`estimate_ipwhr.Rd`

`estimate_ipwhr`

is a function that estimates the hazard ratio for a
treatment with a right-censored outcome using inverse-probablity weighted
estimation. Weights can be constructed to account for unequal treatment
probabilities.

```
estimate_ipwhr(
data,
models,
wt = NULL,
tau = NULL,
labels = NULL,
subgroup = NULL,
subgroup_ps = TRUE,
wt_type = "i",
trim = NULL,
trunc = NULL,
ref = NULL
)
```

- 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. The model specification is not allowed to include`identify_competing`

because`estimate_ipwhr`

does not currently support competing risks. The`identify_censoring`

statement is not allowed to include a formula with a term on the right-hand side other than`1`

(i.e. an intercept) because`estimate_ipwhr`

does not currently support inverse probability of censoring weights.- wt
An optional vector of pre-computed weights to be applied to the analysis

- tau
Either

`NULL`

or a positive numeric value indicating a point at which all data will be considered to be censored. Thus if`tau`

is non-`NULL`

and a subject has an observed event or censoring time that occurs after`tau`

then they will be considered to have been censored at`tau`

. When`tau`

is`NULL`

, then no artificial censoring is imposed.- 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).- ref
Either

`NULL`

or a string specifying the category to use as the reference for hazard ratios. When the value is`NULL`

then the reference category is taken to be the category with the earliest name according to alphabetical order.

An object that inherits from classes `hr`

and `ipw`

that
contains information about the estimated hazard ratio and IP weights