cholesterol             package:multcomp             R Documentation

_C_h_o_l_e_s_t_e_r_o_l _R_e_d_u_c_t_i_o_n _D_a_t_a _S_e_t

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

     Cholesterol reduction for five treatments.

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

     data("cholesterol")

_F_o_r_m_a_t:

     This data frame contains the following variables

     _t_r_t treatment groups, a factor at levels '1time', '2times',
          '4times', 'drugD' and 'drugE'.

     _r_e_s_p_o_n_s_e cholesterol reduction.

_D_e_t_a_i_l_s:

     A clinical study was conducted to assess the effect of three
     formulations of the same drug on reducing cholesterol. The
     formulations were 20mg at once ('1time'), 10mg twice a day
     ('2times'), and 5mg four times a day ('4times'). In addition, two
     competing drugs were used as control group ('drugD' and 'drugE').
     The purpose of  the study was to find which of the formulations,
     if any, is efficacious and how these formulations compare with the
     existing drugs.

_S_o_u_r_c_e:

     P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg
     (1999). _Multiple Comparisons and Multiple Tests Using the SAS
     System_. Cary, NC: SAS Institute Inc., page 153.

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

       ### adjusted p-values for all-pairwise comparisons in a one-way layout 
       ### set up ANOVA model  
       amod <- aov(response ~ trt, data = cholesterol)

       ### set up multiple comparisons object for all-pair comparisons
       cht <- glht(amod, linfct = mcp(trt = "Tukey"))

       ### cf. Westfall et al. (1999, page 171)
       summary(cht, test = univariate())
       summary(cht, test = adjusted("Shaffer"))
       summary(cht, test = adjusted("Westfall"))

       ### use only a subset of all pairwise hypotheses
       K <- contrMat(table(cholesterol$trt), type="Tukey")
       Ksub <- rbind(K[c(1,2,5),],
                     "D - test" = c(-1, -1, -1, 3, 0),
                     "E - test" = c(-1, -1, -1, 0, 3))

       ### reproduce results in Westfall et al. (1999, page 172)
       amod <- aov(response ~ trt - 1, data = cholesterol)
       summary(glht(amod, linfct = mcp(trt = Ksub[,5:1])), 
               test = adjusted("Westfall"))

