psrwe_powerp: Get posterior samples based on PS-power prior approach

In psrwe: PS-Integrated Methods for Incorporating RWE in Clinical Studies

Get posterior samples based on PS-power prior approach

Description

Draw posterior samples of the parameters of interest for the PS-power prior approach

Usage

psrwe_powerp(
  dta_psbor,
  v_outcome = "Y",
  outcome_type = c("continuous", "binary"),
  prior_type = c("fixed", "random"),
  ...,
  seed = NULL
)

Arguments

dta_psbor	A class PSRWE_BOR object generated by psrwe_borrow.
v_outcome	Column name corresponding to the outcome.
outcome_type	Type of outcomes: continuous or binary.
prior_type	Whether treat power parameter as fixed (fixed) or fully Bayesian (random).
...	extra parameters for calling function rwe_stan.
seed	Random seed.

Value

A class PSRWE_RST list with the following objects

Observed

Observed mean and SD of the outcome by group, arm and stratum

Control

A list of estimated mean and SD of the outcome by stratum in the control arm

Treatment

A list of estimated mean and SD of the outcome by stratum in the treatment arm for RCT

Effect

A list of estimated mean and SD of the treatment effect by stratum for RCT

Borrow

Borrowing information from dta_psbor

stan_rst

Result from STAN sampling

Examples

data(ex_dta)
dta_ps <- psrwe_est(ex_dta,
       v_covs = paste("V", 1:7, sep = ""),
       v_grp = "Group",
       cur_grp_level = "current")
ps_borrow <- psrwe_borrow(total_borrow = 30, dta_ps)
rst <- psrwe_powerp(ps_borrow, v_outcome = "Y_Con", seed = 123)

psrwe documentation built on March 18, 2022, 5:33 p.m.