Estimates the cumulative risk of a right-censored outcome using an augmented inverse probability weighted estimator.

estimate_aipwrisk is a function that estimates the cumulative incidence of a right-censored outcome using an augmented inverse probability weighted estimator.

estimate_aipwrisk(
  data,
  models,
  wt = NULL,
  times = NULL,
  tau = NULL,
  labels = NULL,
  subgroup = NULL,
  subgroup_ps = TRUE,
  wt_type = "i",
  trim = NULL,
  trunc = NULL,
  nboot = 0,
  seed = 100,
  continuous = TRUE,
  tau_censor = FALSE,
  fully_efficient = FALSE,
  verbose = FALSE
)

Arguments

data

The input data frame

models

A model_specification object describing the incoming data, including the names of the variables containing the outcome (as a time-to-event), treatment, censoring, and associated models. The outcome and censoring times should be NA if they are censored). If no identify_missing statement was included in the model_specification, the estimator will default to a complete-case (listwise deletion) analysis.

wt

An optional vector of pre-computed weights to be applied to the analysis

times

An optional vector of times at which the risk should be computed. Defaults to all observed event times.

tau

The maximum length of follow-up to consider, estimation will not be performed beyond this point.

labels

vector of strings that are used to describe analysis, used for faceting

subgroup

An expression indicating which observations to include in the analysis

subgroup_ps

A logical indicating whether the PS should be re-estimated within the subgroup (default = TRUE)

wt_type

The type of confounding weight used, "i"=IPTW (default), "n"=IPTW normalized to sum to \(n\) in each treatment group, or a numeric indicating which group to use as a standard in an SMR weight.

trim

A scalar value (between 0 and 0.5) providing the thresholds for symmetrical propensity score trimming. For example, trim = 0.01 will drop observations with propensity scores below to the 1st percentile and above the 99th percentile (default = NULL, no trimming).

trunc

A scalar value (between 0 and 1) providing the top percentile at which to truncate confounding weights. For example, trunc = 0.99 will truncate all weights above the 99th percentile, i.e., weights larger than the 99th percentile will be set equal to the 99th percentile (default = NULL, no truncation).

nboot

The number of bootstrap resamples to generate. Used only if post-estimation bootstrap is used.

seed

A seed for the random number generator to ensure reproducibility of bootstrap replications

continuous

Requests continuous bootstrap weights from a Dirichlet versus discrete weights from a multinomial (default = TRUE)

tau_censor

For backward compatabiity before 0.36.17. Treat censoring at end of follow-up (tau) as a censoring event to be included in censoring models? Defaults to FALSE.

fully_efficient

use the fully efficient AIPW estimator. WARNING: this process is very computational and memory intensive

verbose

show progress

Value

An object that inherits from classes cumrisk and aipw that contains information about the estimated risk function.

Author

Alexander Breskin