-------------------------------------------------------------------------------
help for fagan                                            (Ben Adarkwa Dwamena)

-------------------------------------------------------------------------------

Fagan's bayesian nomogram

Syntax

        fagan varlist [if exp] [in range] [, {options} *]


 
Description

    fagan creates a plot showing the relationship between the prior
        probability specified by user over the range 0-1, the likelihood
        ratio(combination of sensitivity and specificity), and posterior test
        probability.  fagan plots an axis on the left with the prior
        log-odds, an axis in the middle representing the log likelihood ratio
        and an axis on the right representing the posterior log-odds.  Lines
        are then drawn from the prior probability on the left through the
        likelihood ratios in the center and extended to the posterior
        probabilities on the right.

    fagan requires a varlist of likelihood ratios positive and negative
        respectively i.e two or more sets of likelihood ratios.

Options
    #prev is the prior or pretest probability of disease
    grpvar() subgroup variable name
    legendopts(#) specify options that affect the plot legend.
    ysize() and xsize() specify the height and width of plot respectively.


Remarks

    The clinical or patient-relevant utility of diagnostic test is evaluated
        using the likelihood ratios to calculate post-test probability based
        on Bayes' theorem as follows:
    Pretest Probability=Prevalence of target condition
    Post-test probability= likelihood ratio x pretest probability/[(1-pretest
        probability) x (1-likelihood ratio)]


    Assuming that the study samples are representative of the entire
        population, an estimate of the pretest probability of target
        condition is calculated from the global prevalence of this disorder
        across the studies.
    In this way, likelihood ratios are more clinically meaningful than
        sensitivities or specificities.  This approach would be useful for
        the clinicians who might use the likelihood ratios generated from
        here to calculate the post-test probabilities of nodal disease based
        on the prevalence rates of their own practice population.

    Thus, this approach permits individualization of diagnostic evidence.
        This concept is depicted visually with Fagan's nomograms. When Bayes
        theorem is expressed in terms of log-odds, the posterior log-odds are
        linear functions of the prior log-odds and the log likelihood ratios.

    The farther the likelihood ratio is from 1, the larger the change will be
        from the pretest to the posttest probability.
    One can group likelihood ratios into magnitudes:
    LR+ > 10 makes large and often conclusive increases in the likelihood of
        disease
    LR+ 5-10 makes moderate increases
    LR+ 2-5 makes small increases
    LR+ 1-2 makes insignificant increases
    LR- 0.5-1.0 makes insignificant decreases
    LR- 0.2-0.5 makes small decreases
    LR- 0.1-0.2 makes moderate decreases
    LR- < 0.1 makes large and often conclusive decreases in the likelihood of
        disease


Examples

. use fagan.dta, clear

. fagan lrp lrn, grpvar(test)

. fagan lrp lrn, grpvar(test) pr(0.5)

. fagan lrp lrn, grpvar(test) pr(0.5) scheme(s2color)

Author

    Ben A Dwamena, Division of Nuclear Medicine, Department of Radiology,
  University of Michigan Medical School, Ann Arbor, Michigan.  Email
  bdwamena@umich.edu


See Also
Related commands:

  fagani (if installed)