ahaz {ahaz} | R Documentation |

## Fit semiparametric additive hazards model

### Description

Fit a semiparametric additive hazards regression model. Right-censored and left-truncated survival data are supported.

### Usage

`ahaz(surv, X, weights, univariate=FALSE, robust=FALSE) `

### Arguments

`surv` |
Response in the form of a survival object, as returned by the
function |

`X` |
Design matrix. Missing values are not supported. |

`weights` |
Optional vector of observation weights. Default is 1 for each observation. |

`univariate` |
Fit all univariate models instead of the joint
model. Default is |

`robust` |
Robust calculation of variance. Default is |

### Details

The semiparametric additive hazards model specifies a hazard function of the form:

`h(t) = h_0(t) + \beta' Z_i`

for `i=1,\ldots,n`

where `Z_i`

is the vector of covariates,
`\beta`

the vector of regression coefficients and `h_0`

is an
unspecified baseline hazard. The semiparametric additive hazards model
can be viewed as an
additive analogue of the well-known Cox proportional hazards
regression model.

Estimation is based on the estimating equations of Lin & Ying (1994).

The option `univariate`

is intended for screening purposes in
data sets with a large number of covariates. It is substantially faster than the
standard approach of combining `ahaz`

with
`apply`

, see the examples.

### Value

An object with S3 class `"ahaz"`

.

`call` |
The call that produced this object. |

`nobs` |
Number of observations. |

`nvars` |
Number of covariates. |

`D` |
A |

`d` |
A vector of length |

`B` |
An |

`univariate` |
Is |

`data` |
Formatted version of original data (for internal use). |

`robust` |
Is |

### References

Lin, D.Y. & Ying, Z. (1994). *Semiparametric analysis of
the additive risk model.* Biometrika; **81**:61-71.

### See Also

`summary.ahaz`

, `predict.ahaz`

,
`plot.ahaz`

.
The functions `coef`

,
`vcov`

, `residuals`

.

### Examples

```
data(sorlie)
# Break ties
set.seed(10101)
time <- sorlie$time+runif(nrow(sorlie))*1e-2
# Survival data + covariates
surv <- Surv(time,sorlie$status)
X <- as.matrix(sorlie[,15:24])
# Fit additive hazards model
fit1 <- ahaz(surv, X)
summary(fit1)
# Univariate models
X <- as.matrix(sorlie[,3:ncol(sorlie)])
fit2 <- ahaz(surv, X, univariate = TRUE)
# Equivalent to the following (slower) solution
beta <- apply(X,2,function(x){coef(ahaz(surv,x))})
plot(beta,coef(fit2))
```

*ahaz*version 1.15 Index]