Big Green CD 8/BGCD_8_Dev.iso/NEXTSTEP/UNIX/Educational/R-0.49-MI/R-0.49-I/help/survival4/coxph

Text File | 1997-09-13 | 6.6 KB | 163 lines

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         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)

    _A_r_g_u_m_e_n_t_s:

          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 func-
         tion.

             data:
         a data.frame in which to interpret the variables named
         in the formula, or in the subset and the weights argu-
         ment.

           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()a.action.

          weights:
         case weights.

              eps:
         convergence criteria.  Iteration will continue until
         relative change in log-likelihood is less than eps.
         Default is .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:
         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 com-
         putaionally.  The exact method computes the exact par-
         tial 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:
         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,x,y:
         flags to control what is returned.  If these are true,
         then the model frame, the model matrix, and/or the
         response is returned as components of the fitted model,
         with the same names as the flag arguments.

         Value:

         an object of class "coxph"

         Depending on the call, the predict, residuals, and
         survfit routines may need to reconstruct the x matrix
         created by coxph.  Differences in the environment, such
         as which data frames are attached or the value of
         options()ontrasts, may cause this computation to fail
         or worse, to be incorrect.  See the survival overview
         document for details.  The proportional hazards model
         is usually expressed in terms of a single survival time
         value for each person, with possible censoring.  Ander-
         son and Gill reformulated the same problem as a count-
         ing process; as time marches onward we observe the
         events for a subject, rather like watching a Geiger
         counter.  The data for a subject is presented as multi-
         ple rows or "observations", each of which applies to an
         interval of observation (start, stop].

         There are two special terms that may be used in the
         model equation.  A 'strata' term identifies a strati-
         fied Cox model; separate baseline hazard functions are
         fit for each strata.  The cluster term is used to com-
         pute a robust variance for the model.  The term +
         cluster(id), where id == unique(id), is equivalent to
         specifying the robust=T argument, and produces an
         approximate jackknife estimate of the variance.  If the
         id variable were not unique, but instead identifies
         clusters of correlated observations, then the variance
         estimate is based on a grouped jackknife.

         In certain data cases the actual MLE estimate of a
         coefficient is infinity, e.g., a dichotomous variable
         where one of the groups has no events.  When this hap-
         pens the associated coefficient grows at a steady pace
         and a race condition will exist in the fitting routine:
         either the log likelihood converges, the information
         matrix becomes effectively singular, an argument to exp
         becomes too large for the computer hardware, or the
         maximum number of interactions is exceeded.  The rou-
         tine attempts to detect when this has happened, not
         always successfully.

         References:

         P. Andersen and R. Gill. "Cox's regression model for
         counting processes, a large sample study", Annals of
         Statistics, 10:1100-1120, 1982. T.Therneau, P.
         Grambsch, and T.Fleming. "Martingale based residuals
         for survival models", Biometrika, March 1990.

         cluster, survfit, Surv, strata.

    _E_x_a_m_p_l_e_s:

         # Create the simplest test data set
         #
         > test1 <- list(time=  c(4, 3,1,1,2,2,3),
                         status=c(1,NA,1,0,1,1,0),
                         x=     c(0, 2,1,1,1,0,0),
                         sex=   c(0, 0,0,0,1,1,1))
         > coxph( Surv(time, status) ~ x + strata(sex), test1)  #stratified model
         #
         # 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))