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:
for where
is the vector of covariates,
the vector of regression coefficients and
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))