plot.coxph_mpl_dc: Plot a baseline hazard estimates from coxph_mpl_dc Object

In Kenny-Jing-Xu/survivalMPLdc: Penalised Likelihood for Survival Analysis with Dependent Censoring

Description

Plot the baseline hazard with the confidence interval estimates

Usage

## S3 method for class 'coxph_mpl_dc'
plot(
  x,
  parameter = "theta",
  funtype = "hazard",
  xout,
  se = TRUE,
  ltys,
  cols,
  ...
)

Arguments

x	an object inheriting from class coxph_mpl_dc
parameter	the set of parameters of interest. Indicate parameters="theta" for the baseline hazard estimated by theta and parameters="gamma" for the baseline hazard estimated by gamma
funtype	the type of function for ploting, i.e. funtype="hazard" for baseline hazard, funtype="cumhazard" for baseline cumulative hazard and funtype="survival" for baseline survival function
xout	the time values for the baseline hazard plot
se	se=TRUE gives both the MPL baseline estimates and 95% confidence interval plots while se=FALSE gives only the MPL baseline estimate plot.
ltys	a line type vector with two components, the first component is the line type of the baseline hazard while the second component is the line type of the 95% confidence interval
cols	a colour vector with two components, the first component is the colour of the baseline hazard while the second component is the colour the 95% confidence interval
...	other arguments

Details

When the input is of class coxph_mpl_dc and parameters=="theta", the baseline estimates base on θ and xout (with the corresponding 95% confidence interval if se=TRUE ) are ploted. When the input is of class coxph_mpl_dc and parameters=="gamma", the baseline hazard estimates based on γ and xout (with the corresponding 95% confidence interval if se=TRUE ) are ploted.

Value

the baseline hazard plot

Author(s)

Jing Xu, Jun Ma, Thomas Fung

References

Brodaty H, Connors M, Xu J, Woodward M, Ames D. (2014). "Predictors of institutionalization in dementia: a three year longitudinal study". Journal of Alzheimers Disease 40, 221-226.

Xu J, Ma J, Connors MH, Brodaty H. (2018). "Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood". Statistics in Medicine 37, 2238–2251.

See Also

coef.coxph_mpl_dc, coxph_mpl_dc.control, coxph_mpl_dc

Examples

 ##-- Copula types
 copula3 <- 'frank'

##-- A real example
##-- One dataset from Prospective Research in Memory Clinics (PRIME) study
##-- Refer to article Brodaty et al (2014),
##   the predictors of institutionalization of dementia patients over 3-year study period

data(PRIME)

surv<-as.matrix(PRIME[,1:3]) #time, event and dependent censoring indicators
cova<-as.matrix(PRIME[, -c(1:3)]) #covariates
colMeans(surv[,2:3])  #the proportions of event and dependent censoring

n<-dim(PRIME)[1];print(n)
p<-dim(PRIME)[2]-3;print(p)
names(PRIME)

##--MPL estimate Cox proportional hazard model for institutionalization under medium positive
##--dependent censoring
control <- coxph_mpl_dc.control(ordSp = 4,
                                binCount = 200, tie = 'Yes',
                                tau = 0.5, copula = copula3,
                                pent = 'penalty_mspl', smpart = 'REML',
                                penc = 'penalty_mspl', smparc = 'REML',
                                cat.smpar = 'No' )

coxMPLests_tau <- coxph_mpl_dc(surv=surv, cova=cova, control=control, )

plot(x = coxMPLests_tau, parameter = "theta", funtype="hazard",
     xout = seq(0, 36, 0.01), se = TRUE,
     cols=c("blue", "red"), ltys=c(1, 2), type="l", lwd=1, cex=1, cex.axis=1, cex.lab=1,
     xlab="Time (Month)", ylab="Hazard",
     xlim=c(0, 36), ylim=c(0, 0.05)
     )
     title("MPL Hazard", cex.main=1)
     legend( 'topleft',legend = c( expression(tau==0.5), "95% Confidence Interval"),
     col = c("blue", "red"),
     lty = c(1, 2),
     cex = 1)

plot(x = coxMPLests_tau, parameter = "theta", funtype="cumhazard",
    xout = seq(0, 36, 0.01), se = TRUE,
    cols=c("blue", "red"), ltys=c(1, 2),
    type="l", lwd=1, cex=1, cex.axis=1, cex.lab=1,
    xlab="Time (Month)", ylab="Hazard",
    xlim=c(0, 36), ylim=c(0, 1.2)
)
title("MPL Cumulative Hazard", cex.main=1)
legend( 'topleft',
       legend = c( expression(tau==0.5), "95% Confidence Interval"),
       col = c("blue", "red"),
       lty = c(1, 2),
       cex = 1
)

plot(x = coxMPLests_tau, parameter = "theta", funtype="survival",
    xout = seq(0, 36, 0.01), se = TRUE,
    cols=c("blue", "red"), ltys=c(1, 2),
    type="l", lwd=1, cex=1, cex.axis=1, cex.lab=1,
    xlab="Time (Month)", ylab="Hazard",
    xlim=c(0, 36), ylim=c(0, 1)
)
title("MPL Survival", cex.main=1)
legend( 'bottomleft',
       legend = c( expression(tau==0.5), "95% Confidence Interval"),
       col = c("blue", "red"),
       lty = c(1, 2),
       cex = 1
)

Kenny-Jing-Xu/survivalMPLdc documentation built on Oct. 28, 2020, 8:14 a.m.