post.model {SurrogateBMA}

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, \sigma^2 Gamma0) prior is assumed.

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)