estimate_ipwcount.Rdestimate_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
)The input data frame
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.
An optional vector of pre-computed weights to be applied to the analysis
An vector of times at which the counts should be computed.
vector of strings that are used to describe analysis, used for faceting
An expression indicating which observations to include in the analysis
A logical indicating whether the PS should be re-estimated within the subgroup (default = TRUE)
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.
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).
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).
The number of bootstrap resamples to generate. Used only if post-estimation bootstrap is used.
A seed for the random number generator to ensure reproducibility of bootstrap replications
Requests continuous bootstrap weights from a Dirichlet versus discrete weights from a multinomial (default = TRUE)
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.