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Fit a proportional means regression model for weighted composite endpointof recurrent event and death

Description

Fit a semiparametric proportional means regression model for the weightedcomposite endpoint of recurrent event and death (Mao and Lin, 2016).(Jared D. Huling (ORCID: 0000-0003-0670-4845) contributed to the optimization of this code.)

Usage

CompoML(id, time, status, Z, w = NULL, ep = 1e-04)

Arguments

Value

An object of classCompoML with the following components.beta: a vector of estimated regression coefficients (log-mean ratios);var: estimated covariance matrix forbeta;t: unique event times;y: estimated baseline mean function (oft).

References

Mao, L. and Lin, D. Y. (2016). Semiparametric regression for the weightedcomposite endpoint of recurrent and terminal events. Biostatistics, 17, 390-403.

See Also

plot.CompoML,print.CompoML

Examples

## load package and datalibrary(Wcompo)head(hfmock)## fit a weighted PM (w_D=2, w_1=1)obj <- CompoML(hfmock$id,hfmock$time,hfmock$status,hfmock[,c("Training","HF.etiology")],               w=c(2,1))## print out the resultobjoldpar <- par(mfrow = par("mfrow"))par(mfrow=c(1,2))## plot the estimated mean function for## non-ischemic patients by treatmentplot(obj,c(1,0),ylim=c(0,1.5),xlim=c(0,50),     main="Non-ischemic",     xlab="Time (months)",cex.main=1.2,lwd=2)plot(obj,c(0,0),add=TRUE,cex.main=1.2,lwd=2,lty=2)legend("topleft",lty=1:2,lwd=2,c("Exercise training","Usual care"))## plot the estimated mean function for## ischemic patients by treatmentplot(obj,c(1,1),ylim=c(0,1.5),xlim=c(0,50),     main="Ischemic",     xlab="Time (months)",cex.main=1.2,lwd=2)plot(obj,c(0,1),add=TRUE,cex.main=1.2,lwd=2,lty=2)legend("topleft",lty=1:2,lwd=2,c("Exercise training","Usual care"))par(oldpar)

A dataset from the HF-ACTION trial

Description

The Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training(HF-ACTION) study was conducted between 2003–2007 to investigate whetheradding exercise training to the usual care of heart failure patients improves their cardiovascular outcomes (O'Conner et al., 2009).This is a mock dataset consisting of 963 patients with baseline information aboutheart failure etiology.

Usage

hfmock

Format

A data frame with 1,315 rows and 5 variables:

id

Unique patient identifier.

time

Event time (months).

status

Event type; 2 = recurrent hospitalization, 1 = death, 0 = censoring.

Training

1 = exercise training, 0 = usual care.

HF.etiology

1 = ischemic, 0 = non-ischemic.

References

O'CONNOR, C. M., WHELLAN, D. J., LEE, K. L., KETEYIAN, S. J.,COOPER, L. S., ELLIS, S. J., LEIFER, E. S.,KRAUS, W. E., KITZMAN, D. W., BLUMENTHAL, J. A. et al. (2009).Efficacy and safety of exercise training inpatients with chronic heart failure: Hf-action randomized controlled trial.J. Am. Med. Assoc. 301, 1439–1450.

Plot the predicted mean function under the proportional means model

Description

Plot the predicted mean function under the proportional means model fora new observation.

Usage

## S3 method for class 'CompoML'plot(  x,  z = NULL,  xlab = "Time",  ylab = "Mean function",  lty = 1,  frame.plot = FALSE,  add = FALSE,  ...)

Arguments

Value

No return value, called for side effects.

See Also

CompoML,print.CompoML.

Examples

## see example for CompoML

Print the analysis results of the proportional means model

Description

Print the analysis results of the proportional means model.

Usage

## S3 method for class 'CompoML'print(x, ...)

Arguments

Value

Print the results ofCompoML object

See Also

CompoML,plot.CompoML.