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 _b_a_s_e_d _o_n _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. For dichotomous variables,
     approximate confidence intervals for the risk difference, risk
     ratio and odds ratio are available. If the variable of interest is
     the rate of tumours in long-term rodent carcinogenicity trials
     (without cause of death information),  confidence intervals for
     poly-k-adjusted tumour rates are available. For abundance data of
     multiple species, approximate simultaneous confidence intervals
     for differences of Simpson and Shannon-indices are implemented.

     Please note, that the methods implemented in this package are NOT
     validated yet and NOT published so far.

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


       Package:  MCPAN
       Type:     Package
       Version:  1.1-9
       Date:     2009-10-12
       License:  GPL

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

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

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

     Reference for muliple differences of proportions:

     _Schaarschmidt, F., Sill, M., and Hothorn, L.A. (2008):_
     Approximate Simultaneous Confidence Intervals for Multiple
     Contrasts of Binomial Proportions. Biometrical Journal 50,
     782-792.

     See binomRDci for the code that reproduces the examples in this
     reference.

     Reference for multiple differences of poly-k-rates:

     _Schaarschmidt, F., Sill, M., and Hothorn, L.A. (2008):_
     Poly-k-trend tests for survival adjusted analysis of tumor rates
     formulated as approximate multiple contrast test. Journal of
     Biopharmaceutical Statistics 18, 934-948.

     See poly3ci for the code that reproduces the example in this
     reference.

     Simultaneous confidence intervals for Dunnett-type comparisons to
     control using odds ratios:

     _Holford, T.R., Walter, S.D. and Dunnett, C.W. (1989):_
     Simultaneous interval estimates of the odds ratio in studies with
     two or more comparisons. Journal of Clinical Epidemiology 42,
     427-434.

     Background references:

     The Add-2 Confidence interval for the difference of two binomial
     proportions:

     _Agresti, A. and Caffo, B.(2000):_ Simple and effective confidence
     intervals for proportions and differences of proportions result
     from adding two successes and two failures. American Statistician
     54, p. 280-288.

     and its generalization to a single contrast of several proportions
     in:

     _Price, R.M. and Bonett, D.G. (2004):_ An improved confidence
     interval for a linear function of binomial proportions.
     Computational Statistics and Data Analysis 45, 449-456.

     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.

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

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

     # 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 binomRDci and poly3ci:

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

     plot(MethylCI)

