mstate: redrank – R documentation

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redrank

Reduced rank proportional hazards model for competing risks and multi-state models

Description

This function estimates regression coefficients in reduced rank proportional hazards models for competing risks and multi-state models.

Usage

redrank(
  redrank,
  full = ~1,
  data,
  R,
  strata = NULL,
  Gamma.start,
  method = "breslow",
  eps = 1e-05,
  print.level = 1
)

Arguments

`redrank`	Survival formula, starting with either Surv(time,status) ~ or with Surv(Tstart,Tstop,status) ~, followed by a formula containing covariates for which a reduced rank estimate is to be found
`full`	Optional, formula specifying that part which needs to be retained in the model (so not subject to reduced rank)
`data`	Object of class 'msdata', as prepared for instance by `msprep`, in which to interpret the `redrank` and, optionally, the `full` formulas
`R`	Numeric, indicating the rank of the solution
`strata`	Name of covariate to be used inside the `strata` part of `coxph`
`Gamma.start`	A matrix of dimension K x R, with K the number of transitions and R the rank, to be used as starting value
`method`	The method for handling ties in `coxph`
`eps`	Numeric value determining when the iterative algorithm is finished (when for two subsequent iterations the difference in log-likelihood is smaller than `eps`)
`print.level`	Determines how much output is written to the screen; 0: no output, 1: iterations, for each iteration solutions of Alpha, Gamma, log-likelihood, 2: also the Cox regression results

Details

For details refer to Fiocco, Putter & van Houwelingen (2005, 2008).

Value

A list with elements

`Alpha`	the Alpha matrix
`Gamma`	the Gamma matrix
`Beta`	the Beta matrix corresponding to `covariates`
`Beta2`	the Beta matrix corresponding to `fullcovs`
`cox.itr1`	the `coxph` object resulting from the last call giving `Alpha`
`alphaX`	the matrix of prognostic scores given by `Alpha`, n x R, with n number of subjects
`niter`	the number of iterations needed to reach convergence
`df`	the number of regression parameters estimated
`loglik`	the log-likelihood

Author(s)

Marta Fiocco and Hein Putter H.Putter@lumc.nl

References

Fiocco M, Putter H, van Houwelingen JC (2005). Reduced rank proportional hazards model for competing risks. Biostatistics 6, 465–478.

Fiocco M, Putter H, van Houwelingen HC (2008). Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Statistics in Medicine 27, 4340–4358.

Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389–2430.

Examples

## Not run: 
    # This reproduces the results in Fiocco, Putter & van Houwelingen (2005)
    # Takes a while to run
    data(ebmt2)
    # transition matrix for competing risks
    tmat <- trans.comprisk(6,names=c("Relapse","GvHD","Bacterial","Viral","Fungal","Other"))
    # preparing long dataset
    ebmt2$stat1 <- as.numeric(ebmt2$status==1)
    ebmt2$stat2 <- as.numeric(ebmt2$status==2)
    ebmt2$stat3 <- as.numeric(ebmt2$status==3)
    ebmt2$stat4 <- as.numeric(ebmt2$status==4)
    ebmt2$stat5 <- as.numeric(ebmt2$status==5)
    ebmt2$stat6 <- as.numeric(ebmt2$status==6)
    covs <- c("dissub","match","tcd","year","age")
    ebmtlong <- msprep(time=c(NA,rep("time",6)),
                       stat=c(NA,paste("stat",1:6,sep="")),
                       data=ebmt2,keep=covs,trans=tmat)

    # The reduced rank 2 solution
    rr2 <- redrank(Surv(Tstart,Tstop,status) ~ dissub+match+tcd+year+age,
                   data=ebmtlong, R=2)
    rr3$Alpha; rr3$Gamma; rr3$Beta; rr3$loglik
    # The reduced rank 3 solution
    rr3 <- redrank(Surv(Tstart,Tstop,status) ~ dissub+match+tcd+year+age,
                   data=ebmtlong, R=3)
    rr3$Alpha; rr3$Gamma; rr3$Beta; rr3$loglik
    # The reduced rank 3 solution, with no reduction on age
    rr3 <- redrank(Surv(Tstart,Tstop,status) ~ dissub+match+tcd+year, full=~age,
                   data=ebmtlong, R=3)
    rr3$Alpha; rr3$Gamma; rr3$Beta; rr3$loglik
    # The full rank solution
    fullrank <- redrank(Surv(Tstart,Tstop,status) ~ dissub+match+tcd+year+age,
                        data=ebmtlong, R=6)
    fullrank$Beta; fullrank$loglik

## End(Not run)

mstate

Data Preparation, Estimation and Prediction in Multi-State Models

v0.3.1

GPL (>= 2)

Authors

Hein Putter [aut, cre], Liesbeth C. de Wreede [aut], Marta Fiocco [aut], Ronald B. Geskus [ctb], Edouard F. Bonneville [ctb], Damjan Manevski [ctb]

Initial release

2020-12-17