SurrogateBMA (version 1.0)

post.model: 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)

Value

a numeric vector; the posterior probabilities of the candidate models.

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, \(\sigma^2\) Gamma0) prior is assumed.

Author

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)