survcorr: Correlation Analysis of Survival Times by Iterative Multiple...

In SurvCorr: Correlation of Bivariate Survival Times

Correlation Analysis of Survival Times by Iterative Multiple Imputation

Description

This R-package implements the iterative multiple imputation algorithm as proposed by Schemper, Kaider, Wakounig and Heinze (2013) for estimation of a correlation coefficient for bivariate possibly censored time-to-event data.

Usage

survcorr(formula1, formula2, 
    data, methods = "imi", alpha = 0.05, intra = FALSE, 
    M = 10, MCMCSteps = 10, epsilon = 0.001, maxiter = 100)

Arguments

formula1	Survival object for first time-to-event variable, e.g. Surv(time1, status1)~1
formula2	Survival object for second time-to-event variable, e.g. Surv(time2, status2)~1
data	Data set to look up variables
methods	Correlation method(s). Currently, only "imi" (iterative multiple imputation) is implemented.
alpha	One minus confidence level (for confidence interval computation)
intra	If TRUE, an intraclass correlation coefficient will be computed, assuming that the two time-to-event variables are interchangeable in each observation.
M	Number of imputations (for IMI)
MCMCSteps	Number of MCMCSteps (for IMI)
epsilon	Accuracy of numerical estimation of correlation coefficients
maxiter	Maximum number of iterations in IMI

Details

The analysis of correlations within pairs of survival times is of interest to many research topics in medicine, such as the correlation of survival-type endpoints of twins, the correlation of times till failure in paired organs, or the correlation of survival time with a surrogate endpoint. The dependence of such times is assumed monotonic and thus quantification by rank correlation coefficients appropriate. The typical censoring of such times requires more involved methods of estimation and inference as have been developed in recent years. As an alternative to the maximum likelihood methodology for the normal copula approach (NCE) this package implements an iterative multiple imputation (IMI) method which requires only about 0.05% of the computing time of NCE, without sacrificing statistical performance. For IMI, survival probabilities at death or censoring times are first transformed to normal deviates. Then, those deviates which relate to censored times are iteratively augmented, by conditional multiple imputation, until convergence is obtained for the normal scores rank correlation, which is similar to Spearman's rank correlation. Schemper, Kaider, Wakounig and Heinze (2013) compared statistical properties of NCE and IMI by means of a Monte Carlo study and by means of three real data sets; two of them are available in this package.

Value

rho	estimated correlation coefficient
ci.lower	lower limit of confidence interval for rho
ci.upper	upper limit of confidence interval for rho
simData	imputed data sets for each iteration, with components M (number of imputations), z1M, z2M (imputed normal deviates), delta1, delta2 (censoring indicators), t1, t2 (imputed non-censored survival times)
M	number of imputations
MCMCSteps	number of MCMC steps in iterative imputation
rj.trans	the M atanh-transformed correlation coefficients from M imputed data sets
rj.t.mean	the posterior mean of the atanh-transofmred correlation coefficients over the M imputations
var	the variance of atanh(rho), with components within, between and total
df	the number of degrees of freedom (important for confidence interval computation)
alpha	1-confidence level
call	the function call (useful for making use of update(obj))

Author(s)

Meinhard Ploner, Alexandra Kaider, Georg Heinze

References

Schemper,M., Kaider,A., Wakounig,S. & Heinze,G. (2013): "Estimating the correlation of bivariate failure times under censoring", Statistics in Medicine, 32, 4781-4790 doi: 10.1002/sim.5874.

Examples

## Example 1
data(kidney)
obj = survcorr(formula1=Surv(TIME1, STATUS1) ~ 1, formula2=Surv(TIME2, STATUS2) ~ 1, 
  data=kidney, M=1000, MCMCSteps=10, alpha=0.05, epsilon=0.001)

## Example 2
data(diabetes)
obj = survcorr(formula1=Surv(TIME1, STATUS1) ~ 1, formula2=Surv(TIME2, STATUS2) ~ 1, 
  data=diabetes, M=100, MCMCSteps=10, alpha=0.05, epsilon=0.001)
plot(obj, "times")

SurvCorr documentation built on Nov. 10, 2022, 5:43 p.m.