PMDA.crreg: Multiple Imputation using Poor Man's Data Augmentation scheme (PMDA) for Competing risks regression models

Description

for internal uses

PMDA.crreg(formula, data, imax = 25, k = 10, th0 = 1e-3 ,
status, trans, cens, keep, model = "FG")

formula

a formula

data

data used in this function

imax

maximum number of iteration

number of dataset to generate for each iteration

th0

note used currently

status

name of the column were the status of observation is indicated

trans

what is the transition of interest in the status column

cens

code used for censored data in the status column

keep

must be the same as the covariate used in formula

model

which model shall be used currently FG state for Fine and Gray proportional hazards regression model

betaestimation of the coefficient(s)
sr_sigmaestimation of the standard errors
n_iternumber of iteration used
sigma1mean of standard errors not augmented for the between imputations composant of the variance

This function is called by MIICD.crreg with option PMDA (Poor Man's Data Augmentation)

PAN, Wei. A Multiple Imputation Approach to Cox Regression with Interval-Censored Data. Biometrics, 2000, vol. 56, no 1, p. 199-203.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys.

Schenker, N. and Welsh, A. (1988). Asymptotic results for multiple imputation. The Annals of Statistics pages 1550-1566.

Tanner, M. A. and Wong, W. H. (1987). An application of imputation to an estimation problem in grouped lifetime analysis. Technometrics 29, 23-32.

Wei, G. C., & Tanner, M. A. (1991). Applications of multiple imputation to the analysis of censored regression data. Biometrics, 47(4), 1297-1309.