baseHaz | R Documentation |

`mjoint`

objectThis function returns the (baseline) hazard increment from a
fitted `mjoint`

object. In addition, it can report either the
*uncentered* or the more ubiquitous *centered* version.

baseHaz(object, centered = TRUE, se = FALSE)

`object` |
an object inheriting from class |

`centered` |
logical: should the baseline hazard be for the mean-centered
covariates model or not? Default is |

`se` |
logical: should standard errors be approximated for the hazard
increments? Default is |

When covariates are included in the time-to-event sub-model,
`mjoint`

automatically centers them about their respective
means. This also applies to non-continuous covariates, which are first
coded using a dummy-transformation for the design matrix and subsequently
centered. The reason for the mean-centering is to improve numerical
stability, as the survival function involves exponential terms. Extracting
the baseline hazard increments from `mjoint.object`

returns the
Breslow hazard estimate (Lin, 2007) that corresponds to this mean-centered
model. This is the same as is done in the R `survival`

package when
using `coxph.detail`

(Therneau and Grambsch, 2000).
If the user wants to access the baseline hazard estimate for the model in
which no mean-centering is applied, then they can use this function, which
scales the mean-centered baseline hazard by

*\exp\{-\bar{w}^\top γ_v\},*

where *\bar{w}* is a vector of the means from the time-to-event
sub-model design matrix.

A `data.frame`

with two columns: the unique failure times and
the estimate baseline hazard. If `se=TRUE`

, then a third column is
appended with the corresponding standard errors (for the centred case).

Graeme L. Hickey (graemeleehickey@gmail.com)

Therneau TM, Grambsch PM. *Modeling Survival Data: Extending the Cox
Model.* New Jersey: Springer-Verlag; 2000.

Lin DY. On the Breslow estimator. *Lifetime Data Anal.* 2007;
**13(4)**: 471-480.

`mjoint`

and `coef`

.

## Not run: # Fit a joint model with bivariate longitudinal outcomes data(heart.valve) hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ] fit2 <- mjoint( formLongFixed = list("grad" = log.grad ~ time + sex + hs, "lvmi" = log.lvmi ~ time + sex), formLongRandom = list("grad" = ~ 1 | num, "lvmi" = ~ time | num), formSurv = Surv(fuyrs, status) ~ age, data = list(hvd, hvd), inits = list("gamma" = c(0.11, 1.51, 0.80)), timeVar = "time", verbose = TRUE) baseHaz(fit2, centered = FALSE) ## End(Not run)

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