nltm {nltm} | R Documentation |
Fits a non-linear transformation (NLT) model for analyzing survival data, see Tsodikov (2003). The class of NLT models includes the following currently supported models Cox proportional hazard and proportional hazard cure models, proportional odds model, proportional hazard - proportional hazard cure model, proportional hazard - proportional odds model, Gamma frailty model, and proportional hazard - proportional odds model.
nltm(formula=formula(data), data=parent.frame(), subset, na.action, init, control, model=c("PH","PHC","PO","PHPHC","PHPOC","GFM","PHPO"), verbose=FALSE, ...)
formula |
A formula object, with the response on the left of a
~ operator, and the terms on the right. The response must be
a survival object as returned by the Surv function. |
data |
A data.frame in which to interpret the variables named in
the formula , or in the subset argument. |
subset |
Expression saying that only a subset of the rows of the data should be used in the fit. |
na.action |
A missing-data filter function, applied to the
model.frame, after any subset argument has been used. Default is
options()$na.action . |
init |
Vector of initial values for the calculation of the maximum likelihood estimator of the regression parameters. Default initial value is zero. |
control |
Object of class coxph.control specifying
iteration limit and other control options. Default is
nltm.control(...) . |
model |
A character string specifying a non-linear transformation
model. Default Proportional Hazards Model.
The conditional survival function S(t|z) given the covariates z of each of the models currently supported are given below. Let S_0(t) be the non-parametric baseline survival function, and theta(z) and eta(z) predictors. We take theta(z)=exp(β_theta z) and eta(z)=exp(β_eta z).
|
verbose |
If TRUE it stores information from maximization of likelihood and calculation of information matrix in a file. Default is FALSE. |
... |
Other arguments |
an object of class "coxph"
.
Gilda Garibotti (garibott AT math.utah.edu), Alexander Tsodikov
Tsodikov AD (2003) "Semiparametric models: a generalized self-consistency approach". Journal of the Royal Statistical Society B, 65, Part 3, 759-774.
Tsodikov AD, Ibrahim JG, Yakovlev AY (2003) "Estimating cure rates from survival data: an alternative to two-component mixture models". Journal of the American Statistical Association, Vol. 98, No. 464, 1063-1078.
Tsodikov AD (2002) "Semi-parametric models of long- and short-term survival: an application to the analysis of breast cancer survival in Utah by age and stage". Statistics in Medicine, 21, 895-920.
Wendland MMM, Tsodikov AD, Glenn MJ, Gaffney DK (2004) "Time interval to the development of breast carcinoma after treatment for Hodgkin disease". Cancer Vol. 101, No. 6, 1275-1282.
coxph
, nltm.control
## Not run: # Simple test data set test1 <- list(time=c(10,7,32,23,22,6,16,34,32,25,11,20,19,6,17,35,6,13,9,6,1), status=c(1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0), size=c(1.79,7.93,2.02,6.89,2.30,7.82,1.25,9.85,6.02,3.43,4.72,7.45,8.83,9.53,1.10,1.06,5.25,5.86,2.03,3.62,3.52), age=factor(c(65,65,65,65,99,45,65,99,99,99,65,45,65,55,45,45,55,55,55,99,65))) nltm(Surv(time,status) ~ size + age, data=test1, model="PO") ## End(Not run)