`estimate_ipwrisk.Rd`

`estimate_ipwrisk`

is a function that estimates the cumulative incidence of a
right-censored outcome using inverse-probablity weighted estimation. Weights
can be constructed for treatment and censoring that address confounding and
dependent censoring.

```
estimate_ipwrisk(
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
)
```

- 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.

An object that inherits from classes `cumrisk`

and `ipw`

that contains information about the estimated risk
function and IP weights.