| Title: | Proportional Odds Model with Censored, Time-Lagged Categorical Outcome |
|---|---|
| Description: | Implements a semiparametric estimator for the odds ratio model with censored, time-lagged, ordered categorical outcome in a randomized clinical trial that incorporates baseline and time-dependent information. Tsiatis, A. A. and Davidian, M. (2021) <arXiv:2106.15559>. |
| Authors: | Marie Davidian [aut], A. A. Tsiatis [aut], Shannon T. Holloway [aut, cre] |
| Maintainer: | Shannon T. Holloway <[email protected]> |
| License: | GPL-2 |
| Version: | 1.9 |
| Built: | 2025-01-07 03:13:26 UTC |
| Source: | https://github.com/cran/tLagPropOdds |
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)catProbs(data, ..., ti = NULL, td = NULL)
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. |
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.
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.
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"))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"))
Prints the key results.
Prints the key results.
## S3 method for class 'catProbsObj' print(x, ...) ## S3 method for class 'tLagObj' print(x, ...)## S3 method for class 'catProbsObj' print(x, ...) ## S3 method for class 'tLagObj' print(x, ...)
x |
A tLagObj object. The value returned by tLagPropOdds(). |
... |
Ignored. |
data(tLagData) # full AIPWCC estimator res <- catProbs(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) print(x = res) data(tLagData) # full AIPWCC estimator res <- tLagPropOdds(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) print(x = res)data(tLagData) # full AIPWCC estimator res <- catProbs(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) print(x = res) data(tLagData) # full AIPWCC estimator res <- tLagPropOdds(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) print(x = res)
These data are provided for the purposes of illustrating the use of the software. Though the data were generated under a scenario similar to a real-world COVID-19 therapeutics clinical trial, they should not be interpreted as representing true clinical trial data.
data(tLagData)data(tLagData)
tLagData is a time-dependent data.frame containing the following information for 602 participants ascertained at day 90 of a fictitious randomized clinical trial.
A unique participant identifier.
The treatment received, where A={0,1}.
The ordered outcome category. There are 6 categories ascertained at day 90.
at home and off oxygen, number of days >= 77;
at home and off oxygen, number of days 49-76;
at home and off oxygen, number of days 1-48;
not hospitalized and either at home on oxygen or not home;
hospitalized for medical care or in hospice care; and
dead.
If participant is censored, Cat = NA.
The time at which the outcome category was determined or the censoring time. For Cat = 1-5, U is the interim analysis time (90 days). For Cat = 6, U is the time of death. For Cat = NA, U is the censoring time.
The event indicator (1 if U is the time at which the outcome category was determined; 0 if censored).
A continuous baseline covariate.
The lower bound of the time interval to which the given covariate values pertain.
The upper bound of the time interval to which the given covariate values pertain.
A time-dependent indicator of hospital status, where 1 indicates that the participant was not in the hospital during interval (start, stop]; 0 otherwise.
The expected number of continuous days out of hospital at the time of the interim analysis (90 days).
Inverse probability weighted complete case (IPWCC) and augmented inverse probability weighted complete case (AIPWCC) estimators for the odds ratio in a proportional odds model with time-lagged ordered categorical outcome in a randomized clinical trial.
tLagPropOdds(data, ..., ti = NULL, td = NULL, itmax = 500, tol = 1e-05)tLagPropOdds(data, ..., ti = NULL, td = NULL, itmax = 500, tol = 1e-05)
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. |
itmax |
An integer object. The maximum number of iterations for the Newton-Raphson algorithm used to estimate parameters alpha and beta. |
tol |
A numeric object. The value at which the Newton-Raphson is deemed to have converged. |
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. (not discussed in the simulations of the original manuscript.)
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.
An S3 object of class tLagObj containing a list. The elements of the list correspond to the selected AIPWCC and/or IPWCC estimators. For each estimator, two matrix objects are returned: $logOdds contains the estimated beta parameters, their standard errors estimated using the sandwich estimator, the 95% confidence intervals, and the p-values for the log odds ratio; $odds contains the estimated odds ratio, their standard errors estimated using the delta method, and the 95% confidence intervals. The S3 object has an additional attribute, "type" giving a verbose description of the components contained in the estimator.
data(tLagData) # full AIPWCC estimator tLagPropOdds(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) # partial, time-dependent AIPWCC estimator tLagPropOdds(data = tLagData, td = c("hospStatus", "daysOut")) # partial, time-independent AIPWCC estimator tLagPropOdds(data = tLagData, ti = "x")data(tLagData) # full AIPWCC estimator tLagPropOdds(data = tLagData, ti = "x", td = c("hospStatus", "daysOut")) # partial, time-dependent AIPWCC estimator tLagPropOdds(data = tLagData, td = c("hospStatus", "daysOut")) # partial, time-independent AIPWCC estimator tLagPropOdds(data = tLagData, ti = "x")