{smcl}
{* *! version 1.0.0 4dec2017}{...}
{vieweralsosee "[ME] meqrlogit" "mansection ME meqrlogit"}{...}
{vieweralsosee "" "--"}{...}
{vieweralsosee "[R] binreg" "mansection R binreg"}{...}
{viewerjumpto "Syntax" "metadta##syntax"}{...}
{viewerjumpto "Description" "metadta##description"}{...}
{viewerjumpto "Options" "metadta##options"}{...}
{viewerjumpto "Remarks" "metadta##remarks"}{...}
{viewerjumpto "Examples" "metadta##examples"}{...}
{viewerjumpto "Stored results" "metadta##results"}{...}
{title:Title}
{p 4 4 2}
{opt metadta} {hline 2} Fixed-effects and random-effects meta-analysis, meta-regression and network meta-analysis
of diagnostic test accuracy studies using logistic and logistic-normal regression.
{marker syntax}{...}
{title:Syntax}
{p 4 4 2}
[{help by} {varlist}{cmd::}] {opt metadta tp fp fn tn [tp2 fp2 fn2 tn2 index comparator]} [{indepvars}] {ifin} {cmd:,}
{opt stu:dyid}({var})
[{it:{help metadta##options_table:options}} {it:{help metadta##foptions_table:foptions}}
{it:{help metadta##soptions_table:soptions}}]
{p 4 4 2}
{it:studyid} is a variable identifying each study.{p_end}
{p 4 6 2}{it:indepvars} should be {cmd:string} for categorical variables and/or {cmd:numeric} for continous variables.
{cmd:The variable names should not contain underscores}.{p_end}
{marker description}{...}
{title:Description}
{pstd}
{cmd:metadta} implements a generalized linear mixed model for the binomial family
with a logit link.
{pstd}
By default a random-effects model accounting for the correlation between logit sensitivity and logit specif is fitted. A fixed-effects model is automatically used when the correlation cannot be
computed due to insufficient data i.e when there are less than {cmd:3} or whenever it is plausible to assume that the studies are homogeneous.
{pstd}
Four different types of analysis can be performed.{p_end}
{pstd}
1. The {cmd:basic} analysis requires data from independent studies where each row contains data from seperate studies.{p_end}
{pstd}
2. {cmd:Comparative} analysis where each studies each study contributes two rows to the dataset; one for each test. The two tests are similar in all studies.{p_end}
{pstd}
3. {cmd:Arm-based network} meta-analysis where a study contributes more two rows to the dataset; one for each tests. Different tests can be used in different studies.{p_end}
{pstd}
4. {cmd:Contrast-based network} meta-analysis where each study contributes at least one row of dataset, with each row containing data for each comparison between an index/candidate and a comparator test(s).{p_end}
{pstd}
The model parameters are further processed and results presented in tables, forest plot and/or SROC plot. After a comparative meta-analysis or contrast-based network meta-analysis, the study-specific relative sensitivity and specificity can be tabulated and/or plotted.
{pstd}
When there are no covariates, heterogeneity is also quantified using the I-squared measure({help metapreg##ZD2014:Zhou and Dendukuri 2014}).
{synoptline}
{pstd}
{cmd: Note 1} We recommend going through our published tutorial. Find it here: {browse "https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00747-5"}
{pstd}
{cmd: Note 2} The Stata do file to reproduce the examples in the tutorial can be found here: {browse "https://github.com/VNyaga/Metadta/blob/master/NyagaArbyn2022ArchPubHealth.do"}
{marker options_table}{...}
{synoptset 30 tabbed}{...}
{synopthdr}
{synoptline}
{dlgtab:Modeling}
{synopt :{opt mod:el(random|fixed [, modelopts])}}the type of model to fit; default is {cmd:random}. {help metadta##optimization_options:modelopts}
control the control the optimization process{p_end}
{synopt :{opth cov:(metadta##vartype:bcov[, wcov])}} specifies the variance-covariance structure of
the random-effects; default {cmd:bcov} is {cmd: un}structured (and {cmd:wcov} is {cmd:ind}ependent; in {cmd:network} meta-analysis){p_end}
{synopt : {opth cv:effect(se|sp|sesp)}}specify which latent outcome is to be affected by
covariate information; default is {cmd:sesp}{p_end}
{synopt : {opth in:teraction(se|sp|sesp)}}specify for latent outcome to include the interaction terms; default is {cmd:sesp}{p_end}
{synopt :{opt cb:network}}notifies the program that the data is in the form {cmd: tp1 fp1 fn1 tn1 tp2 fp2 fn2 tn2 index comparator} to perform a contrast-based network meta-analysis{p_end}
{synopt :{opt comp:arative}}notifies the program that the studies are comparative (2 observations per study) {p_end}
{synopt :{opt ab:network}}instructs the program to perform arm-based network meta-analysis (expects atleast 2 observations per study) {p_end}
{synopt :{opth by:(varname:byvar)}}specificies the stratifying variable for which the margins are estimated {p_end}
{synopt :{opt prog:ress}}show the {cmd:progress} of the model estimation process{p_end}
{synopt :{opt A:lphasort}}sort the categorical variables alphabetically {p_end}
{synopt :{opt ref([label], [top|bottom])}}specifies the reference level in network and comparative analysis and its position in computing the probability ratios{p_end}
{synopt :{opt nomc}}do not perform {cmd:m}odel {cmd:c}omparison with likelihood-ratio tests comparison the specified model with other simpler models{p_end}
{dlgtab:General}
{synopt :{opt str:atify}}requests for a consolidated sub-analyses by the {opth by:(varname:byvar)} variable{p_end}
{synopt :{opth label:(varname:[namevar=varname], [yearvar=varname])}}specifies that date be labelled by its name and/or year{p_end}
{synopt :{opt noove:rall}}suppress the display of overall estimates in the itable and fplot{p_end}
{synopt :{opt nosubg:roup}}suppress the display of the group estimates in the itable and fplot{p_end}
{synopt :{opt summ:aryonly}}suppress the individual studies in the itable and fplot{p_end}
{synopt :{opth down:load(path)}}specify the location where a copy of data used to plot the forest plot should be stored {p_end}
{synopt :{opt dp:(#)}} set decimal points to display; default is {cmd: 3}{p_end}
{synopt :{opth l:evel(level)}}set confidence level; default is {cmd: level(95)}{p_end}
{synopt :{opt pow:er(#)}}set the exponentiating power; default is {cmd: 0}{p_end}
{synopt :{opth sort:by(varlist)}}sort the appearance of studies in the itable and fplot based on the variables in {help varlist}{p_end}
{synopt :{opth ci:method(metapreg##citype:citype)}}specifies how the confidence intervals
for the individuals study proportions are computed; default is {cmd:citype(exact)} for proportions.
For relative ratios, the score/Koopman CI are computed{p_end}
{dlgtab:Tables}
{synopt :{opt noit:able}}suppress display of the table containing the studies{p_end}
{synopt :{opt sum:table(logodds|abs|rr|all)}}which summary tables to present from the model estimates. Default is none of the tables. {p_end}
{synoptline}
{marker foptions_table}{...}
{synopthdr:foptions}
{synoptline}
{synopt :{opt nofp:lot }}suppress the forest plot(s){p_end}
{synopt :{opt xla:bel(list)}}defines x-axis labels. No checks are made as to whether these points are sensible.
The points in the list {cmd:must} be comma separated.{p_end}
{synopt :{opt xt:ick(list)}}adds the listed tick marks to the x-axis. The points in the list {cmd:must} be comma separated.{p_end}
{synopt :{opt plots:tat(label)}}specifies comma seperated labels(name) for proportions/relative ratios in the forest plot{p_end}
{synopt :{opth outp:lot(abs|rr)}}specifies to plot absolute or relative measures when studies are comparative; default is {cmd:outplot(abs)}{p_end}
{synopt :{opt tex:ts(#)}}increases or decreases the text size of the label. Default value is 1{p_end}
{synopt :{opth lc:ols(varlist)}}specifies additional columns to the left of the plot{p_end}
{synopt :{opt double:}}allows more white space in the fplot by running the text on the left i.e {cmd:lcols(varlist)} over two lines in the fplot{p_end}
{synopt :{opth diam:opt(scatter##connect_options:connect_options)}}controls the diamonds{p_end}
{synopt :{opth point:opt(scatter##marker_options:marker_options)}}controls the points for the study estimates{p_end}
{synopt :{opth arr:owopt(twoway_pcarrow:marker_options)}}controls the arrows for the truncated confidence intervals of the study estimates{p_end}
{synopt :{opth ol:ineopt(scatter##connect_options:connect_options)}}controls the overall and subgroup estimates line{p_end}
{synopt :{opt as:text(percent)}}percentage of the forest plot to be taken up by the text. Default is 50 {p_end}
{synopt :{opth cio:pt(scatter##connect_options:connect_options)}}controls the appearance of confidence intervals for studies{p_end}
{synopt :{opt subl:ine}}displays the line with group estimates{p_end}
{synopt :{opt noovl:ine}}suppress the overall line; by default the overall line is displayed{p_end}
{synopt :{opt nost:at}}suppress the text with estimates and confidence intervals; by default the text is displayed{p_end}
{synopt :{opt grid}}place grid lines between the studies; by default the grid lines are suppressed{p_end}
{synopt :{opt log:scale}}requests the plot to be in the (natural)log scale{p_end}
{synopt :{opth gra:phsave(filename)}}save the plot in/as {it:filename}{p_end}
{synopt :{help twoway_options}}specifies other overall graph options{p_end}
{synopt :{opth xline:(metadta##linearg:linearg)}} adds a vertical line at a specified {cmd: x} value{p_end}
{synoptline}
{marker soptions_table}{...}
{synopthdr:soptions}
{synoptline}
{synopt :{opt nosr:oc}}suppress the sroc plot {p_end}
{synopt :{opt nocur:ve}}suppress the sroc curve in the sroc plot {p_end}
{synopt :{opt col:orpalette(string)}}specify the colour for each class of the grouping categorical variable. {p_end}
{synopt :{opt nopred:iction}}suppress the prediction region{p_end}
{synopt :{opt b:ubbles}}show the study size as weight{p_end}
{synopt :{opt bub:bleid}}identify the bubbles by row index {p_end}
{synopt :{opth sp:ointopt(scatter##marker_options:marker_options)}}controls the appearance of study points{p_end}
{synopt :{opth op:ointopt(scatter##marker_options:marker_options)}}controls the appearance of overall summary point(s){p_end}
{synopt :{opth cu:rveopt(scatter##connect_options:connect_options)}}controls the appearance of curve(s){p_end}
{synopt :{opth ci:opt(line_options:line_options)}}controls the appearance of confidence line(s){p_end}
{synopt :{opth predci:opt(line_options:line_options)}}controls the appearance of prediction line{p_end}
{synopt :{opth bubo:pt(scatter##marker_options:marker_options)}}controls the appearance of bubbles{p_end}
{synopt :{opth bid:opt(scatter##marker_label_options:label_options)}}controls the appearance of labels for the bubbles{p_end}
{synopt :{opth gra:phsave(filename)}} save the plot in/as {it:filename}{p_end}
{synopt :{help twoway_options}} specifies other overall graph options e.g legend{p_end}
{marker optimization_options}{...}
{synoptline}
{synopthdr :Model options}
{synoptline}
{dlgtab: random-effects model}
{syntab:Integration}
{synopt :{opt intp:oints(# [# ...])}}set the number of
integration (quadrature) points; default is {cmd:intpoints(7)}{p_end}
{synopt :{opt lap:lace}}use Laplacian approximation; equivalent to
{cmd:intpoints(1)}{p_end}
{syntab :Maximization}
{synopt :{it:{help meqrlogit##maximize_options:maximize_options}}}control
the maximization process; seldom used{p_end}
{synopt :{opt retol:erance(#)}}tolerance for random-effects estimates; default
is {cmd:retolerance(1e-8)}; seldom used{p_end}
{synopt :{opt reiter:ate(#)}}maximum number of iterations for random-effects
estimation; default is {cmd:reiterate(50)}; seldom used{p_end}
{synopt :{opt matsqrt}}parameterize variance components using matrix square
roots; the default{p_end}
{synopt :{opt matlog}}parameterize variance components using matrix logarithms
{p_end}
{synopt :{opth refine:opts(meqrlogit##maximize_options:maximize_options)}}control
the maximization process during refinement of starting values
{p_end}
{dlgtab: fixed-effects model}
{synopt :{it:{help binreg##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end}
{synopt :{opt fisher(#)}}Fisher scoring steps{p_end}
{synopt :{opt search}}search for good starting values{p_end}
{synoptline}
{marker vartype}{...}
{synopthdr :bcov}
{synoptline}
{synopt :{opt ind:ependent}}different variance parameters for logit sensitivity and logit specificity random-effects, covariance is 0; the default{p_end}
{synopt :{opt exc:hangeable}}equal variances for logit sensitivity and logit specificity random-effects,
and a different covariance parameter{p_end}
{synopt :{opt id:entity}}equal variances for logit sensitivity and logit specificity random-effects,
covariance is 0{p_end}
{synopt :{opt un:structured}}all variances logit sensitivity and logit specificity random-effects and covariances to be distinctly
estimated{p_end}
{synoptline}
{marker vartype}{...}
{synopthdr :wcov}
{synoptline}
{synopt :{opt ind:ependent}}different variance parameters for logit sensitivity and logit specificity random-effects in the nested factor, the default{p_end}
{synopt :{opt id:entity}}equal variances for logit sensitivity and logit specificity random-effects in the nested factor {p_end}
{synopt :{opt zero}}zero variance for logit sensitivity and logit specificity in the nested factor i.e assume homogeneity {p_end}
{synoptline}
{marker citype}{...}
{synopthdr :citype}
{synoptline}
{synopt :{opt exact}}calculate exact confidence intervals; the default{p_end}
{synopt :{opt wald}}calculate Wald confidence intervals{p_end}
{synopt :{opt wilson}}calculate Wilson confidence intervals{p_end}
{synopt :{opt agres:ti}}calculate Agresti-Coull confidence intervals{p_end}
{synopt :{opt jeff:reys}}calculate Jeffreys confidence intervals{p_end}
{p2colreset}{...}
{synoptline}
{p2colreset}{...}
{marker remarks}{...}
{title:Remarks}
{pstd}
{helpb meqrlogit} is used for the random-effects model and {helpb binreg} for the fixed-effects model.
The binomial distribution is used to model the within-study variability ({help metapreg##Hamza2008:Hamza et al. 2008}).
Studies with less variability have more influence in the pooled estimate since they contribute more to the likelihhod function. The
weighting is implicit and parameter estimation is an iterative procedure. Therefore, even though the forest plot never displays
weights for the individual studies, weighting is indeed done.
{marker options}{...}
{title:Options}
{dlgtab:Model}
{phang}
{opt mod:el(modeltype [, modelopts])} specifies the type of model to fit. {it: modeltype} can be either {cmd:fixed} or {cmd:random}.
{it: modelopts} are options controlling the maximisation (both for the fixed- and random-effects model),
and integration (only for the random-effects model) process.{p_end}
{phang2}
{opt mod:el(fixed [, modelopts])} fits a fixed-effects logistic regression model using {helpb binreg}.
{it: modelopts}:
{opt fisher(#)},
{opt search},
{opth tech:nique(maximize##algorithm_spec:algorithm_spec)},
{opt dif:ficult},
{opt iter:ate(#)},
{opt tol:erance(#)},
{opt ltol:erance(#)},
{opt nrtol:erance(#)},
{opt nonrtol:erance}, and
{opt from(init_specs)}; see {manhelp maximize R}. These options are seldom used.
{pmore2}
Setting {cmd:technique()} set to something other than BHHH, changes the {it:vcetype} to {cmd:vce(oim)}.
Specifying {cmd:technique(bhhh)} changes {it:vcetype} to {cmd:vce(opg)}.
{phang2}
{opt fisher(#)} specifies the number of Newton-Raphson steps that should use
the Fisher scoring Hessian or expected information matrix (EIM) before
switching to the observed information matrix (OIM). This option is useful only for Newton-Raphson
optimization.
{phang2}
{opt search} specifies that the command search for good starting values. This
option is useful only for Newton-Raphson optimization.
{phang2}
{opt mod:el(random [, integrate_options maximize_options])} fits a mixed-effects logistic regression model using {helpb meqrlogit}.
The model accounts for the between study heterogeneity and the intrisinc correlation between sensitivity and specificity.
{pmore2}
{it:integrate_options}: {help meqrlogit##laplace:laplace}, {opt intpoints(#)}
{phang3}
{opt intpoints(#)} sets the number of integration points for adaptive
Gaussian quadrature. The more integration points, the more accurate the
approximation to the log likelihood. However, computation time increases with
the number of quadrature points. {cmd:intpoints(7)} is the default.
{phang3}
{help meqrlogit##laplace:laplace} specifies that log likelihoods be calculated using
the Laplacian approximation, equivalent to adaptive Gaussian quadrature with
one integration point; {cmd:laplace} is equivalent to {cmd:intpoints(1)}. The computational time
saved by using {cmd:laplace} is a function of the number of quadrature points raised to a power 2 (the
dimension of the random-effects specification).
{pmore3}
The Laplacian approximation has been known to produce biased parameter
estimates, and the bias tends to be more prominent in the estimates of the
variance components rather than in the estimates of the fixed effects.
{phang2}
{help meqrlogit##maximize_options:maximize_options}:
{opt dif:ficult},
{opth tech:nique(maximize##algorithm_spec:algorithm_spec)},
{opt iter:ate(#)},
{opt tol:erance(#)},
{opt ltol:erance(#)},
{opt nrtol:erance(#)},
{opt nonrtol:erance}, and
{opt from(init_specs)};
see {helpb maximize:[R] maximize}. These options are seldom used.
{pmore}
Examples, {cmd: model(random, intpoint(9))} to increase the integration points,
{cmd: model(random, technique(bfgs))} to specify Stata's BFGS maximiztion algorithm.
{phang}
{opt cov([bcov][, wvoc])} specifies the structure of the covariance matrix for the random-effects.
{it:bcov} controls the stucture of the random effects on the first level of hierarchy and is one of the following:
{cmd:independent}, {cmd:exchangeable}, {cmd:identity}, or {cmd:unstructured}.
{pmore}
{cmd:covariance(independent)} covariance structure allows a distinct
variance for each of the two random-effects and assumes the covariance is 0.
The independent covariance matrix has 2 unique parameters.
{phang2}
{cmd:covariance(exchangeable)} structure specifies one common variance for two
random effects and 'non-zero' covariance. The exchangeable covariance matrix has 2 unique parameters.
{phang2}
{cmd:covariance(identity)} is all variances are equal and the covariance is 0.
The identity covariance matrix has 1 parameter.
{phang2}
{cmd:covariance(unstructured)} allows for all variances and covariances to be
distinct. The unstructured covariance matrix has 3 unique parameters. The default is {cmd:covariance(unstructured)}.
{phang2}
{it:wvoc} controls the stucture of the random effects on the second level of hierarchy and is one of the following: {cmd:independent}, {cmd:identity}, or {cmd:zero}.
{phang}
{cmd: cveffect(effect)} specify which latent outcome is to be affected by
covariate information. {it:effect} can be one of the following: {cmd:sesp}, {cmd:se}, {cmd:sp}.
The default is {cmd:sesp}{p_end}
{phang2}
{cmd: cveffect(sesp)} specifies that the covariate be included in the equations for the logit sensitivity and logit specificity.
{cmd: cveffect(sesp)} is the default.
{phang2}
{cmd: cveffect(se)} specifies that the covariate be included only in the equations for the logit sensitivity.
{phang2}
{cmd: cveffect(sp)} specifies that the covariate be included only in the equations for the logit specificity.
{phang}
{cmd: interaction(effect)} specifies on which latent outcome the interactions between the {cmd:main} variable of interest and
other confounding variables should be included. {it:effect} can be one of the following: {cmd:sesp}, {cmd:se}, {cmd:sp}.
The default is {cmd:sesp}. This requires at-least two independent variables.
{phang2}
{cmd: interaction(sesp)} specifies that interactions be included in the equations for the logit sensitivity and logit specificity.
{cmd: interaction(sesp)} is the default.
{phang2}
{cmd: interaction(se)} specifies that interactions be included in the equation for the logit sensitivity.
{phang2}
{cmd: interaction(sp)} specifies that interactions be included in the equationfor the logit specificity.
{phang}
{cmd: comparative} indicate that for each study there are two observations per study.
The variable identifying the pairs should be a {cmd: string} variable and should be the {cmd:fifth} variable after
{cmd: tp tn fp fn}.
{phang}
{cmd:cbnetwork} instructs the program to fit a model for contrast-based network meta-analysis. The expected data is the format {cmd: tp1 fp1 fn1 tn1 tp2 fp2 fn2 tn2 index comparator [covariates]}.
There can be more than one row of observation for each study with data from each seperate comparison.
{phang}
{cmd: abnetwork} instructs the program to fit a model for contrast-based network meta-analysis.
The variable identifying the different tests should be a {cmd: string} variable and should be the {cmd:fifth} variable after
{cmd: tp tn fp fn}.
{phang}
{cmd: progress} specifies whether the noisily or quitely display the model maximization process as is being executed.
By defualt the maximization process is executed quitely. This options is useful is stata is taking too long and you want
to see if its actually busy with the maximisation process. On the down-side, there is alot of unnecessary ouput.
{phang}
{cmd: nomc} indicates not perform {cmd:m}odel {cmd:c}omparison with likelihood-ratio tests comparison the specified model with other simpler models.
By default, simpler models are also fitted to the data and model comparison done using a likelihood ratio tests. For a model with interactions,
the simpler models have one less interaction term in each of the two equations. For a model without interactions, the simpler model leaves one
covariate effect in each of the two equations. A null model, i.e without covariates, is also compared with the specified model.
By default, model comparison is perfomed. By imposing {cmd: nomc}, substantial time can be saved
especially for complex models involving many covariates and interactions.
{phang}
{cmd: alphasort} sort the categorical variables alphabetically. By default, are encoded such that the value labels are sorted according to the
rank order as they appear in the data. The option {cmd:alphasort} sorts the categorical variables in the model alphabetical so that the class that appears
first alphabetically is assigned the base level.
{phang}
{cmd:by(byvar)} specifies that the summary etsimates be stratified/grouped according to the variable declared. This is useful in meta-regression with more than one covariate,
and the {cmd:byvar} is not one of the covariates or when there are interactions and the first covariate is not an ideal grouping variable. By default, results are grouped according to the
levels of the first categorical variable in the regression equation.
{pmore}
This option is not the same as the Stata {help by} prefix which repeates the analysis for each group of observation for which the values of the prefixed variable are the same.
{dlgtab:Tables}
{phang}
{cmd: sumtable(none|logodds|abs|rr|all)}} specifies which summary margins tables to present from the logistic regression model estimates.
{it:parameter} can be one of the following:
{cmd: logodds}, {cmd: abs}, {cmd: rr}, {cmd: all} or a list eg {cmd: logodds abs rr}. By default none of the tables is displayed.
The estimates are obtained using {help margins} which calculates predictions from a fitted model at fixed values of some covariates
and averaging or otherwise integrating over the remaining covariates. The tables present the parameter estimates, their standard-errors,
the z-statistic, and a confidence interval.
{phang2}
{cmd: sumtable(logodds)}} requests the marginal {cmd:log-odds} for each class of the categorical variable, and the log-odds
at the mean of continous varibles. When there are interaction, the log-odds are computed for the each combination of the
variable of interest and the confounding factor.
{phang2}
{cmd: sumtable(abs)}} requests the marginal {cmd:proportions} i.e {absolute} estimates for each class of the categorical variable, and the log-odds
at the mean of continous varibles. When there are interaction, the proportions are computed for the each combination of the
variable of interest and the confounding factor. The standard-errors, Z-statistic and p-value are in the log-odds scale.
{phang2}
{cmd: sumtable(rr)}} requests the marginal {cmd:r}elative {cmd:r}atio estimates. When studies are not comparative, the ratio are computed using the first class in
the categorical variable as the base. With data from comparative studies, the ratio comparing the two classes in the variable of interest is presented for each class of the
confounding variables. The standard-errors, Z-statistic and p-value are in the log scale. {cmd: sumtable(rr)} requires at-least one covariate.
{phang}
{cmd: noitable} suppress display of the table containing the studies and the summary estimates. By default, the table presented.
{dlgtab:General}
{phang}
{cmd: ref([label][,position])} specifies the reference category and its position when computing the summary probability ratios in comparative
and network meta-analysis. The first category in the {cmd:fifth} variable is used as the reference by default.
{phang}
{cmd: stratify} requests for a consolidated sub-analyses by the {opth by:(varname:byvar)} variable. The results are
presented in one table and one forest plot
{phang}
{cmd: label(varname[namevar=varname], [yearvar=varname])} specifies that date be labelled by its name and/or year.
Either or both option/s may be left blank.
{phang}
{cmd: nooverall} suppress the display of overall estimates in the itable and fplot. By default, the overall summary estimate is presented.
{phang}
{cmd: nosubgroup} suppress the display of the group estimates for each level of the grouping variable in the itable and fplot.
By default, the group estimates are displayed. The group absolute estimates are displayed with one categorical variable.
With data from comparative studies, two categorical variables where one is the variable of interest and the other a confounding variable, it is possible to
present the group relative ratios with the options {cmd:comparative} and {cmd: outplot(rr)}. The group estimates are not presented when there are more
than one (for {cmd: outplot(abs)}) or two(for {cmd: outplot(rr)}) independent variables.
{phang}
{cmd: summaryonly} suppress the individual studies in the itable and fplot. This options is usefull when you want to present the group and overall estimates only,
or when the number of studies in each of the groups are too many to avoid over-crowding the itable and/or forest plot.
{phang}
{cmd: download(filepath)} specify the location where a copy of data used to plot the forest plot should be stored. The is useful if you need to fit other models to the
exact data used by {cmd:metadta}, or when you want to generate other plots with the generated data.
The saved dataset is in the long format and contains the following variables:
{pmore}
_ES {space 10}Estimate proportion/relative ratio{p_end}
{pmore}
_LCI {space 9}The lower confidence limit for _ES {p_end}
{pmore}
_UCI {space 9}The upper confidence limit for _ES{p_end}
{pmore}
_ESAMPLE {space 5}Indicator variable if the study was included ({cmd:1}) or excluded({cmd:0}) in the model.{p_end}
{pmore}
_USE {space 9}Indicator variable for the role of an observation in the dataset: {cmd:-2} = labels, {cmd:0} = blanks, {cmd:1} = studies, {cmd:2} = group data, & {cmd:3} = overall data.{p_end}
{pmore}
_LABEL {space 7}Labels for each observations {p_end}
{pmore}
_ID {space 10}A numeric observations ID equivalence of _LABEL{p_end}
{pmore}
_PARAMETER {space 3}Indicator variable to identify data on the sensitivity({cmd:1}) and specificity({cmd:0})
{p_end}
{phang}
{opt dp:(#)} sets the decimal points to display in the summary tables, the itable and the forestplot; default is {cmd: 3}. {cmd:#} is an any sensible positive integer.
{phang}
{cmd: level(level)} sets the confidence level. The default is {cmd: level(95)}. The set level is used in all computations for the confidence intervals,
regions, prediction regions, and other computations that require a specification of the significance level. Other typical values are {cmd:level(90)} and {cmd:level(99)}.
{phang}
{cmd: power(#)} sets the exponentiating power with base {cmd:10}; default is {cmd: 0}. Any integer is allowed.
When specified, the estimates in the summary tables, itable and the forest plot are multiplied by 10 raised to power {cmd:#}.
The x-axis labels of the forest plot should be adjusted accordingly when power(#) is adjusted. This option is useful to present percentages instead of proportions or
when the estimates are very small and still want to display a reasonable number of decimal places.
{phang}
{cmd: sortby(varlist)} sort the appearance of studies in the itable and fplot based on the variables in {help varlist}. By default, the studies in the itable
and the forest plot are displayed as they appear in the data editor. If there group summaries are requested, data is sorted by group but within the group, the order
is maintained. The groups are sorted by their rank index in the data, i.e, the groups are sorted according to their appearance in the data.
{phang}
{cmd: cimethod(citype)} specifies how the binomial confidence intervals
for the individuals study proportions are computed. {it:citype} can be any of the following:
{opt exact}, {opt wald}, {opt wilson}, {opt agresti}, and {opt jeffreys}.
{pmore}
{cmd:cimethod(exact)} is the default and specifies exact (also known in the literature
as Clopper-Pearson [{help metadta##CP1934:1934}]) binomial confidence intervals.
{pmore}
{cmd:cimethod(wald)} specifies calculation of Wald confidence intervals.
{pmore}
{cmd:cimethod(wilson)} specifies calculation of Wilson confidence intervals.
{pmore}
{cmd:cimethod(agresti)} specifies calculation of Agresti-Coull confidence intervals.
{pmore}
{cmd:cimethod(jeffreys)} specifies calculation of Jeffreys confidence intervals.
{pmore}
See {help metadta##BCD2001:Brown, Cai, and DasGupta (2001)} for a discussion and
comparison of the different binomial confidence intervals.
{pmore}
For relative ratios, the score/Koopman[{help metadta##KOOPMAN1984:1984}] CI are computed{p_end}
{dlgtab:foptions}
{phang}
{cmd: nofplot} suppress the forest plot(s). By default, a forest plot is presented.
{phang}
{opt logscale} requests the plot to be in the (natural)log scale{p_end}
{phang}
{opt xla:bel(list)} defines x-axis labels. No checks are made as to whether these points are sensible. The points in the list {cmd:must} be comma separated.{p_end}
{phang}
{opt xt:ick(list)} adds the listed tick marks to the x-axis. The points in the list {cmd:must} be comma separated.{p_end}
{phang}
{opt plots:tat(label)} specifies comma-seperated labels(name) for proportions/relative ratios in the forest plot. E.g plotstat(Sensitivity, Specificity){p_end}
{phang}
{cmd: outplot(abs|rr)} specifies to plot absolute or relative measures when data is {cmd:comparative}; default is {cmd:outplot(abs)}.
{pmore}
{cmd: outplot(abs)} specifies to plot the absolute measures, i.e the sensitivity and specificity.
{pmore}
{cmd:outplot(rr)} specifies to plot the relative measures.
{phang}
{opt tex:ts(#)}increases or decreases the text size of the label. Default value is 1{p_end}
{phang}
{cmd: lcols(varlist)}specifies additional columns to the left of the plot. {cmd: texts(#)} can be used to fine-tune the size of the text in order to
achieve a satisfactory appearance. The columns are labelled with the variable label, or the variable name if
this is not defined. The first variable specified in {cmd:lcols()} is assumed to be the study identifier and this is used in the itable output.
{phang}
{opt double:} allows more white space in the fplot by running the text on the left i.e {cmd:lcols(varlist)} over two lines in the fplot. This might be useful to make
the forest plot look less crowded.
{phang}
{opt diamopt(options)} controls the appearance of the diamonds.
See {help scatter##connect_options:connect_options} for the relevant options. e.g {cmd: diamopt(lcolor(red))}
displays {cmd:red} (a) red diamond(s).
{phang}
{opt pointopt(options)} controls the points for the study estimates.
See {help scatter##marker_options:marker_options} for the relevant options. e.g {cmd: pointopt(msymbol(x) msize(0))}
{phang}
{opt arrowopt(options)} controls the arrows for the truncated confidence intervals of the study estimates.
See {help twoway_pcarrow:marker_options} for the relevant options. e.g {cmd: arrowopt(barbsize(small) msize(1))}
{phang}
{opt ciopt(options)} ontrols the appearance of confidence intervals for studies.
See {help scatter##connect_options:connect_options} for the relevant options.
{phang}
{opt olineopt(options)} controls the overall and subgroup estimates line.
See {help scatter##connect_options:connect_options} for the relevant options.
{phang}
{opt astext(percent)} percentage of the forest plot to be taken up by the text. Default is 50. The percentage must be in the range 10-90.
{phang}
{cmd: subline} displays the line with group estimates. By default, the only the diamonds for the groups is displayed but not the lines.
{phang}
{cmd: noovline} suppress the overall line; by default the overall line is displayed.
{phang}
{opt nost:at} suppress the text with estimates and confidence intervals; by default the text is displayed{p_end}
{phang}
{opt grid} show horizontal grid lines on the forestplot; by default no grids are displayed. With many studies, adding grid lines help with readabilitity{p_end}
{phang}
{opt graphsave(filename)} specifies the location where the .gph graph will be saved. The graph is saved asis. If more than 1 graphs are generated, 'flpot_#' is appended to the name.{p_end}
{phang}
{help twoway_options} specifies other overall graph to control how the look of the plot. This allows the addition of titles, subtitles, captions,
etc., control of margins, plot regions, graph size, aspect ratio, and the use of schemes. eg
{cmd: graphregion(color(white))}
{phang}
{marker linearg}{...}
{opt xline(linearg)} adds a vertical line where {cmd:linearg} is {cmd: x [,options]}. The options to specify the look of the line include
{cmd:lstyle(}{it:{help linestyle}}{cmd:)},
{cmd:lpattern(}{it:{help linepatternstyle}}{cmd:)},
{cmd:lwidth(}{it:{help linewidthstyle}}{cmd:)},
and {cmd:lcolor(}{it:{help colorstyle}}{cmd:)}
; see {manhelp line G-2:graph twoway line}.
{dlgtab:soptions}
{phang}
{cmd: nosroc} suppress the sroc/cross plot. By default an sroc/cross plot is presented. For the model with no covariates, more than one
covariates, or only continous variables, the sroc plots presents a center, an sroc curve, and the corresponding confidence and prediction regions for the
overall sensitivity and specificity. A scatter of the studies in the meta-analysis is also plotted.
When there is one categorical variable, the center, sroc curve, confidence and prediction region, and the studies in each level
are plotted each in a distinct colour. When a fixed-effects model is fitted, the sroc curve, and the confidence and prediction intervals are not presented.
Instead, a cross is plotted spanning the confidence intervals of sensitivity and specificity.
{phang}
{cmd: colorpalette(string)} specify the colour for each class of the grouping categorical variable. The default color palette has the following
colours: {cmd: black forest_green cranberry blue sienna orange emerald magenta dknavy gray purple}. Each class of the grouping
variable is assigned a colour from the palette.
{phang}
{cmd: nocurve} suppress the sroc curve. By default, the curvn is displayed whenever the
a random-effects model is fitted.
{phang}
{cmd: noprediction} suppress the prediction region. By default, the prediction region is displayed whenever the
a random-effects model is fitted.
{phang}
{cmd: bubbles} show the study size as weight. By default, only a scatter points are displayed to indicate the studies. This option
might be useful if interested in seeing the the study sizes since the bubble are proportional to the studies sizes.
{phang}
{cmd: bubbleid} identify the bubbles by row index. When the bubbles are plotted, one might be interested in identifying some studies. Each
bubble is identified by a number which is the row number of the submitted data.
{phang}
{opt spointopt(marker_options)} See {help scatter##marker_options:marker_options} controls the appearance of study points{p_end}
{phang}
{opt opointopt(marker_options)} See {help scatter##marker_options:marker_options} controls the appearance of overall summary point(s){p_end}
{phang}
{opt curveopt(connect_options)} See {help scatter##connect_options:connect_options} controls the appearance of curve(s){p_end}
{phang}
{opt ciopt(line_options)} See {help line_options:line_options} controls the appearance of confidence line(s){p_end}
{phang}
{opt predciopt(line_options)} See {help line_options:line_options} controls the appearance of prediction line{p_end}
{phang}
{opt bubopt(marker_options)} See {help scatter##marker_options:marker_options} controls the appearance of bubbles{p_end}
{phang}
{opt bidopt(label_options)} See {help scatter##marker_label_options:label_options} controls the appearance of labels for the bubbles{p_end}
{phang}
{opt graphsave(filename)} specifies the location where the .gph graph will be saved. The graph is saved asis. If more than 1 graphs are generated, 'flpot_#' is appended to the name.{p_end}
{phang}
{help twoway_options} specifies the graph options for the SROC plot giving control of axis, scales, legend, titles, subtitles, captions,
margins, plot regions, graph size, aspect ratio, etc. e.g
{cmd: graphregion(color(white))}. To change the order of the legend, the elements in the SROC plot are drawn in the following order; summary points, confidence cross,
sroc curve, confidence region, prediction region, observed data, the study bubbles and finaly the identifiers for the bubbles.
{marker examples}{...}
{title:Examples}
{marker example_one}{...}
{pstd}
{cmd :1. Intercept-only random-effects model}
{pmore}
The intercept-only model is the classical model for meta-anlysis to pool data. The data is obtained from
a systematical review of sensitivity and specificity of cytology and other markers including telomerase for
primary diagnosis of bladder cancer {help metadta##Glass200:Glas et al (2003)}.
{pmore}
The look of the sroc and forest plot is enhanced via {cmd:soptions(...)} and {cmd:foptions(...)}.
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/t/telomerase.dta""':. use "http://fmwww.bc.edu/repec/bocode/t/telomerase.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn, ///}
{p_end}
{pmore3}
{cmd:studyid(study) model(random) dp(2) sumtable(all) ///}
{p_end}
{pmore3}
{cmd:soptions(xtitle("False positive rate") xlabel(0(0.2)1) xscale(range(0 1)) ///}
{p_end}
{pmore3}
{cmd:ytitle("Sensitivity") yscale(range(0 1)) ylabel(0(0.2)1, nogrid) ///}
{p_end}
{pmore3}
{cmd:graphregion(color(white)) plotregion(margin(medium)) xsize(15) ysize(15)) ///}
{p_end}
{pmore3}
{cmd:foptions(graphregion(color(white)) texts(3) xlabel(0, 0.5, 1) ///}
{p_end}
{pmore3}
{cmd:diamopt(color(red)) pointopt(msymbol(s)msize(1)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_one":click to run})}
{marker example_two_one}{...}
{pstd}
{cmd :2.1 Metaregression - Comparative studies - 1 Covariate}
{pmore}
This example demonstrates how the intercept only model is extended in a meta-regression setting.
The data is from a Cochrane review on the accuracy of human papillomavirus testing and repeat cytology to
triage of women with an equivocal Pap smear to diagnose cervical precancer performed by {help metadta##Arbyn2013:Arbyn et al(2013)}.
{pmore}
The option {cmd:comparative} indicates that the data is in pairs and automatically the first covariate serves as the pair identifying variable.
With one categorical variable, different sroc curves each for every category is also displayed. The look of the sroc and forest plot is enhanced
via {cmd:soptions(...)} and {cmd:foptions(...)}.
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/a/ascus.dta""':. use "http://fmwww.bc.edu/repec/bocode/a/ascus.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn test, ///}
{p_end}
{pmore3}
{cmd:studyid(studyid) model(random) comparative sumtable(all) ///}
{p_end}
{pmore3}
{cmd:soptions(xtitle("False positive rate") xlabel(0(0.2)1) xscale(range(0 1)) ///}
{p_end}
{pmore3}
{cmd:ytitle("Sensitivity") yscale(range(0 1)) ylabel(0(0.2)1, nogrid) ///}
{p_end}
{pmore3}
{cmd:legend(order(1 "Repeat Cytology" 2 "HC2") ring(0) bplacement(6)) ///}
{p_end}
{pmore3}
{cmd:graphregion(color(white)) plotregion(margin(zero)) col(red blue)) ///}
{p_end}
{pmore3}
{cmd:foptions(graphregion(color(white)) outplot(abs) texts(2) xlabel(0, 1, 1) ///}
{p_end}
{pmore3}
{cmd:diamopt(color(red)) pointopt(msymbol(s)msize(1)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_two_one":click to run})}
{marker example_two_two}{...}
{pstd}
{cmd :2.2 Metaregression - Comparative studies - 1 Covariate - RR}
{pmore}
This example extends {help metadta##example_two_one:Example 2.1} by presenting the relative summary measures instead of the absolute measures.
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/a/ascus.dta""':. use "http://fmwww.bc.edu/repec/bocode/a/ascus.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn test, ///}
{p_end}
{pmore3}
{cmd:studyid(studyid) model(random) comparative sumtable(all) ///}
{p_end}
{pmore3}
{cmd:foptions(graphregion(color(white)) outplot(rr) texts(2) xlabel(0, 1, 1) ///}
{p_end}
{pmore3}
{cmd:diamopt(color(red)) pointopt(msymbol(s)msize(1)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_two_two":click to run})}
{marker example_three}{...}
{pstd}
{cmd :3. Metaregression - Comparative studies - 2 Covariates}
{pmore}
This example demonstrates how to examine (dis-)similarity of relative sensitivity and specificity across the different clinical setup.
Using sample of data from a meta-analysis on accuracy of human papillomavirus testing on self-collected versus
clinician-samples {help metadta##Arbyn2014:Arbyn et al(2014)}, we test whether the relative sensitivity and relative specificity between self and
clinician sample are similar across the clinical settings.
{pmore}
From simple analysis via tables, it was suspected that the sensitivity and specificity was different by the clinical setting and sample collection.
We therefore include an interaction between {cmd:sample} and {cmd:Setting} with the option {cmd:interaction(sesp)}. {cmd:sesp} implies that the interaction
terms are introduced in both the logit {cmd:se}nsitivity and logit {cmd:sp}ecificity equations. Model comparison with simpler models that do exclude the
interaction terms happens automatically.
{pmore}
With the option {cmd:sumtable(rr)} only the summary relative ratios are presented. The option {cmd:noitable} also suppresses
the table with individual studies.
{pmore}
The look of the sroc and forest plot is enhanced via {cmd:soptions(...)} and {cmd:foptions(...)}. With more than one covariate or when the focus is on RR,
the sroc plot is not presented.
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/c/clinself.dta""':. use "http://fmwww.bc.edu/repec/bocode/c/clinself.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn sample Setting, ///}
{p_end}
{pmore3}
{cmd:studyid(study) interaction(sesp) model(random) ///}
{p_end}
{pmore3}
{cmd:summaryonly comparative sumtable(rr) noitable ///}
{p_end}
{pmore3}
{cmd:soptions(xtitle("False positive rate") xlabel(0(0.2)1) xscale(range(0 1)) ///}
{p_end}
{pmore3}
{cmd:ytitle("Sensitivity") yscale(range(0 1)) ylabel(0(0.2)1, nogrid) ///}
{p_end}
{pmore3}
{cmd:graphregion(color(white)) plotregion(margin(zero))) ///}
{p_end}
{pmore3}
{cmd:foptions(diamopt(color(red)) olineopt(color(red) lpattern(dash)) ///}
{p_end}
{pmore3}
{cmd:outplot(RR) graphregion(color(white)) texts(2) xlabel(0.7, 1, 1.2))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_three":click to run})}
{marker example_four}{...}
{pstd}
{cmd :4. Metaregression - Comparative - 3 Covariates}
{pmore}
We extend {help metadta##example_three:Example 3} above and examine differences in relative sensitivity and specificity by test assay while
accounting for the clinical setup. The look of the sroc and forest plot is enhanced via {cmd:soptions(...)} and {cmd:foptions(...)}.
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/c/clinself.dta""':. use "http://fmwww.bc.edu/repec/bocode/c/clinself.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn sample Setting TA, ///}
{p_end}
{pmore3}
{cmd:studyid(study) interaction(sesp) model(random) cov(unstructured) ///}
{p_end}
{pmore3}
{cmd:comparative noitable sumtable(rr) ///}
{p_end}
{pmore3}
{cmd:soptions(xtitle("False positive rate") xlabel(0(0.2)1) xscale(range(0 1)) ///}
{p_end}
{pmore3}
{cmd:ytitle("Sensitivity") yscale(range(0 1)) ylabel(0(0.2)1, nogrid) ///}
{p_end}
{pmore3}
{cmd:graphregion(color(white)) plotregion(margin(zero))) ///}
{p_end}
{pmore3}
{cmd:foptions(outplot(rr) grid graphregion(color(white)) texts(1.5) xlabel(0.7, 1, 1.3) ///}
{p_end}
{pmore3}
{cmd:arrowopt(msize(1)) diamopt(color(red)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_four":click to run})}
{pstd}
{cmd :5. Metaregression - contrast-based network meta-analysis - RR}
{pmore}
Each row in the dataset is of the form {cmd: tp1 fp1 fn1 tn1 tp2 fp2 fn2 tn2 index comparator}. The data should be a from a 2x2 table as displayed below;
{p 22}
{c |} Disease Status
{p_end}
{pmore2}
index{space 4} {c |} Positive{space 5}Negative {c |}
{p_end}
{pmore2}
{c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -}
{p_end}
{p 10}
Positive{space 3} {c |} {space 2} tp1 {space 7} fp1 {space 2 } {c |}
{p_end}
{p 10}
Negative{space 3} {c |} {space 2} fn1 {space 7} tn1 {space 2} {c |}
{p_end}
{pmore2}
{c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -}
{p_end}
{p 22}
{c |} Disease Status
{p_end}
{pmore2}
comparator{c |} Positive{space 5}Negative {c |}
{p_end}
{pmore2}
{c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -}
{p_end}
{p 10}
Positive{space 3} {c |} {space 2} tp2 {space 7} fp2 {space 2 } {c |}
{p_end}
{p 10}
Negative{space 3} {c |} {space 2} fn2 {space 7} tn2 {space 2} {c |}
{p_end}
{pmore2}
{c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -} {c -}
{p_end}
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/p/pairedta.dta""':. use "http://fmwww.bc.edu/repec/bocode/p/pairedta.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp1 fp1 fn1 tn1 tp2 fp2 fn2 tn2 hpv1 hpv2, ///}
{p_end}
{pmore3}
{cmd:studyid(study) model(random) cov(, zero) ///}
{p_end}
{pmore3}
{cmd:cbnetwork sumtable(rr) ///}
{p_end}
{pmore3}
{cmd:foptions(outplot(rr) grid graphregion(color(white)) texts(1.85) ///}
{p_end}
{pmore3}
{cmd:xlabel(0.75, 0.90, 1, 1.11, 1.33) logscale lcols(hpv2 setting) astext(70) ///}
{p_end}
{pmore3}
{cmd:arrowopt(msize(1)) pointopt(msymbol(s)msize(1)) diamopt(color(red)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_five":click to run})}
{pstd}
{cmd :6. Metaregression - arm-based network meta-analysis - RR}
{pmore2}
{stata `"use "http://fmwww.bc.edu/repec/bocode/n/network.dta""':. use "http://fmwww.bc.edu/repec/bocode/n/network.dta"}
{p_end}
{pmore2}
{cmd:. metadta tp fp fn tn test , ///}
{p_end}
{pmore3}
{cmd:studyid(study) model(random, laplace) ///}
{p_end}
{pmore3}
{cmd:abnetwork ref(HC2) sumtable(all) ///}
{p_end}
{pmore3}
{cmd:foptions(outplot(rr) graphregion(color(white)) texts(1.75) ///}
{p_end}
{pmore3}
{cmd:xlabel(0.80, 0.90, 1, 1.11, 2) logscale astext(70) ///}
{p_end}
{pmore3}
{cmd:arrowopt(msize(1)) pointopt(msymbol(s)msize(.5)) ///}
{p_end}
{pmore3}
{cmd:diamopt(color(red)) olineopt(color(red) lpattern(dash)))}
{p_end}
{pmore2}
{it:({stata "metadta_examples example_six":click to run})}
{marker results}{...}
{title:Stored results}
{pstd}
{cmd:metadta} stores the following in {cmd:e()}:
{synoptset 24 tabbed}{...}
{p2col 5 15 19 2: Scalars}{p_end}
{synopt:{cmd:e(cmd)}} {cmd:metadta}{p_end}
{synopt:{cmd:e(cmdline)}} command as typed{p_end}
{synopt:{cmd:e(i_sq)}} estimated I squared{p_end}
{synopt:{cmd:e(p_chi2)}} p-value for LR test comparing the fixed and random effects models{p_end}
{synopt:{cmd:e(chi2)}} chisq. for LR test comparing the fixed and random effects models{p_end}
{synopt:{cmd:e(df)}} degrees of freedom{p_end}
{synopt:{cmd:e(sp_df_het)}} degrees of freedom for the LR test on significance of the covariates on specificity{p_end}
{synopt:{cmd:e(sp_p_het)}} p-value for the LR test on significance of the covariates on specificity{p_end}
{synopt:{cmd:e(sp_het)}} chi square statistic for the LR test on significance of the covariates on specificity{p_end}
{synopt:{cmd:e(se_df_het)}} degrees of freedom for the LR test on significance of the covariates on sensitivity{p_end}
{synopt:{cmd:e(se_p_het)}} p-value for the LR test on sigficance of the covariates on sensitivity{p_end}
{synopt:{cmd:e(se_het)}} chi square statistic for the LR test on significance of the covariates on sensitivity{p_end}
{synopt:{cmd:e(sp_p_z)}} p-value for testing if specificity = 0.5 or rel. specificity = 1 {p_end}
{synopt:{cmd:e(sp_z)}} z-statistic for testing if specificity = 0.5 or rel. specificity = 1 {p_end}
{synopt:{cmd:e(sp_ci_upp)}} upper confidence limit of the overall specificity estimate{p_end}
{synopt:{cmd:e(sp_ci_low)}} lower confidence limit of the overall specificity estimate{p_end}
{synopt:{cmd:e(sp_seES)}} standard error of the overall specificity estimate{p_end}
{synopt:{cmd:e(sp_ES)}} overall specificity estimate (either a proportion or ratio){p_end}
{synopt:{cmd:e(se_p_z)}} p-value for testing if sensitivity = 0.5 or rel. sensitivity = 1{p_end}
{synopt:{cmd:e(se_z)}} z-statistic for testing if sensitivity = 0.5 or rel. sensitivity = 1 {p_end}
{synopt:{cmd:e(se_ci_upp)}} upper confidence limit of the overall sensitivity estimate{p_end}
{synopt:{cmd:e(se_ci_low)}} lower confidence limit of the overall sensitivity estimate{p_end}
{synopt:{cmd:e(se_seES)}} standard error of the overall sensitivity estimate{p_end}
{synopt:{cmd:e(se_ES)}} overall sensitivity estimate (either a proportion or ratio){p_end}
{synoptset 24 tabbed}{...}
{p2col 5 15 19 2: Matrices}{p_end}
{synopt:{cmd:e(sprrout)}} summary relative specificity ratios {p_end}
{synopt:{cmd:e(serrout)}} summary relative sensitivity ratios {p_end}
{synopt:{cmd:e(rrout)}} summary relative ratios {p_end}
{synopt:{cmd:e(absoutsp)}} summary specificity proportions{p_end}
{synopt:{cmd:e(absoutse)}} summary sensitivity proportions{p_end}
{synopt:{cmd:e(absout)}} summary proportions{p_end}
{synopt:{cmd:e(logodds)}} summary log-odds{p_end}
{synopt:{cmd:e(vcovar)}} variance-covariance matrix of logit sensitivity and specificity.{p_end}
{p2colreset}{...}
{title:Technical notes}
{cmd: by}
{pstd}
When prefix {cmd:by} is used, only the results from the last group or the first model will be stored respectively.
{cmd: Post-estimation}
{pstd}
{cmd:metadta} also stores (in memory) the estimation results from the specified model in {cmd:metadta_modest}. These results can be
saved, restored and stored and used in making comparisons. See {help estimates}.
{title:Author}
{pmore}
Victoria N. Nyaga ({it:Victoria.NyawiraNyaga@sciensano.be}) {p_end}
{pmore}
Unit Cance Epidemiology - Belgian Cancer Center, {p_end}
{pmore}
Sciensano,{p_end}
{pmore}
Juliette Wytsmanstraat 14, {p_end}
{pmore}
1050 Brussels, {p_end}
{pmore}
Belgium.{p_end}
{pmore}
Marc Arbyn ({it:Marc.Arbyn@sciensano.be}) {p_end}
{pmore}
Belgian Cancer Center/Unit of Cancer Epidemiology, {p_end}
{pmore}
Sciensano,{p_end}
{pmore}
Juliette Wytsmanstraat 14, {p_end}
{pmore}
B1050 Brussels, {p_end}
{pmore}
Belgium.{p_end}
{title:References}
{marker Hamza2008}{...}
{phang}
Hamza et al. 2008. The binomial distribution of meta-analysis was preferred to model within-study variability.
{it:Journal of Clinical Epidemiology} 61: 41-51.
{marker BCD2001}{...}
{phang}
Brown, L. D., T. T. Cai, and A. DasGupta. 2001.
Interval estimation for a binomial proportion.
{it:Statistical Science} 16: 101-133.
{marker CP1934}{...}
{phang}
Clopper, C. J., and E. S. Pearson. 1934. The
use of confidence or fiducial limits illustrated in the case of the binomial.
{it:Biometrika} 26: 404-413.
{marker KOOPMAN1984}{...}
{phang}
Koopman, P. A. R. 1984. Confidence intervals for the ratio of two binomial proportions.
{it:Biometrics} 40(2): 513-517.
{marker Arbyn2013}{...}
{phang}
Arbyn M., Roelens J., Simoens C., Buntinx F., Paraskevaidis E., Martin-Hirsch PPL., Prendiville W. 2013.
Human Papillomavirus Testing Versus Repeat Cytology for Triage of Minor Cytological Cervical Lesions.
{it:Cochrane Database of Systematic Reviews}: 31-201.
{marker Arbyn2014}{...}
{phang}
Arbyn, M., Verdoodt, F., Snijders, P.J., Verhoef, V.M., Suonio, E., Dillner, L., Minozzi, S., Bellisario, C., Banzi, R., Zhao, F.H. and Hillemanns, P., 2014.
Accuracy of human papillomavirus testing on self-collected versus clinician-collected samples: a meta-analysis.
{it:The lancet oncology} 15(2): 172-183.
{p_end}