pi.pred: Based on the output obtained by applying ipw.pi.competing...
In xiaoli-mcw/PIcompete: Prevalence-Incidence mixture model for competing risks time-to-event outcomes
Description
Usage
Arguments
Value
Author(s)
References
Description
Based on the output obtained by applying ipw.pi.competing function to data,
pi.predict function calculates predicted prevalence by using the inverse function of logit function,
cumulative sub-distribution hazards and cumulative incidences for events 1 and 2.
Usage
1
pi.pred(input, p.mat, i.mat1, i.mat2, time.points)
Arguments
input
the output obtained from ipw.pi.competing
p.mat
design matrix for predicting prevalence by using the inverse of logit function;
both of vector and matrix types are allowed; the first component or column should include 1 for the intercept.
i.mat1
design matrix for predicting cumulative sub-distribution hazards and cumulative incidences for event 1
i.mat2
design matrix for predicting cumulative sub-distribution hazards and cumulative incidences for event 2
time.points
time points at which cumulative sub-distribution hazards and cumulative incidences for events 1 and 2 are predicted
Value
The output is a list of class pi.predict, which contains the following elements.
prev predicted prevalence for the sub-groups with the covariates defined in p.mat
subdist.hazard1 predicted cumulative sub-distribution hazards for event 1 for the sub-group with the covariates defined in i.mat1
subdist.hazard2 predicted cumulative sub-distribution hazards for event 2 for the sub-group with the covariates defined in i.mat2
cum.inc1 predicted cumulative incidences for event 1 for the sub-group with the covariates defined in i.mat1
cum.inc2 predicted cumulative incidences for event 2 for the sub-group with the covariates defined in i.mat2
Author(s)
Noorie Hyun, nhyun@mcw.edu, Xiao Li xiaoli@mcw.edu
References
Hyun N, Katki HA, Graubard BI.
Sample-Weighted Semiparametric Estimation of Cause-Specific Cumulative Risk and Incidence Using Left or Interval-Censored Data from Electronic Health Records. Statistics in Medicine 2020;
under the 2nd review.
xiaoli-mcw/PIcompete documentation built on May 20, 2020, 7:44 p.m.
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Description Usage Arguments Value Author(s) References
Description
Based on the output obtained by applying ipw.pi.competing function to data, pi.predict function calculates predicted prevalence by using the inverse function of logit function, cumulative sub-distribution hazards and cumulative incidences for events 1 and 2.
Usage
1 | pi.pred(input, p.mat, i.mat1, i.mat2, time.points)
|
Arguments
input |
the output obtained from ipw.pi.competing |
p.mat |
design matrix for predicting prevalence by using the inverse of logit function; both of vector and matrix types are allowed; the first component or column should include 1 for the intercept. |
i.mat1 |
design matrix for predicting cumulative sub-distribution hazards and cumulative incidences for event 1 |
i.mat2 |
design matrix for predicting cumulative sub-distribution hazards and cumulative incidences for event 2 |
time.points |
time points at which cumulative sub-distribution hazards and cumulative incidences for events 1 and 2 are predicted |
Value
The output is a list of class pi.predict, which contains the following elements.
prev predicted prevalence for the sub-groups with the covariates defined in p.mat
subdist.hazard1 predicted cumulative sub-distribution hazards for event 1 for the sub-group with the covariates defined in i.mat1
subdist.hazard2 predicted cumulative sub-distribution hazards for event 2 for the sub-group with the covariates defined in i.mat2
cum.inc1 predicted cumulative incidences for event 1 for the sub-group with the covariates defined in i.mat1
cum.inc2 predicted cumulative incidences for event 2 for the sub-group with the covariates defined in i.mat2
Author(s)
Noorie Hyun, nhyun@mcw.edu, Xiao Li xiaoli@mcw.edu
References
Hyun N, Katki HA, Graubard BI. Sample-Weighted Semiparametric Estimation of Cause-Specific Cumulative Risk and Incidence Using Left or Interval-Censored Data from Electronic Health Records. Statistics in Medicine 2020; under the 2nd review.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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