est: Evaluate point estimators of the treatment effect for...

In mjg211/OptGS: Optimal and Near-optimal Group-sequential Designs for Clinical Trials with Continuous Outcomes

Description

est() computes unadjusted and adjusted point estimates of the treatment effect based on results from a two-stage group-sequential clinical trial. It uses these to determine point estimator (conditional and marginal) bias and residual mean squared error.

Usage

est(
  des = OptGS::des_gs(),
  tau = seq(-des$delta, 2 * des$delta, length.out = 100),
  density = 1000,
  summary = FALSE
)

Arguments

des	An object of class OptGS_des, as returned by des_gs, des_nearopt, des_opt, or build. Defaults to des_gs(). Currently, this must be a two-stage trial.
tau	A numeric vector indicating the values of τ to perform calculations for. Defaults to seq(-des$delta, 2*des$delta, length.out = 100).
density	A numeric whole number indicating the number of standardised test statistics to consider when computing the bias and RMSE. Larger values increase accuracy of integral evaluations but also increase run time. Defaults to 1000.
summary	A logical variable indicating whether a summary of the function's progress should be printed to the console. Defaults to FALSE.

Value

A list with additional class "OptGS_est". It will contain each of the input variables (subject to internal modification), along with the following elements:

• estimates: A tibble giving details on point estimates.

• estimators: A tibble giving performance of the point estimators.

See Also

build, des_gs, des_nearopt, des_opt, sim.

Examples

# Calculations for the default parameters
perf <- est()

mjg211/OptGS documentation built on May 28, 2021, 3:44 p.m.