Extract regression coefficients of a coxph_mpl_dc Object

Extract the matrix of regression coefficients with their corresponding standard errors, \(z\)-statistics and \(p\)-values of the model part of interest of a coxph_mpl_dc object

# S3 method for coxph_mpl_dc
coef(object, parameter, ...)

Arguments

object	an object inheriting from class coxph_mpl_dc
parameter	the set of parameters of interest. Indicate parameters="beta" for the regression parameter of beta and parameters="phi" for the regression parameter of phi
...	other arguments

Value

est

a matrix of coefficients with standard errors, z-statistics and corresponding p-values

Details

When the input is of class coxph_mpl_dc and parameters=="beta", the matrix of beta estimates with corresponding standar errors, \(z\)-statistics and \(p\)-values are reported. When the input is of class coxph_mpl_dc and parameters=="phi", the matrix of phi estimates with corresponding standar errors, \(z\)-statistics and \(p\)-values are reported.

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

plot.coxph_mpl_dc, coxph_mpl_dc.control, coxph_mpl_dc

Author

Jing Xu, Jun Ma, Thomas Fung

Examples

# \donttest{

 ##-- 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
#>     Event    Depcen 
#> 0.2675815 0.2504288 

n<-dim(PRIME)[1];print(n)
#> [1] 583
p<-dim(PRIME)[2]-3;print(p)
#> [1] 15
names(PRIME)
#>  [1] "Time"           "Event"          "Depcen"         "Age"           
#>  [5] "Gender"         "HighEdu"        "Alzheimer"      "CDR_base"      
#>  [9] "MMSE_base"      "SMAF_base"      "ZBI_base"       "NPI_base"      
#> [13] "Benzon"         "Antiphsy"       "LivingAlone"    "MMSE_change_3m"
#> [17] "SMAF_change_3m" "NPI_change_3m" 

##--MPL estimate Cox proportional hazard model for institutionalization under independent 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, )
MPL_beta<-coef(object = coxMPLests_tau, parameter = "beta",)
MPL_phi<-coef(object = coxMPLests_tau, parameter = "phi",)

# }