binomORci               package:MCPAN               R Documentation

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

     Approximate simultaneous confidence intervals for (weighted
     geometric means of) odds ratios are constructed. Estimates are
     derived from fitting a glm on the logit-link, approximate
     intervals are constructed on the log-link, and transformed to
     origninal scale.

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

     binomORci(x, ...)

     ## Default S3 method:
     binomORci(x, n, names = NULL,
      type = "Dunnett", method="GLM", cmat = NULL,
      alternative = "two.sided", conf.level = 0.95,
      dist="MVN", ...)

     ## S3 method for class 'formula':
     binomORci(formula, data,
      type = "Dunnett", method="GLM", cmat = NULL,
      alternative = "two.sided", conf.level = 0.95,
      dist="MVN", ...)

     ## S3 method for class 'table':
     binomORci(x,
      type = "Dunnett",method="GLM", cmat = NULL,
      alternative = "two.sided", conf.level = 0.95,
      dist="MVN", ...)

     ## S3 method for class 'matrix':
     binomORci(x,
      type = "Dunnett", method="GLM", cmat = NULL,
      alternative = "two.sided", conf.level = 0.95,
      dist="MVN", ...)

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

       x: a numeric vector, giving the number of successes in I
          independent samples, or an object of class '"table"',
          representing the 2xk-table, or an object of class '"matrix"',
          representing the 2xk-table

       n: numeric vector, giving the number of trials (i.e. the sample
          size) in each of the I groups (only required if 'x' is a
          numeric vector, ignored otherwise)  

   names: an optional character string, giving the names of the groups/
          sample in 'x', 'n'; if not specified the possible names of x
          are taken as group names (ignored if 'x' is a table or
          matrix)

 formula: a two-sided formula of the style 'response ~ treatment',
          where 'response' should be a categorical variable with two
          levels, while treatment should be a factor specifying the
          treatment levels

    data: a data.frame, containing the variables specified in formula

    type: a character string, giving the name of a contrast method, as
          defined in 'contrMat(multcomp)'; ignored if 'cmat' is
          sepcified 

  method: a single character string, specifying the method for
          confidence interval computation; Options are '"GLM"' and
          '"Woolf"'. '"GLM"' takes the maximum likelihood estimates and
          the their standard errors; this yields a conservative
          confidence intervals with uninformative limits if x=0 and x=n
          occures. '"Woolf"' adds 0.5 to the cell counts, resulting in
          less conservative bounds. These can be liberal when extreme
          proportions are compared. 

    cmat: a optional contrast matrix 

alternative: a single character string, one of "two.sided", "less",
          "greater" 

conf.level: a single numeric value, simultaneous confidence level 

    dist: a character string, '"MVN"' invokes multiplicity adjustment
          via the multivariate normal distribution, '"N"' invokes use
          of quantiles of the univariate normal distribution

     ...: arguments to be passed to binomest, currently only 'success'
          labelling the event which should be considered as success

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

     This function calls glm and fits a one-way-model with family
     binomial on the logit-link. Then, the point estimates and
     variances estimates from the fit are taken to construct
     simultaneous confidence intervals for differences (of weighted
     arithmetic means) of log-odds. Applying the exponential function
     to these intervals on the logit scale yields intervals for ratios
     (of weighted geometric) of odds. For simple groupwise comparisons,
     one yields intervals for oddsratios. For the case of Dunnett-type
     contrasts, the calculated simultaneous confidence intervals are
     those described in Holford et al. (1989).

     Specifying 'method="GLM"' takes maximum likelihood estimates for
     the log-odds and their standard errors evaluated at the estimate.

     Specifying 'method="Woolf"' takes adds 0.5 to each cell count and
     computes point estimates and standard errors for these continuity
     corrected values. For the two-sample comparison this method is
     refered to as "adjusted Woolf" (Lawson, 2005). In this
     implementation, the lower bounds yielded by this method are
     additionally expanded to 0, if all values in the denominator are
     x=n or all values in the numerator are x=0, and the upper bounds
     are expanded to Inf,  if all values in the denominator are x=0 or
     all values in the numerator are x=n.

     Note, that for the case of general contrasts, the methods are not
     described explicitly so far.

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

     A object of class "binomORci", a list containing: 

conf.int: a matrix with 2 columns: lower and upper confidence bounds,
          and M rows

alternative : character string, as input

conf.level: single numeric value, as input

estimate: a matrix with 1 column: containing the estimates of the
          contrasts

       x: the observed number of successes

       n: the number of trials

       p: the estimated proportions

 success: a character string labelling the event considered as success

   names: the group names

  method: a character string, specifying the method of interval
          construction

    cmat: the contrast matrix used

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

     Frank Schaarschmidt, Daniel Gerhard

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

     Holford, TR, Walter, SD and Dunnett, CW (1989). Simultaneous
     interval estimates of the odds ratio in studies with two or more
     comparisons. Journal of Clinical Epidemiology 42, 427-434.

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

     Intervals for the risk difference binomRDci, summary for odds
     ratio confidence intervals summary.binomORci plot for confidence
     intervals plot.sci

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

     data(liarozole)

     table(liarozole)

     # Comparison to the control group "Placebo",
     # which is the fourth group in alpha-numeric
     # order:

     ORlia<-binomORci(Improved ~ Treatment,
      data=liarozole, success="y", type="Dunnett", base=4)
     ORlia
     summary(ORlia)
     plot(ORlia)

     # if data are available as table:

     tab<-table(liarozole)
     tab
     ORlia2<-binomORci(tab, success="y", type="Dunnett", base=4)
     ORlia2

     plot(ORlia2, lines=1, lineslty=3)

     ############################

     #  Performance for extreme cases

     # method="GLM" (the default)

     test1<-binomORci(x=c(0,1,5,20), n=c(20,20,20,20), names=c("A","B","C","D"))
     test1
     plot(test1)

     # adjusted Woolf interval

     test2<-binomORci(x=c(0,1,5,20), n=c(20,20,20,20), names=c("A","B","C","D"), method="Woolf")
     test2
     plot(test2)

