MCPAN-package             package:MCPAN             R Documentation

_M_u_l_t_i_p_l_e _c_o_m_p_a_r_i_s_o_n _p_r_o_c_e_d_u_r_e_s _u_s_i_n_g _n_o_r_m_a_l _a_p_p_r_o_x_i_m_a_t_i_o_n

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

     Multiple contrast tests and simultaneous confidence intervals
     using normal approximation, if individuals are randomly assigned
     to treatments in a oneway layout. If the variable of interest is
     dichotomous, the binom-methods can be used. If the variable of
     interest is the rate of tumours in long-term rodent
     carcinogenicity trials (without cause of death information), the
     poly3-methods can be used. The methods implemented in this package
     are NOT published in peer-reviewed journals so far.

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


       Package:  MCPAN
       Type:     Package
       Version:  1.0-4
       Date:     2007-08-15
       License:  GPL

_A_u_t_h_o_r(_s):

     Frank Schaarschmidt, Daniel Gerhard Maintainer: Frank
     Schaarschmidt <schaarschmidt@biostat.uni-hannover.de>

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

     For long-term rodent carcinogenicity data: The assumptions of
     poly-3-adjustment are outlined in:

     Bailer, J.A. and Portier, C.J. (1988): Effects of
     treatment-induced mortality and tumor-induced mortality on tests
     for carcinogenicity in small samples. Biometrics 44, 417-431.

     Peddada, S.D., Dinse, G.E., and Haseman, J.K. (2005): A
     survival-adjusted quantal response test for comparing tumor
     incidence rates. Applied Statistics 54, 51-61.

     For correlation of multiple contrasts of binomial proportions,
     see: Bretz F, Hothorn L.: Detecting dose-response using contrasts:
     asymptotic power and sample size determination for binomial data.
     Statistics in Medicine 2002; 21: 3325-3335.

     Simulation results (coverage probability of simultaneous
     confidence intervals) for the binomial proportions and
     poly-3-adjusted tumour rates can be found in:

     Sill, M. (2007): .... Master thesis, Institute of Biostatistics,
     Leibniz University Hannover.

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

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

     # # # 1)
     # Adjusted p-values and simultaneous confidence intervals 
     # for 2xk tables of binomial data: 
     # binomtest, binomci

     # Difference of proportions

     binomRDtest(x=c(2,6,4,13), n=c(34,33,36,34),
      names=c("Placebo", "50", "75", "150"),
      type="Dunnett", method="ADD1")

     binomRDci(x=c(2,6,4,13), n=c(34,33,36,34),
      names=c("Placebo", "50", "75", "150"),
      type="Dunnett", method="ADD1")

     # Odds ratios:

     binomORci(x=c(2,6,4,13), n=c(34,33,36,34),
      names=c("Placebo", "50", "75", "150"),
      type="Dunnett")

     # For more details on evaluation,
     # see:
     # ?liarozole

      data(liarozole) 


     # # # 2)
     # Adjusted p-values and simultaneous confidence intervals 
     # for poly-3-adjusted tumour rates: 
     # poly3test, poly3ci

     data(methyl)
     methyl

     # poly-3-adjusted sample estimates:

     poly3estf(time=methyl$death,
      status=methyl$tumour,
      f=methyl$group)

     # Simultaneous Add-1-confidence intervals
     # for difference to the control group:

     poly3ci(time=methyl$death, status=methyl$tumour,
      f=methyl$group, method="ADD1",
      type="Dunnett", alternative="greater")

     # Test for trend, based on Changepoint contrasts:

     poly3test(time=methyl$death, status=methyl$tumour,
      f=methyl$group, method="ADD1",
      type="Changepoint", alternative="greater")

     # # # 3) Plot of confidence intervals
     # created by binomci and poly3ci:

     MethylCI <- poly3ci(time=methyl$death, status=methyl$tumour,
      f=methyl$group, method="ADD1",
      type="Dunnett", alternative="greater")

     plot(MethylCI)

