ahaz {ahaz} | R Documentation |
Fit a semiparametric additive hazards regression model. Right-censored and left-truncated survival data are supported.
ahaz(surv, X, weights, univariate=FALSE, robust=FALSE)
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 |
The semiparametric additive hazards model specifies a hazard function of the form:
h(t) = h_0(t) + beta' * Z_i
for i=1,...,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.
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 |
Lin, D.Y. & Ying, Z. (1994). Semiparametric analysis of the additive risk model. Biometrika; 81:61-71.
summary.ahaz
, predict.ahaz
,
plot.ahaz
.
The functions coef
,
vcov
, residuals
.
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))