cox.zph               package:survival               R Documentation

Test the Proportional Hazards Assumption of a Cox Regression

Description:

     Test the proportional hazards assumption for a Cox regression
     model fit ('coxph').

Usage:

     cox.zph(fit, transform="km", global=TRUE)

Arguments:

     fit: the result of fitting a Cox regression model, using the
          'coxph' function. 

transform: a character string specifying how the survival times should
          be transformed before the test is performed. Possible values
          are '"km"', '"rank"', '"identity"' or  a function of one
          argument. The default is '"km"' for right-censored data and
          '"identity"' for counting-processing data. 

  global: should a global chi-square test be done, in addition to the
          per-variable tests. 

Value:

     an object of class '"cox.zph"', with components: 

   table: a matrix with one row for each variable, and optionally a
          last row for the global test. Columns of the matrix contain
          the correlation coefficient between transformed survival time
          and the scaled Schoenfeld residuals, a chi-square, and the
          two-sided p-value. For the global test there is no
          appropriate correlation, so an NA is entered into the matrix
          as a placeholder. 

       x: the transformed time axis. 

       y: the matrix of scaled Schoenfeld residuals.  There will be one
          column per variable and one row per event.  The row labels
          contain the original event times (for the identity transform,
          these will be the same as 'x'). 

    call: the calling sequence for the routine.

          The computations require the original 'x' matrix of the Cox
          model fit. Thus it saves time if the 'x=TRUE' option is used
          in 'coxph'. This function would usually be followed by both a
          plot and a print of the result. The plot gives an estimate of
          the time-dependent coefficient 'beta(t)'. If the proportional
          hazards assumption is true, 'beta(t)' will be a horizontal
          line.  The printout gives a test for 'slope=0'. 

References:

     P. Grambsch and T. Therneau (1994), Proportional hazards tests and
     diagnostics based on weighted residuals. _Biometrika,_ *81*,
     515-26.

See Also:

     'coxph', 'Surv'.

Examples:

     fit <- coxph( Surv(futime, fustat) ~ age + rx, ovarian)
     temp<- cox.zph(fit)
     print(temp)                  #display the results
     plot(temp)                   #plot curves

