cifreg: CIF regression In mets: Analysis of Multivariate Event Times

Description

CIF logistic for propodds=1 default CIF Fine-Gray (cloglog) regression for propodds=NULL

Usage

  1 2 3 4 5 6 7 8 9 10 11 12 cifreg( formula, data = data, cause = 1, cens.code = 0, cens.model = ~1, weights = NULL, offset = NULL, Gc = NULL, propodds = 1, ... ) 

Arguments

 formula formula with 'Event' outcome data data frame cause of interest cens.code code of censoring cens.model for stratified Cox model without covariates weights weights for FG score equations offset offsets for FG model Gc censoring weights for time argument, default is to calculate these with a Kaplan-Meier estimator, should then give G_c(T_i-) propodds 1 is logistic model, NULL is fine-gray model ... Additional arguments to lower level funtions

Details

For FG model:

\int (X - E ) Y_1(t) w(t) dM_1

is computed and summed over clusters and returned multiplied with inverse of second derivative as iid.naive. Where

w(t) = G(t) (I(T_i \wedge t < C_i)/G_c(T_i \wedge t))

and

E(t) = S_1(t)/S_0(t)

and

S_j(t) = ∑ X_i^j Y_{i1}(t) w_i(t) \exp(X_i^T β)

The iid decomposition of the beta's, however, also have a censoring term that is also is computed and added to UUiid (still scaled with inverse second derivative)

\int (X - E ) Y_1(t) w(t) dM_1 + \int q(s)/p(s) dM_c

and returned as iid

For logistic link standard errors are slightly to small since uncertainty from recursive baseline is not considered, so for smaller data-sets it is recommended to use the prop.odds.subdist of timereg that is also more efficient due to use of different weights for the estimating equations. Alternatively, one can also bootstrap the standard errors.

Thomas Scheike

Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 ## data with no ties data(bmt,package="timereg") bmt$time <- bmt$time+runif(nrow(bmt))*0.01 bmt\$id <- 1:nrow(bmt) ## logistic link OR interpretation ll=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1) summary(ll) plot(ll) nd <- data.frame(tcell=c(1,0),platelet=0,age=0) pll <- predict(ll,nd) plot(pll) ## Fine-Gray model fg=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL) summary(fg) plot(fg) nd <- data.frame(tcell=c(1,0),platelet=0,age=0) pfg <- predict(fg,nd) plot(pfg) sfg <- cifreg(Event(time,cause)~strata(tcell)+platelet+age,data=bmt,cause=1,propodds=NULL) summary(sfg) plot(sfg) ### predictions with CI based on iid decomposition of baseline and beta # fg <- cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL,cox.prep=TRUE) # Biid <- iid.baseline.cifreg(Biid,time=20) # FGprediid(Biid,bmt[1:5,]) 

mets documentation built on Oct. 23, 2020, 5:55 p.m.