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- _T_e_s_t _t_h_e _p_r_o_p_o_r_t_i_o_n_a_l _h_a_z_a_r_d_s _a_s_s_u_m_p_t_i_o_n _o_f _a _C_o_x _r_e_g_r_e_s_-
- _s_i_o_n.
-
- cox.zph(fit, transform='km', global=T)
-
- _A_r_g_u_m_e_n_t_s:
-
- fit:
- the result of fitting a Cox regression model, using the
- coxph function.
-
- transform:
- survival times transformed before the test is per-
- formed. Possible values are the character strings
- 'km', 'rank', 'identity', or a function of one argu-
- ment.
-
- global:
- should a global chisquare test be done, in additoin to
- the
- per-variable tests.
-
- x:
- if true, then the result will be a list containing the
- test table (a matrix), x and y. If false then only the
- test table is returned.
-
- Value:
-
- an object of class cox.zph, with components:
-
- _A_r_g_u_m_e_n_t_s:
-
- 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
- chisquare, 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=T 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 pro-
- portional 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 residu-
- als. Biometrika, vol 81, 515-26.
-
- _E_x_a_m_p_l_e_s:
-
- fit <- coxph( Surv(futime, fustat) ~ age + surgery, jasa, x=T)
- temp<- cox.zph(fit)
- print(temp) #display the results
- plot(temp) #plot curves
-
-