idem (version 5.2)

summary.IDEMINFER: Summary of the inference results

Description

Summarize survivors only or Survivor Averaged Causal Effect (SACE) based on the imputation and bootstrap analysis

Usage

# S3 method for IDEMINFER
summary(object, opt = c("survivor", "SACE"), sace.deltas = NULL, ...)

Value

A class summary.IDEMINFER list containing

deltas

imputation sensitivity parameters

n.boot

number of bootstrap samples in bootstrap analysis

sace.deltas

SACE sensitivity parameters when opt = SACE

rst

A data frame with columns

• Delta0: Imputation sensitivity parameter for control arm,

• Delta1: Imputation sensitivity parameter for intervention arm

• SACE_Delta: SACE sensitivity parameter when opt = SACE

• Effect: SACE estimate

• LB: Lower bound of the 95

• UB: Upper bound of the 95

• PValue: p-value when when n.boot > 0 in the IDEMINFER object

Arguments

object

A class IDEMINFER list generated by imInfer

opt

Types of the summary

• survivor: Survivors only analysis

• SACE: Survivor Averaged Causal Effect

sace.deltas

Vector of sensitivity parameters for SACE estimation. If NULL, the values will be generated based on the standard deviations of the estimated differences in the functional outcomes between the treatment and control arms

...

Optional arguments for summary

Details

For SACE, the default sensitivity parameters will be determined by the standard deviation of the treatment effect size on the functional outcomes.

References

Chiba Y, VanderWeele TJ (2011). A simple method for principal strata effects when the outcome has been truncated due to death. American Journal of Epidemiology 173(7):745-751.

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.infer <- imInfer(rst.imp, n.boot = 100, effect.quantiles = c(0.25,0.5,0.75));
rst.sace  <- summary(rst.infer, opt = "SACE")}