |
| Maintainer: | Yunshan Duan <yunshan@utexas.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-01 10:20:06 UTC |
Flexible Evaluation of Surrogate Markers with Bayesian Model Averaging
Description
Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using Bayesian Model Averaging approach.
Author(s)
Yunshan Duan
Maintainer: Yunshan Duan <yunshan@utexas.edu>
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Calculates the proportion of treatment effect explained
Description
Calculates the proportion of treatment effect on the primary outcome explained by the treatment effect on the surrogate marker using Bayesian Model Averaging (BMA). This function is intended to be used for a fully observed continuous outcome and one single continuous surrogate marker. The user can also request to calculate a 95% credible interval, evaluated by Bayesian bootstrapping. A robust option is provided where we choose a more preferrable approach between the BMA and non-parametric methods based on cross-validation.
Usage
R.BMA.est(Y, S, A, method = "BMA", nmc = 500, nBB = 100, conf.int = TRUE,
alpha = 0.05, prior.para = NULL, kfold.k = 3)
Arguments
Y |
numeric vector; primary outcome, assumed to be continuous. |
S |
numeric vector; surrogate marker, assumed to be continuous. |
A |
numeric vector; treatment arm, assumed to be binary. The treatment arm = 1 when the patient is enrolled in the treatment group, treatment arm = 0 when in the control group. |
method |
"BMA" or "robust"; Default is "BMA". If method = "robust", we choose a more preferrable approach between the BMA and non-parametric methods based on cross-validation. |
nmc |
number of MCMC samples in posterior inference, default is 500. |
nBB |
number of replicates in Bayesian bootstrap, default is 100. Igored if conf.int = FALSE. |
conf.int |
TRUE or FALSE; indicates whether a 95% credible interval for the proportion explained is requested, default is TRUE. |
alpha |
the confidence level for the credible interval, the 100(1 - |
prior.para |
a list of hyper-parameters in the inverse-Gamma-Normal prior for the variance and coefficients, including a0_list, b0_list, mu0_list, Gamma0_list, Gamma0_inv_list , each being a list of 5 with 5 parameters under the 5 different candidate models. An Inv-Gamma(a0, b0) - Normal(mu0, |
kfold.k |
the value of k in k-fold cross validation, default is 3. Ignored if method = "BMA". |
Value
R.est |
the estimate of the proportion explained by the surrogate marker. |
p.model |
the posterior probability of the candidate models being true. |
ci |
the credible (confidence) interval for the proportion explained by the surrogate marker. |
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Examples
data(exampleData)
R.BMA.est(Y = exampleData$Y, S = exampleData$S, A = exampleData$A)
Calculates the proportion of treatment effect explained
Description
Calculates the proportion of treatment effect on the primary outcome explained by the treatment effect on the surrogate marker using Bayesian Model Averaging. This function is intended to be used for a fully observed continuous outcome and one single continuous surrogate marker. The user can also request to calculate a 95% credible interval, evaluated by Bayesian bootstrapping. The MSE of predicting the primary outcome in the treatment can be calculated when test data is given.
Usage
R.BMAonly(Y, S, A, nmc = 500, nBB = 100, conf.int = TRUE, alpha = 0.05,
prior.para = NULL, testdata = FALSE, Ytest = NULL, Stest = NULL, Atest = NULL)
Arguments
Y |
numeric vector; primary outcome, assumed to be continuous. |
S |
numeric vector; surrogate marker, assumed to be continuous. |
A |
numeric vector; treatment arm, assumed to be binary. The treatment arm = 1 when the patient is enrolled in the treatment group, treatment arm = 0 when in the control group. |
nmc |
number of MCMC samples in posterior inference, default is 500. |
nBB |
number of replicates in Bayesian bootstrap, default is 100. Igored if conf.int = FALSE. |
conf.int |
TRUE or FALSE; indicates whether a 95% credible interval for the proportion explained is requested, default is TRUE. |
alpha |
the confidence level for the credible interval, the 100(1 - |
prior.para |
a list of hyper-parameters in the inverse-Gamma-Normal prior for the variance and coefficients, including a0_list, b0_list, mu0_list, Gamma0_list, Gamma0_inv_list , each being a list of 5 with 5 parameters under the 5 different candidate models. An Inv-Gamma(a0, b0) - Normal(mu0, |
testdata |
TRUE or FALSE; indicates whether test data is available, default is FALSE. The MSE of predicting the primary outcome is evaluated if TRUE. |
Ytest |
numeric vector; primary outcome in test data, assumed to be continuous. Default is NULL, ignored if testdata = FALSE. |
Stest |
numeric vector; surrogate marker in test data, assumed to be continuous. Default is NULL, ignored if testdata = FALSE. |
Atest |
numeric vector; treatment arm in test data, assumed to be binary. The treatment arm = 1 when the patient is enrolled in the treatment group, treatment arm = 0 when in the control group. Default is NULL, ignored if testdata = FALSE. |
Value
R.BMA |
the estimate of the proportion explained by the surrogate marker. |
p.model |
the posterior probability of the candidate models being true. |
ci |
the credible interval for the proportion explained by the surrogate marker. |
MSE |
the MSE of predicting the primary outcome for test data. |
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Examples
data(exampleData)
R.BMAonly(Y = exampleData$Y, S = exampleData$S, A = exampleData$A)
Calculates the R value given model and parameters
Description
Provides a function to calculate F_m(theta_m).
Usage
R.m.theta(S, m, theta, A, weight = FALSE, nBB = 100)
Arguments
S |
|
m |
|
theta |
|
A |
|
weight |
|
nBB |
Value
a numeric value or a numeric verctor when weight = TRUE. the value of R or the values of R using Bayesian bootstap replicates of the surrogate marker.
Author(s)
Yunshan Duan
Calculate prodiction MSE for nonparametric method
Description
Function to calculate prodiction MSE for nonparametric method.
Usage
cv.np(Y, S, A, Ytest, Stest, Atest)
Arguments
Y |
|
S |
|
A |
|
Ytest |
|
Stest |
|
Atest |
Value
a numeric value; MSE in cross-validation.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Example data
Description
Example data
Usage
data("exampleData")Format
A data frame with 400 rows and 3 variables:
Ythe primary outcome
Sthe surrogate marker
Athe treatmentt arm
Examples
data(exampleData)
names(exampleData)
Function to be integrated when calculating the expected primary outcome
Description
Function to be integrated when calculating the expected primary outcome.
Usage
fpred.int(eps, mu, sig, m)
Arguments
eps |
|
mu |
|
sig |
|
m |
Value
a function.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Generates the default prior hyperparameters
Description
Generates a list of hyper-parameters we use by default in the inverse-Gamma-Normal prior for the variance and coefficients, including a0_list, b0_list, mu0_list, Gamma0_list, Gamma0_inv_list , each being a list of 5 with 5 parameters under the 5 different candidate models. An Inv-Gamma(a0, b0) - Normal(mu0, \sigma^2 Gamma0) prior is assumed.
Usage
gen.prior()
Value
A list of hyper-parameters, including a0_list, b0_list, mu0_list, Gamma0_list, Gamma0_inv_list , each being a list of 5 with 5 parameters under the 5 different candidate models.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Examples
gen.prior()
# if we want to change the prior parameter a0 in model 1
prior.para <- gen.prior()
prior.para$a0_list[[1]] <- 1
Simple function using Rcpp
Description
Simple function using Rcpp
Usage
gpxi_int(i, m, beta, S0, S1, sig2)
Examples
## Not run:
gpxi_int(i, m, beta, S0, S1, sig2)
## End(Not run)
Calculates the posterior probability of the candidate models
Description
Gives the posterior probability of each candidate model being true.
Usage
post.model(Y, S, A, prior.para = NULL)
Arguments
Y |
numeric vector; primary outcome, assumed to be continuous. |
S |
numeric vector; surrogate marker, assumed to be continuous. |
A |
numeric vector; treatment arm, assumed to be binary. The treatment arm = 1 when the patient is enrolled in the treatment group, treatment arm = 0 when in the control group. |
prior.para |
a list of hyper-parameters in the inverse-Gamma-Normal prior for the variance and coefficients, including a0_list, b0_list, mu0_list, Gamma0_list, Gamma0_inv_list , each being a list of 5 with 5 parameters under the 5 different candidate models. An Inv-Gamma(a0, b0) - Normal(mu0, |
Value
a numeric vector; the posterior probabilities of the candidate models.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Examples
data(exampleData)
post.model(Y = exampleData$Y, S = exampleData$S, A = exampleData$A)
Generates posterior samples of the parameters
Description
Provides a function to generate MCMC samples of p(theta_m | M = m, Data).
Usage
post.theta(Y, S, A, m, nmc = 500, prior.para = NULL)
Arguments
Y |
|
S |
|
A |
|
m |
|
nmc |
|
prior.para |
Value
a numeric matrix; each row contains a MC sample from the posterior of the parameters given a certain model.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Calculate the expected primary outcome in the treatment group given the model and the parameters
Description
Provides a function to calculate the expected primary outcome in the treatment group given the model and the parameters.
Usage
pred.func(theta, ST, m)
Arguments
theta |
|
ST |
|
m |
Value
a numeric vector.
Author(s)
Yunshan Duan
References
Duan, Y. and Parast, L., 2023. Flexible evaluation of surrogate markers with Bayesian model averaging. Statistics in Medicine.
Simple function using Rcpp
Description
Simple function using Rcpp
Usage
rcpp_hello_world()
Examples
## Not run:
rcpp_hello_world()
## End(Not run)