survfit.coxph            package:survival            R Documentation

_C_o_m_p_u_t_e _a _S_u_r_v_i_v_a_l _C_u_r_v_e _f_r_o_m _a _C_o_x _m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the predicted survivor function for a Cox proportional 
     hazards model.

_U_s_a_g_e:

     ## S3 method for class 'coxph':
     survfit(formula, newdata, 
             se.fit=TRUE, conf.int=.95,
             individual=FALSE, 
             type,vartype,
             conf.type=c("log","log-log","plain","none"),...)

_A_r_g_u_m_e_n_t_s:

 formula: A 'coxph' object.  

 newdata: a data frame with the same variable names as those that
          appear  in the 'coxph' formula.  

          The curve(s) produced will be representative of a cohort
          whose  covariates correspond to the values in 'newdata'. 
          Default is the mean of the covariates used in the  'coxph'
          fit.  

individual: a logical value indicating whether each row of 'newdata'
          represents a distinct individual (FALSE, the default),  or if
          each row of the data frame represents different  time epochs
          for only one individual (TRUE).   In the former case the
          result will have one curve for each row in 'newdata', in the
          latter only a single curve will be produced. 

conf.int: the level for a two-sided confidence interval on the survival
          curve(s).  Default is 0.95.  

  se.fit: a logical value indicating whether standard errors should be 
          computed.  Default is 'TRUE'.  

type,vartype: a character string specifying the type of survival curve.
           Possible values are  '"aalen"' or '"kaplan-meier"'  (only
          the first two characters are necessary).  The default is
          '"aalen"'   Earlier versions of 'survfit'  used
          'type="tsiatis"' to get the '"aalen"' estimator.  For
          backward  compatibility, this is still allowed.  

conf.type: One of '"none"', '"plain"', '"log"' (the default), or
          '"log-log"'.  Only enough of the string to uniquely identify
          it is necessary. The first option causes confidence intervals
          not to be generated.  The second causes the standard
          intervals 'curve +- k *se(curve)', where k is determined from
          'conf.int'.  The log option calculates intervals based on the
          cumulative hazard or log(survival). The last option bases
          intervals on the log hazard or log(-log(survival)).  

     ...: for future methods

_D_e_t_a_i_l_s:

     See 'survfit' for more Details.

     The estimates used are the Kalbfleisch-Prentice  (Kalbfleisch and
     Prentice, 1980, p.86) and the Tsiatis/Link/Breslow,  which reduce
     to the Kaplan-Meier and Fleming-Harrington estimates, 
     respectively, when the weights are unity.   When the parent data
     is a Cox model, there is an extra term in the variance of the
     curve, due to the variance of the coefficients and  hence variance
     in the computed weights.  Details of the Aalen  estimator and its
     variance are found in Tsiatis (1981). 

     The two variants of the F-H estimate have to do with how ties are
     handled. If there were 3 deaths out of 10 at risk, then the first
     increments the hazard by 3/10 and the second by 1/10 + 1/9 + 1/8.
     For the first method S(t) = exp(H), where H is  the Nelson-Aalen
     cumulative hazard estimate, whereas the 'fh2' method will  give
     results S(t) results closer to the Kaplan-Meier.  For curves
     created  after a Cox model these correspond to the Breslow and
     Efron estimates,  respectively, and the proper choice is made
     automatically.

_V_a_l_u_e:

     an object of class '"survfit"'.   See 'survfit.object' for 
     details. Methods defined for survfit objects are   'print',
     'plot',  'lines', and 'points'.

_R_e_f_e_r_e_n_c_e_s:

     Dorey, F. J. and Korn, E. L. (1987).  Effective sample sizes for
     confidence  intervals for survival probabilities.  _Statistics in
     Medicine_  *6*, 679-87. 

     Fleming, T. H. and Harrington, D. P. (1984).  Nonparametric
     estimation of the  survival distribution in censored data.  _Comm.
     in Statistics_   *13*, 2469-86. 

     Kablfleisch, J. D. and Prentice, R. L. (1980).   _The Statistical
     Analysis of Failure Time Data._ New York:Wiley. 

     Link, C. L. (1984). Confidence intervals for the survival 
     function using Cox's proportional hazards model with   covariates.
      _Biometrics_   *40*, 601-610.

     Tsiatis, A. (1981). A large sample study of the estimate  for the
     integrated hazard function in Cox's regression  model for survival
     data. _Annals of Statistics_   *9*, 93-108.

_S_e_e _A_l_s_o:

     'print',   'plot',   'lines',    'coxph',   'Surv',   'strata'.

_E_x_a_m_p_l_e_s:

     #fit a Kaplan-Meier and plot it 
     fit <- survfit(Surv(time, status) ~ x, data = aml) 
     plot(fit, lty = 2:3) 
     legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3) 

     #fit a Cox proportional hazards model and plot the  
     #predicted survival for a 60 year old 
     fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) 
     plot(survfit(fit, newdata=data.frame(age=60)),
          xscale=365.25, xlab = "Years", ylab="Survival") 

     # Here is the data set from Turnbull
     #  There are no interval censored subjects, only left-censored (status=3),
     #  right-censored (status 0) and observed events (status 1)
     #
     #                             Time
     #                         1    2   3   4
     # Type of observation
     #           death        12    6   2   3
     #          losses         3    2   0   3
     #      late entry         2    4   2   5
     #
     tdata <- data.frame(time  =c(1,1,1,2,2,2,3,3,3,4,4,4),
                         status=rep(c(1,0,2),4),
                         n     =c(12,3,2,6,2,4,2,0,2,3,3,5))
     fit  <- survfit(Surv(time, time, status, type='interval') ~1, 
                   data=tdata, weight=n)

     #
     # Time to progression/death for patients with monoclonal gammopathy
     #  Competing risk curves (cumulative incidence)
     fit1 <- survfit(Surv(stop, event=='progression') ~1, data=mgus1,
                         subset=(start==0))
     fit2 <- survfit(Surv(stop, status) ~1, data=mgus1,
                         subset=(start==0), etype=event) #competing risks
     # CI curves are always plotted from 0 upwards, rather than 1 down
     plot(fit2, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
                 col=2:3, xlab="Years post diagnosis of MGUS")
     lines(fit1, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
                 conf.int=FALSE)
     text(10, .4, "Competing Risk: death", col=3)
     text(16, .15,"Competing Risk: progression", col=2)
     text(15, .30,"KM:prog")

