Inverse probability weighted complete case (IPWCC) and augmented inverse probability weighted complete case (AIPWCC) estimators for the probability of falling into a specific time-lagged ordered categorical outcome in a randomized clinical trial.
catProbs(data, ..., ti = NULL, td = NULL)An S3 object of class catProbsObj containing a list. The elements of the list correspond to the selected AIPWCC and/or IPWCC estimators. For each estimator, a list of matrix objects is returned, one for each treatment, that contains the estimated probabilities, their asymptotic standard errors, and the 95% confidence intervals. The S3 object has an additional attributes, "type", giving a verbose description of the components contained in the estimator.
A data.frame object. A data.frame containing all observed data. At a minimum, this data.frame must contain columns with headers "id", "U", "delta", "Cat" and "A". If the time-independent component of the estimator is to be included, data.frame must also contain the bases of f(X). If the time-dependent component is included, data.frame must also contain the bases of h(X,L) as well as the time intervals with column headers {"tstart", "tstop"} or {"start","stop"}. See Details for additional information.
Ignored. Included to require named inputs.
A character or integer vector or NULL. The columns of data to be included in the time-independent component of the estimator, f_m(X) m = 0, ..., M. If NULL, the time-independent component is excluded from the AIPWCC estimator. See Details for additional information.
A character or integer vector or NULL. The columns of data to be included in the time-dependent component of the estimator, h_l(X,Lbar), l = 1, ..., L. If NULL, the time-dependent component is excluded from the AIPWCC estimator. See Details for additional information.
At a minimum, the data provided for the analysis must contain the following information:
A unique participant identifier.
The time to ascertainment of category or censoring.
The indicator of ascertainment of category (1 if U is the time to ascertainment; 0 otherwise).
The ordered outcome category. Data must be provided as a factor or an integer or be able to be converted to an integer without loss of information. If participant was censored (delta = 0), Cat can take any integer-like value or NA.
The treatment received. Data must be provided as a factor or an integer or be able to be converted to an integer without loss of information.
With the exception of Cat, data must be complete.
If the time-independent component is to be included in the AIPWCC estimator, data must also include the time-independent basis functions f_m(X) m = 0, ..., M. If the intercept (f_0) term is not provided, it will be added by the software.
If the time-dependent component is to be included in the AIPWCC estimator, the data.frame must be a time-dependent dataset as described by package survival. Specifically, the time-dependent data must be specified for intervals (start,stop], and the data must include the following additional columns:
The lower boundary of the time interval to which the data pertain.
The upper boundary of the time interval to which the data pertain.
Note that column headers {"start", "stop"} are also accepted.
The various combinations of inputs ti and td yield the following:
the IPWCC estimate is returned. (denoted as IPW in the simulations of the original manuscript.)
the IPWCC and the full AIPWCC estimates are returned. (denoted as AIPW2 in the simulations of the original manuscript.)
the IPWCC and the partial, time-independent AIPWCC estimates are returned. (denoted as AIPW1 in the simulations of the original manuscript.)
the IPWCC and the partial, time-dependent AIPWCC estimates are returned.
If a treatment subgroup has <5% censoring, a message is generated and the treatment subgroup is removed from the time-dependent component of the AIPWCC estimator. If there is no censoring, the IPWCC estimator approaches the usual proportional odds estimator.
data(tLagData)
# full AIPWCC estimator
catProbs(data = tLagData, ti = "x", td = c("hospStatus", "daysOut"))
# partial, time-independent AIPWCC estimator
catProbs(data = tLagData, ti = "x")
# partial, time-dependent AIPWCC estimator
catProbs(data = tLagData, td = c("hospStatus", "daysOut"))
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