idem (version 5.2)

imInfer: Treatment effect estimation and hypothesis testing

Description

Estimate treatment effect size. Estimate variation and conduct hypothesis testing by bootstrap analysis.

Usage

imInfer(
  imp.rst,
  n.boot = 0,
  n.cores = 1,
  update.progress = NULL,
  effect.quantiles = c(0.25, 0.5, 0.75),
  quant.ci = c(0.025, 0.975),
  ...,
  seed = NULL
)

Value

A class IDEMTEST list containing

lst.var

List of specification parameters

deltas

Vector of sensitivity parameters

theta

A data frame with columns

• Delta0: Sensitivity parameter for control arm

• Delta1: Sensitivity parameter for intervention arm

• Theta: Estimated \(\theta\)

• SD: Standard deviation (when n.boot >0)

• PValue: p-value (when n.boot >0

effect.quantiles

A data frame with columns

• Delta:Sensitivity parameter

• TRT:Treatment arm

• Q: Quantiles of the composite endpoint to be estimated

• QuantY: Estimated quantiles if the quantiles correspond to functional outcome (when n.boot >0)

• QuantSurv: Estimated quantiles if the quantiles correspond to survival days (when n.boot >0)

• Q: Boostrap quantiles for the QuantY (when n.boot >0)

• QSurv: Boostrap quantiles for the QuantSurv (when n.boot >0)

bootstrap

A list with length n.boot. The \(i\)th item is the class IDEMEST list corresponding to the \(i\)th bootstrap sample

Arguments

imp.rst

A class IDEMIMP object returned from imImpAll

n.boot

Number of bootstrap samples

n.cores

Number of cores for parallel computation. Fixed at 1 for Windows.

update.progress

Parameter reserved for run idem in GUI mode

effect.quantiles

Composite quantiles of interest for measuring treatment effect

quant.ci

Quantiles for extracting bootstrap confidence intervals

...

Extra options for ranking subjects using the composite endpoint that include

• cut.z: Clinically meaningful difference in the functional outcome

• cut.surv: Clinically meaningful difference in survival time

seed

Random seed

Details

If n.boot=0, bootstrap analysis will not be conducted. Instead, only the treatment effect size will be estimated using the imputed data.

Examples

if (FALSE) {
rst.abc <- imData(abc, trt="TRT", surv="SURV", outcome=c("Y1","Y2"),
                 y0=NULL, endfml="Y2",
                 trt.label = c("UC+SBT", "SAT+SBT"),
                 cov=c("AGE"), duration=365, bounds=c(0,100));
rst.fit  <- imFitModel(rst.abc);
rst.imp <- imImpAll(rst.fit, deltas=c(-0.25,0,0.25),
                    normal=TRUE, chains = 2, iter = 2000, warmup = 1000);
rst.est <- imInfer(rst.imp, n.boot = 0, effect.quantiles = c(0.25,0.5,0.75));
rst.test <- imInfer(rst.imp, n.boot = 100, effect.quantiles = c(0.25,0.5,0.75));}