survdiff              package:survival              R Documentation

_T_e_s_t _S_u_r_v_i_v_a_l _C_u_r_v_e _D_i_f_f_e_r_e_n_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Tests if there is a difference between two or more survival curves
     using the G-rho family of tests, or for a single curve against a
     known alternative.

_U_s_a_g_e:

     survdiff(formula, data, subset, na.action, rho=0)

_A_r_g_u_m_e_n_t_s:

 formula: a formula expression as for other survival models, of the
          form 'Surv(time, status) ~ predictors'.  For a one-sample
          test, the predictors must consist of a single 'offset(sp)'
          term, where 'sp' is a vector giving the survival probability
          of each subject.  For a k-sample test, each unique
          combination of predictors defines a subgroup. A 'strata' term
          may be used to produce a stratified test. To cause missing
          values in the predictors to be treated as a separate group,
          rather than being omitted, use the 'strata' function with its
          'na.group=T' argument. 

    data: an optional data frame in which to interpret the variables
          occurring in the formula. 

  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'. 

     rho: a scalar parameter that controls the type of test. 

_V_a_l_u_e:

     a list with components:

       n: the number of subjects in each group. 

     obs: the weighted observed number of events in each group. If
          there are strata, this will be a matrix with one column per
          stratum. 

     exp: the weighted expected number of events in each group. If
          there are strata, this will be a matrix with one column per
          stratum. 

   chisq: the chisquare statistic for a test of equality. 

     var: the variance matrix of the test. 

  strata: optionally, the number of subjects contained in each stratum. 

_M_E_T_H_O_D:

     This function implements the G-rho family of Harrington and
     Fleming (1982), with weights on each death of S(t)^rho, where S is
     the Kaplan-Meier estimate of survival. With 'rho = 0' this is the
     log-rank or Mantel-Haenszel test, and with 'rho = 1' it is
     equivalent to the Peto & Peto modification of the Gehan-Wilcoxon
     test.

     If the right hand side of the formula consists only of an offset
     term, then a one sample test is done. To cause missing values in
     the predictors to be treated as a separate group, rather than
     being omitted, use the 'factor' function with its 'exclude'
     argument.

_R_e_f_e_r_e_n_c_e_s:

     Harrington, D. P. and Fleming, T. R. (1982). A class of rank test
     procedures for censored survival data. _Biometrika_ *69*, 553-566.

_E_x_a_m_p_l_e_s:

     ## Two-sample test
     survdiff(Surv(futime, fustat) ~ rx,data=ovarian)

     ## Stratified 7-sample test

     survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung)

     ## Expected survival for heart transplant patients based on
     ## US mortality tables
     expect <- survexp(futime ~ ratetable(age=(accept.dt - birth.dt),
          sex=1,year=accept.dt,race="white"), jasa, cohort=FALSE,
          ratetable=survexp.usr)
     ## actual survival is much worse (no surprise)
     survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect))