coxreg {eha} | R Documentation

## Cox regression

### Description

Performs Cox regression with some special attractions, especially sampling of risksets and the weird bootstrap.

### Usage

    coxreg(formula = formula(data), data = parent.frame(), weights,
    subset, t.offset, na.action = getOption("na.action"), init = NULL, method =
    c("efron", "breslow", "mppl", "ml"), control = list(eps = 1e-08, maxiter =
    25, trace = FALSE), singular.ok = TRUE, model = FALSE, center = TRUE, x =
    FALSE, y = TRUE, hazards = TRUE, boot = FALSE, efrac = 0, geometric = FALSE,
    rs = NULL, frailty = NULL, max.survs = NULL)

### Arguments

`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.
`weights` | Case weights; time-fixed or time-varying.
`subset` | An optional vector specifying a subset of observations to be used in the fitting process.
`t.offset` | Case offsets; time-varying.
`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 of the iteration. Default initial value is zero for all variables.
`method` | Method of treating ties, "efron" (default), "breslow", "mppl" (maximum partial partial likelihood), or "ml" (maximum likelihood).
`control` | a list with components `eps` (convergence criterion), `maxiter` (maximum number of iterations), and `silent` (logical, controlling amount of output). You can change any component without mention the other(s).
`singular.ok` | Not used
`model` | Not used
`center` | Logical. If `center = TRUE` (default), the baseline hazards are calculated at the means of the covariates and for the reference category for factors, otherwise at the value zero. See Details.
`x` | Return the design matrix in the model object?
`y` | return the response in the model object?
`hazards` | Calculate baseline hazards? Default is TRUE.
`boot` | Number of boot replicates. Defaults to FALSE, no boot samples.
`efrac` | Upper limit of fraction failures in 'mppl'.
`geometric` | If TRUE, forces an 'ml' model with constant riskset probability. Default is FALSE.
`rs` | Risk set?
`frailty` | Grouping variable for frailty analysis. Not in use yet.
`max.survs` | Sampling of risk sets? If given, it should be (the upper limit of) the number of survivors in each risk set.

### Details

The default method, `efron`, and the alternative, `breslow`, are both the same as in `coxph` in package `survival`. The methods `mppl` and `ml` are maximum likelihood, discrete-model, based.

If `center = TRUE` (default), graphs show the "baseline" distribution at the means of (continuous) covariates, and for the reference category in case of factors (avoiding representing "flying pigs"). If `center = FALSE` the baseline distribution is at the value zero of all covariates. It is usually a good idea to use `center = FALSE` in combination with "precentering" of covariates, that is, subtracting a reference value, ideally close to the center of the covariate distribution. In that way, the "reference" will be the same for all subsets of the data.

### Value

A list of class `c("coxreg", "coxph")` with components

`coefficients` | Fitted parameter estimates.
`var` | Covariance matrix of the estimates.
`loglik` | Vector of length two; first component is the value at the initial parameter values, the second component is the maximized value.
`score` | The score test statistic (at the initial value).
`linear.predictors` | The estimated linear predictors.
`residuals` | The martingale residuals.
`hazard` | The estimated baseline hazard, calculated at the means of the covariates (rather, columns of the design matrix). Is a list, with one component per stratum. Each component is a matrix with two columns, the first contains risktimes, the second the corresponding hazard atom.
`means` | Means of the columns of the design matrix corresponding to covariates, if `center = TRUE`. Columns corresponding to factor levels gice a zero in the corresponding position in `means`. If `center = FALSE`, `means` are all zero.
`w.means` | Weighted (against exposure time) means of covariates; weighted relative frequencies of levels of factors.
`n` | Number of spells in indata (possibly after removal of cases with NA's).
`events` | Number of events in data.
`terms` | Used by extractor functions.
`assign` | Used by extractor functions.
`y` | The Surv vector.
`isF` | Logical vector indicating the covariates that are factors.
`covars` | The covariates.
`ttr` | Total Time at Risk.
`levels` | List of levels of factors.
`formula` | The calling formula.
`bootstrap` | The (matrix of) bootstrap replicates, if requested on input. It is up to the user to do whatever desirable with this sample.
`boot.sd` | The estimated standard errors of the bootstrap replicates.
`call` | The call.
`method` | The method.
`convergence` | Did the optimization converge?
`fail` | Did the optimization fail? (Is `NULL` if not).

### Warning

The use of `rs` is dangerous, see note. It can however speed up computing time considerably for huge data sets.

### Note

This function starts by creating risksets, if no riskset is supplied via `rs`, with the aid of `risksets`. Supplying output from `risksets` via `rs` fails if there are any NA's in the data! Note also that it depends on stratification, so `rs` contains information about stratification. Giving another strata variable in the formula is an error. The same is ok, for instance to supply stratum interactions.

### Author(s)

Göran Broström

### References

Broström, G. and Lindkvist, M. (2008). Partial partial likelihood. Communications in Statistics: Simulation and Computation 37:4, 679-686.

### See Also

`coxph`, `risksets`

### Examples

     dat <- data.frame(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))
     coxreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
     # Same as:
     rs <- risksets(Surv(dat$time, dat$status), strata = dat$sex)
     coxreg( Surv(time, status) ~ x, data = dat, rs = rs) #stratified model

[Package eha version 2.6.0 Index]