gestboot: Percentile Based Bootstrap Confidence Intervals

In gesttools: General Purpose G-Estimation for End of Study or Time-Varying Outcomes

Description

Generates percentile based confidence intervals for the causal parameters of a fitted SNMM. Bonferroni corrected confidence intervals are also reported for multiple comparisons.

Usage

gestboot(
  gestfunc,
  data,
  idvar,
  timevar,
  Yn,
  An,
  Cn,
  outcomemodels,
  propensitymodel,
  censoringmodel = NULL,
  type,
  EfmVar = NA,
  cutoff,
  bn,
  alpha = 0.05,
  onesided = "twosided",
  seed = NULL,
  ...
)

Arguments

gestfunc	Name (without quotations) of the g-estimation function to run. One of gestSingle or gestMultiple.
data, idvar, timevar, Yn, An, Cn, outcomemodels, propensitymodel, censoringmodel, type, EfmVar, cutoff	Same arguments as in gest functions, to be input into gestfunc.
bn	Number of bootstrapped datasets.
alpha	Confidence level of confidence intervals.
onesided	Controls the type of confidence interval generated. Takes one of three inputs, "upper" for upper one-sided confidence intervals, "lower" for lower one-sided confidence intervals, and "twosided" for two-sided confidence intervals. Defaults to "twosided".
seed	Integer specifying the random seed for generation of bootstrap samples.
...	additional arguments.

Value

Returns a list of the following four elements.

original	The value of the causal parameters estimated on the original data data.
mean.boot	The average values of the causal parameters estimated on the bootstrapped datasets.
conf	The upper and/or lower bounds of 1-α confidence intervals for each element of ψ. For example, if type=2, and ψ=(ψ_0,ψ_1), a separate confidence interval is fitted for ψ_0 and ψ_1.
conf.Bonferroni	The upper and/or lower bounds of Bonferroni corrected confidence intervals for ψ, used for multiple comparisons.
boot.results	A tibble containing the result for each bootstrapped dataset

Examples

datas <- dataexamples(n = 1000, seed = 123, Censoring = FALSE)
data <- datas$datagest
data <- FormatData(
  data = data, idvar = "id", timevar = "time", An = "A",
  varying = c("A", "L"), GenerateHistory = TRUE, GenerateHistoryMax = 1
)
idvar <- "id"
timevar <- "time"
Yn <- "Y"
An <- "A"
Cn <- NA
outcomemodels <- list("Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A")
propensitymodel <- c("A~L+U+as.factor(time)+Lag1A")
censoringmodel <- NULL
type <- 1
EfmVar <- NA
bn <- 5
alpha <- 0.05
gestfunc <- gestSingle
gestboot(gestfunc, data, idvar, timevar, Yn, An, Cn, outcomemodels, propensitymodel,
  censoringmodel = NULL, type = 1, EfmVar,
  bn = bn, alpha = alpha, onesided = "twosided", seed = 123
)

gesttools documentation built on Jan. 4, 2022, 5:10 p.m.