R: Statistical Methods for Survival Data with Dependent Censoring

# Statistical Methods for Survival Data with Dependent Censoring

## Documentation for package ‘depCensoring’ version 0.1.7

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D E F G I K L M N O P S T U V Y

## -- A --

A_step | A-step in the EAM algorithm described in KMS19

## -- B --

boot.fun | Nonparametric bootstrap approach for the dependent censoring model
boot.funI | Nonparametric bootstrap approach for the independent censoring model
boot.nonparTrans | Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring
Bspline.unit.interval | Evaluate the specified B-spline, defined on the unit interval
Bvprob | Compute bivariate survival probability

## -- C --

cbMV | Combine bounds based on majority vote.
check.args.pisurv | Check argument consistency.
chol2par | Transform Cholesky decomposition to covariance matrix
chol2par.elem | Transform Cholesky decomposition to covariance matrix parameter element.
Chronometer | Chronometer object
clear.plt.wdw | Clear plotting window
CompC | Compute phi function
control.arguments | Prepare initial values within the control arguments
copdist.Archimedean | The distribution function of the Archimedean copula
cophfunc | The h-function of the copula
coppar.to.ktau | Convert the copula parameter the Kendall's tau
cr.lik | Competing risk likelihood function.

## -- D --

D.hat | Obtain the diagonal matrix of sample variances of moment functions
dat.sim.reg.comp.risks | Data generation function for competing risks data
dchol2par | Derivative of transform Cholesky decomposition to covariance matrix.
dchol2par.elem | Derivative of transform Cholesky decomposition to covariance matrix element.
dD.hat | Obtain the matrix of partial derivatives of the sample variances.
Distance | Distance between vectors
dLambda_AFT_ll | Derivative of link function (AFT model)
dLambda_Cox_wb | Derivative of link function (Cox model)
dm.bar | Vector of sample average of each moment function (\bar{m}_n(theta)).
do.optimization.Mstep | Optimize the expected improvement
draw.sv.init | Draw initial set of starting values for optimizing the expected improvement.
DYJtrans | Derivative of the Yeo-Johnson transformation function

## -- E --

EAM | Main function to run the EAM algorithm
EAM.converged | Check convergence of the EAM algorithm.
EI | Expected improvement
estimate.cf | Estimate the control function
estimate.cmprsk | Estimate the competing risks model of Rutten, Willems et al. (20XX).
E_step | E-step in the EAM algorithm as described in KMS19.

## -- F --

feasible_point_search | Method for finding initial points of the EAM algorithm
fitDepCens | Fit Dependent Censoring Models
fitIndepCens | Fit Independent Censoring Models

## -- G --

G.box | Family of box functions
G.cd | Family of continuous/discrete instrumental function
G.cd.mc | Family of discrete/continuous instrumental functions, in the case of many covariates.
G.hat | Compute the Gn matrix in step 3b of Bei (2024).
G.spline | Family of spline instrumental functions
generator.Archimedean | The generator function of the Archimedean copula
get.anchor.points | Get anchor points on which to base the instrumental functions
get.cond.moment.evals | Compute the conditional moment evaluations
get.cvLLn | Compute the critical value of the test statistic.
get.deriv.mom.func | Matrix of derivatives of conditional moment functions
get.dmi.tens | Faster implementation to obtain the tensor of the evaluations of the derivatives of the moment functions at each observation.
get.extra.Estep.points | Get extra evaluation points for E-step
get.instrumental.function.evals | Evaluate each instrumental function at each of the observations.
get.mi.mat | Faster implementation of vector of moment functions.
get.next.point | Obtain next point for feasible point search.
get.starting.values | Main function for obtaining the starting values of the expected improvement maximization step.
get.test.statistic | Obtain the test statistic by minimizing the S-function over the feasible region beta(r).
gridSearch | Grid search algorithm for finding the identified set
gs.algo.bidir | Rudimentary, bidirectional 1D grid search algorithm.
gs.binary | Return the next point to evaluate when doing binary search
gs.interpolation | Return the next point to evaluate when doing interpolation search
gs.regular | Return the next point to evaluate when doing regular grid search

## -- I --

insert.row | Insert row into a matrix at a given row index
IYJtrans | Inverse Yeo-Johnson transformation function

## -- K --

Kernel | Calculate the kernel function
ktau.to.coppar | Convert the Kendall's tau into the copula parameter

## -- L --

Lambda_AFT_ll | Link function (AFT model)
Lambda_Cox_wb | Link function (Cox model)
Lambda_inverse_AFT_ll | Inverse of link function (AFT model)
Lambda_inverse_Cox_wb | Inverse of link function (Cox model)
lf.delta.beta1 | Loss function to compute Delta(beta).
lf.ts | 'Loss function' of the test statistic.
LikCopInd | Loglikehood function under independent censoring
Likelihood.Parametric | Calculate the likelihood function for the fully parametric joint distribution
Likelihood.Profile.Kernel | Calculate the profiled likelihood function with kernel smoothing
Likelihood.Profile.Solve | Solve the profiled likelihood function
Likelihood.Semiparametric | Calculate the semiparametric version of profiled likelihood function
LikF.cmprsk | Second step log-likelihood function.
likF.cmprsk.Cholesky | Wrapper implementing likelihood function using Cholesky factorization.
LikGamma1 | First step log-likelihood function for Z continuous
LikGamma2 | First step log-likelihood function for Z binary.
LikI.bis | Second likelihood function needed to fit the independence model in the second step of the estimation procedure.
LikI.cmprsk | Second step log-likelihood function under independence assumption.
LikI.cmprsk.Cholesky | Wrapper implementing likelihood function assuming independence between competing risks and censoring using Cholesky factorization.
likIFG.cmprsk.Cholesky | Full likelihood (including estimation of control function).
loglike.clayton.unconstrained | Log-likelihood function for the Clayton copula.
loglike.frank.unconstrained | Log-likelihood function for the Frank copula.
loglike.gaussian.unconstrained | Log-likelihood function for the Gaussian copula.
loglike.gumbel.unconstrained | Log-likelihood function for the Gumbel copula.
loglike.indep.unconstrained | Log-likelihood function for the independence copula.
log_transform | Logarithmic transformation function.
Longfun | Long format
LongNPT | Change H to long format

## -- M --

m.bar | Vector of sample average of each moment function (\bar{m}_n(theta)).
MSpoint | Analogue to KMS_AUX4_MSpoints(...) in MATLAB code of Bei (2024).
M_step | M-step in the EAM algorithm described in KMS19.

## -- N --

NonParTrans | Fit a semiparametric transformation model for dependent censoring
normalize.covariates | Normalize the covariates of a data set to lie in the unit interval by scaling based on the ranges of the covariates.
normalize.covariates2 | Normalize the covariates of a data set to lie in the unit interval by transforming based on PCA.

## -- O --

Omega.hat | Obtain the correlation matrix of the moment functions
optimlikelihood | Fit the dependent censoring models.

## -- P --

parafam.d | Obtain the value of the density function
parafam.p | Obtain the value of the distribution function
parafam.trunc | Obtain the adjustment value of truncation
ParamCop | Estimation of a parametric dependent censoring model without covariates.
Parameters.Constraints | Generate constraints of parameters
pi.surv | Estimate the model of Willems et al. (2024+).
plot_addpte | Draw points to be evaluated
plot_addpte.eval | Draw evaluated points.
plot_base | Draw base plot
power_transform | Power transformation function.
PseudoL | Likelihood function under dependent censoring

## -- S --

S.func | S-function
ScoreEqn | Score equations of finite parameters
SearchIndicate | Search function
set.EAM.hyperparameters | Set default hyperparameters for EAM algorithm
set.GS.hyperparameters | Set default hyperparameters for grid search algorithm
set.hyperparameters | Define the hyperparameters used for finding the identified interval
Sigma.hat | Compute the variance-covariance matrix of the moment functions.
SolveH | Estimate a nonparametric transformation function
SolveHt1 | Estimating equation for Ht1
SolveL | Cumulative hazard function of survival time under dependent censoring
SolveLI | Cumulative hazard function of survival time under independent censoring
SolveScore | Estimate finite parameters based on score equations
summary.depFit | Summary of 'depCensoringFit' object
summary.indepFit | Summary of 'indepCensoringFit' object
SurvDC | Semiparametric Estimation of the Survival Function under Dependent Censoring
SurvDC.GoF | Calculate the goodness-of-fit test statistic
SurvFunc.CG | Estimated survival function based on copula-graphic estimator (Archimedean copula only)
SurvFunc.KM | Estimated survival function based on Kaplan-Meier estimator
SurvMLE | Maximum likelihood estimator for a given parametric distribution
SurvMLE.Likelihood | Likelihood for a given parametric distribution

## -- T --

TCsim | Function to simulate (Y,Delta) from the copula based model for (T,C).
test.point_Bei | Perform the test of Bei (2024) for a given point
test.point_Bei_MT | Perform the test of Bei (2024) simultaneously for multiple time points.

## -- U --

uniformize.data | Standardize data format

## -- V --

variance.cmprsk | Compute the variance of the estimates.

## -- Y --

YJtrans | Yeo-Johnson transformation function