catProbs {tLagPropOdds}

Estimation of the Probability of a Specific Categorical Outcome by Treatment

Description

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.

Usage

catProbs(data, ..., ti = NULL, td = NULL)

Arguments

data

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.

ti

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.

td

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.

Details

At a minimum, the data provided for the analysis must contain the following information:

id:

A unique participant identifier.

U:

The time to ascertainment of category or censoring.

delta:

The indicator of ascertainment of category (1 if U is the time to ascertainment; 0 otherwise).

Cat:

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.

A:

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:

tstart:

The lower boundary of the time interval to which the data pertain.

tstop:

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:

ti = NULL, td = NULL

the IPWCC estimate is returned. (denoted as IPW in the simulations of the original manuscript.)

ti != NULL, td != NULL

the IPWCC and the full AIPWCC estimates are returned. (denoted as AIPW2 in the simulations of the original manuscript.)

ti = NULL, td != NULL

the IPWCC and the partial, time-independent AIPWCC estimates are returned. (denoted as AIPW1 in the simulations of the original manuscript.)

ti = NULL, td != NULL

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.

Value

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.

Examples

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"))