help metandi postestimation                                     (Roger Harbord)
                                                             also see:  metandi
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Title

    metandi postestimation -- postestimation tools for metandi


 Description

    Many of Stata's standard postestimation tools will not work after 
    metandi, or won't work as expected, as metandi reshapes the data before
    fitting the model.

    The notable exception is predict, which can be used to obtain posterior
    predictions (empirical Bayes estimates) of the sensitivity and
    specificity in each study (mu), as well as various statistics that may be
    useful for detecting outliers (e.g. ustd) and influential observations
    (cooksd).

    metandiplot graphs the results from metandi.


Syntax for predict

        predict [type] newvarlist [if] [in] [, statistic]

    where statistic is

        mu           posterior predicted (empirical Bayes) sensitivity and
                         specificity; the default
        u            posterior means (empirical Bayes predictions, BLUPs) of
                         random effects
        sdu          posterior standard deviations of the random effects
        ustd         standardized posterior means of random effects
        linpred      linear predictor with empirical Bayes predictions
                         plugged in. linpred = xb + u
        cooksd       Cook's distance for each study.  Only available when
                         model was fitted using gllamm.


    Most of the above statistics require two new variables to store them, one
    for the statistic associated with sensitivity, and one for the statistic
    associated with specificity.  If newvarlist contains only one newvar, the
    statistics associated with sensitivity and specificity will be stored in
    newvar1 and newvar0 respectively.  cooksd, however, is computed once for
    each study and therefore requires only one newvar.


Remarks

    If the model was fitted using gllamm, the predictions are obtained using 
    gllapred, whose help page contains further details of the statistics
    available.  See also the GLLAMM manual (Rabe-Hesketh, Skrondal & Pickles
    2004) and Rabe-Hesketh & Skrondal (2005).  If the model was fitted using
    xtmelogit, the predictions are obtained using predict - see xtmelogit
    postestimation##predict.

Examples

        . metandi tp fp fn tn

    Add empirical Bayes estimates to metandiplot:

        . predict eb

        . metandiplot, addplot(scatter eb1 eb0)

    Check for particularly influential observations using Cook's distance:

        . predict cook, cooksd

        . scatter cook studyid, mlabel(author)

    Check for outliers using standardized predicted random effects
    (interpretable as standardized level-2 residuals):

        . predict ustd_Se ustd_Sp, ustd

        . scatter ustd_Se ustd_Sp, xscale(rev) mlab(studyid) xline(0)
              yline(0)


Also see

    Online:  gllapred, metandi, metandiplot