coxph(formula, data=sys.parent(), subset,
      na.action, weights, eps = 0.0001, init,
      iter.max=10, method=c("efron","breslow","exact"),
      singular.ok=T, robust,
      model=F, x=F, y=T)

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 and the weights argument.

subset:

expression indicating which subset of the rows of data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included (or excluded if negative), or a character vector of row names to be included. All observations are included by default.

na.action:

a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is options()$na.action.

weights:

vector of case weights. If weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.

eps:

convergence threshold. Iteration will continue until the relative change in the log-likelihood is less than eps. Default is 0.0001.

init:

vector of initial values of the iteration. Default initial value is zero for all variables.

iter.max:

maximum number of iterations to perform. Default is 10.

method:

a character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. Nearly all Cox regression programs use the Breslow method by default, but not this one. The Efron approximation is used as the default here, as it is much more accurate when dealing with tied death times, and is as efficient computationally. The exact method computes the exact partial likelihood, which is equivalent to a conditional logistic model. If there are a large number of ties the computational time will be excessive.

singular.ok:

logical value indicating how to handle collinearity in the model matrix. If TRUE, the program will automatically skip over columns of the X matrix that are linear combinations of earlier columns. In this case the coefficients for such columns will be NA, and the variance matrix will contain zeros. For ancillary calculations, such as the linear predictor, the missing coefficients are treated as zeros.

robust:

if TRUE a robust variance estimate is returned. Default is TRUE if the model includes a cluster operative, FALSE otherwise.

model:

logical value: if TRUE, the model frame is returned in component model

x:

logical value: if TRUE, the model frame is returned in component x.

y:

logical value: if TRUE, the model frame is returned in component y.

Therneau, T., Grambsch, P., and Fleming. T. (1990). Martingale based residuals for survival models. Biometrika 77, 147-160.

# Create the simplest test data set
test1 <- list(time=c(4,3,1,1,2,2,3),
              status=c(1,1,1,0,1,1,0),
              x=c(0,2,1,1,1,0,0),
              sex=c(0,0,0,0,1,1,1))

# Fit a stratified model
coxph(Surv(time, status) ~ x + strata(sex), test1)

# Create a simple data set for a time-dependent model
test2 <- list(start=c(1,2,5,2,1,7,3,4,8,8),
              stop=c(2,3,6,7,8,9,9,9,14,17),
              event=c(1,1,1,1,1,1,1,0,0,0),
              x=c(1,0,0,1,0,1,1,1,0,0))

summary(coxph(Surv(start, stop, event) ~ x, test2))

# Fit a stratified model, clustered on patients
bladder1 <- bladder
bladder1$start <- NULL
bladder1 <- bladder1[bladder1$enum < 5, ]
coxph(Surv(stop, event) ~ (rx + size + number) * strata(enum) +
      cluster(id), bladder1, method='breslow')