`estimate_ipwcount.Rd`

`estimate_ipwcount`

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

```
estimate_ipwcount(
data,
models,
wt = NULL,
times,
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 counts, treatment, censoring, and associated models. The 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 vector of times at which the counts should be computed.

- 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 `ipw`

that contains information about the estimated cumulative count
function and IP weights.