summary.aareg            package:survival            R Documentation

_S_u_m_m_a_r_i_z_e _a_n _a_a_r_e_g _f_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     Creates the overall test statistics for an Aalen additive
     regression model

_U_s_a_g_e:

     ## S3 method for class 'aareg':
     summary(object, maxtime, test=c("aalen", "nrisk"), scale=1,...)

_A_r_g_u_m_e_n_t_s:

  object: the result of a call to the 'aareg' function 

 maxtime: truncate the input to the model at time "maxtime" 

    test: the relative time weights that will be used to compute the
          test 

   scale: scales the coefficients.   For some data sets, the
          coefficients of the Aalen model will be very small (10-4);
          this simply multiplies the printed values by a constant, say
          1e6, to make the printout easier to read. 

     ...: for future methods

_D_e_t_a_i_l_s:

     It is not uncommon for the very right-hand tail of the plot to
     have large outlying values, particularly for the standard error.  
     The 'maxtime' parameter can then be used to truncate the range so
     as to avoid these. This gives an updated value for the test
     statistics, without refitting the model.

_V_a_l_u_e:

     a list is returned with the following components

  table : a matrix with rows for the intercept and each covariate, and
          columns giving a slope estimate, the test statistic, it's
          standard error, the z-score and a p-value 

   test : the time weighting used for computing the test statistics 

 test.statistic : the vector of test statistics 

 test.var : the model based variance matrix for the test statistic 

 test.var2 : optionally, a robust variance matrix for the test
          statistic 

  chisq : the overall test (ignoring the intercept term) for
          significance of any variable 

      n : a vector containing the number of observations, the number of
          unique death times used in the computation, and the total
          number of unique death times 

_S_e_e _A_l_s_o:

     aareg, plot.aareg

_E_x_a_m_p_l_e_s:

     afit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
          dfbeta=TRUE)
     summary(afit)
     ## Not run: 
                   slope   test se(test) robust se     z        p 
     Intercept  5.05e-03    1.9     1.54      1.55  1.23 0.219000
           age  4.01e-05  108.0   109.00    106.00  1.02 0.307000
           sex -3.16e-03  -19.5     5.90      5.95 -3.28 0.001030
       ph.ecog  3.01e-03   33.2     9.18      9.17  3.62 0.000299

     Chisq=22.84 on 3 df, p=4.4e-05; test weights=aalen
     ## End(Not run)

     summary(afit, maxtime=600)
     ## Not run: 
                   slope   test se(test) robust se      z        p 
     Intercept  4.16e-03   2.13     1.48      1.47  1.450 0.146000
           age  2.82e-05  85.80   106.00    100.00  0.857 0.392000
           sex -2.54e-03 -20.60     5.61      5.63 -3.660 0.000256
       ph.ecog  2.47e-03  31.60     8.91      8.67  3.640 0.000271

     Chisq=27.08 on 3 df, p=5.7e-06; test weights=aalen
     ## End(Not run)

