ciapower                package:Hmisc                R Documentation

_P_o_w_e_r _o_f _I_n_t_e_r_a_c_t_i_o_n _T_e_s_t _f_o_r _E_x_p_o_n_e_n_t_i_a_l _S_u_r_v_i_v_a_l

_D_e_s_c_r_i_p_t_i_o_n:

     Uses the method of Peterson and George to compute the power of an
     interaction test in a 2 x 2 setup in which all 4 distributions are
     exponential.  This will be the same as the power of the Cox model
     test if assumptions hold.  The test is 2-tailed.   The duration of
     accrual is specified (constant accrual is assumed), as is the
     minimum follow-up time. The maximum follow-up time is then
     'accrual + tmin'.  Treatment allocation is assumed to be 1:1.

_U_s_a_g_e:

     ciapower(tref, n1, n2, m1c, m2c, r1, r2, accrual, tmin, 
              alpha=0.05, pr=TRUE)

_A_r_g_u_m_e_n_t_s:

    tref: time at which mortalities estimated 

      n1: total sample size, stratum 1 

      n2: total sample size, stratum 2 

     m1c: tref-year mortality, stratum 1 control 

     m2c: tref-year mortality, stratum 2 control 

      r1: % reduction in 'm1c' by intervention, stratum 1 

      r2: % reduction in 'm2c' by intervention, stratum 2 

 accrual: duration of accrual period 

    tmin: minimum follow-up time 

   alpha: type I error probability 

      pr: set to 'FALSE' to suppress printing of details 

_V_a_l_u_e:

     power

_S_i_d_e _E_f_f_e_c_t_s:

     prints

_A_U_T_H_O_R:

     Frank Harrell

     Department of Biostatistics

     Vanderbilt University

     f.harrell@vanderbilt.edu

_R_e_f_e_r_e_n_c_e_s:

     Peterson B, George SL: Controlled Clinical Trials 14:511-522;
     1993.

_S_e_e _A_l_s_o:

     'cpower', 'spower'

_E_x_a_m_p_l_e_s:

     # Find the power of a race x treatment test.  25% of patients will
     # be non-white and the total sample size is 14000.  
     # Accrual is for 1.5 years and minimum follow-up is 5y.
     # Reduction in 5-year mortality is 15% for whites, 0% or -5% for
     # non-whites.  5-year mortality for control subjects if assumed to
     # be 0.18 for whites, 0.23 for non-whites.
     n <- 14000
     for(nonwhite.reduction in c(0,-5)) {
       cat("\n\n\n% Reduction in 5-year mortality for non-whites:",
           nonwhite.reduction, "\n\n")
       pow <- ciapower(5,  .75*n, .25*n,  .18, .23,  15, nonwhite.reduction,  
                       1.5, 5)
       cat("\n\nPower:",format(pow),"\n")
     }

