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help for  mici                                                  (SJ3-3: st0000)
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Calculate confidence intervals datasets created by miset

        mici [, indiv] : varlist [in range] [if exp] [, level(#) binomial
                           poisson exposure(varname)]


Description

    mici calculates confidence intervals separately for each of the miset
    datasets and calculates overall confidence intervals by Rubin's rule of
    combination.


Options

    indiv reports the confidence intervals from each of the individual miset
        datasets, as well as the overall confidence intervals. By default,
        only the overall confidence interval is displayed.

    level specifies the desired level of significance in calculating the
        confidence intervals. The default is 95%.

    binomial specifies that each of the variables in varlist has a binomial
        distribution, and mici will calculate the binomial standard error for
        each of the miset datasets and then calculate an overall confidence
        interval for the corresponding proportion using Rubin's combining
        rule. If any variable in varlist is not binary (value 0 or 1), an
        error message is given. Note that the "exact" binomial method is not
        used since it does not enable application of Rubin's rule.

    poisson specifies that the variables are Poisson-distributed counts.
        Poisson standard errors are calculated from individual miset
        datasets, and overall confidence intervals are computed using Rubin's
        rule.

    exposure(varname) is used only with poisson.  varname contains the total
        exposure during which the number of events recorded in varlist was
        observed.


Remarks

    mici can only be used after the multiple datasets have been declared by 
    miset.


Examples

    . mici: var1 if var2==0, binomial
    . mici, indiv: var1


Authors

    Ning Li, Philip Greenwood, and John Carlin, Clinical Epidemiology &
    Biostatistics Unit, Murdoch Children's Research Institute and University
    of Melbourne. Email jbcarlin@unimelb.edu.au


References

    Rubin, D. B. 1987. Multiple Imputation for Nonresponse in Surveys.  New
        York: John Wiley & Sons.

    Schafer, J. L. 1997. Analysis of Incomplete Multivariate Data.  London:
        Chapman & Hall.


Also see

    Online: help for miappend, mimerge, misave, mido, mifit, milincom, 
              mireset, miset