{smcl} {* 18Nov2013}{...} {hline} {cmd:help metaprop} {hline} {title:Fixed and random effects meta-analysis of proportions} {p 8 12 2} {cmd:metaprop} {varlist} {ifin} {weight} [{cmd:,} {it:measure_and_model_options} {it:output_options} {it:forest_plot_options} ] {p 12 12 2} where {it:measure_and_model_options} may be {p 12 12 2} {cmd: ftt} {cmd:cimethod(}{it:string}{cmd:)} {cmd:fixed} {cmd:random} {cmd:cc(}{it:#}{cmd:)} {cmd:wgt(}{it:weightvar}{cmd:)} {cmd:second(}{it:model} or {it:estimates and description}{cmd:)} {cmd:first(}{it:estimates and description}{cmd:)} {p 12 12 2} and where {it:output_options} may be {p 12 12 2} {cmd:dp(#)} {cmd:power(#)} {cmd:by(}{it:byvar}{cmd:)} {cmd:nosubgroup} {cmd:sgweight} {cmdab:il:evel(}{it:#}{cmd:)} {cmdab:ol:evel(}{it:#}{cmd:)} {cmd:sortby(}{it:varlist}{cmd:)} {cmd:label(}{it:namevar yearvar}{cmd:)} {cmd:nokeep} {cmd:notable} {cmd:nograph} {cmd:nosecsub} {p 12 12 2} and where {it:forest_plot_options} may be {p 12 12 2} {cmd:legend(}{it:string}{cmd:)} {cmdab:xla:bel(}{it:#},...{cmd:)} {cmdab:xt:ick(}{it:#},...{cmd:)} {cmd:boxsca(}{it:#}{cmd:)} {cmd:nobox} {cmd:nooverall} {cmd:nowt} {cmd:nostats} {cmd:force} {p 12 12 2} {cmd:lcols(}{it:varlist}{cmd:)} {cmd:rcols(}{it:varlist}{cmd:)} {cmd:astext(}{it:#}{cmd:)} {cmd:double} {cmd:nohet} {cmd:summaryonly} {cmd:rfdist} {cmdab:rfl:evel(}{it:#}{cmd:)} {cmd:boxopt(}{it:}{cmd:)} {cmd:diamopt(}{it:}{cmd:)} {cmd:pointopt(}{it:}{cmd:)} {cmd:ciopt(}{it:}{cmd:)} {cmd:olineopt(}{it:}{cmd:)} {cmd:classic} {cmd:nowarning} {it:graph_options} {title:Description} {p 4 4 2} This routine provides procedures for pooling proportions in a meta-analysis of multiple studies study and/or displays the results in a forest plot. The confidence intervals are based on score(Wilson) (Newcombe, R. G. 1998) or exact binomial(Clopper-Pearson) (Newcombe, R. G. 1998) procedures. A test of whether the summary effect measure is equal to the zero is given, as well as a test for heterogeneity, i.e., whether the true effect in all studies is the same. Heterogeneity is also quantified using the I-squared measure (Higgins et al. 2003). {p 4 4 2} {cmd:metaprop} requires two variables in the format {n, N} such that p = n/N to be declared. {p 4 4 2} Note that the {cmd:metaprop} command requires Stata 10.1 or later versions. {title:Options for metaprop} {dlgtab:Specifying the measure and model} {p 4 8 2} {cmd:ftt} Calculate the pooled estimate after Freeman-Tukey Double Arcsine Transformation (Freeman, M. F. , and Tukey, J. W. 1950) to stabilize the variances. {p 4 8 2} {cmd:cimethod} Specifies the method to compute the confidence intervals for the individual studies. By default, the Score(Wilson) confidence intervals are computed. Available option is "exact". {p 4 8 2} {cmd:fixed} specifies a fixed effect model using the inverse variance method. {p 4 8 2} {cmd:random} specifies a random effects model using the method of DerSimonian and Laird, with the estimate of heterogeneity being taken from the inverse-variance fixed-effect model. {p 4 8 2} {cmd:cc(}{it:#}{cmd:)} defines a fixed continuity correction to add in the case where a study has zero success. By default, {cmd:metaprop} excludes studies with zero success. The {cmd:cc()} option allows the use of non-negative constants. This option is not necessary when the Freeman-Tukey Double Arcsine transformation is performed. {p 4 8 2} {cmd:wgt(}{it:weightvar}{cmd:)} specifies alternative weighting for any data type. The pooled effect size is to be computed by assigning a weight of {it:weightvar} to the studies. You should only use this option if you are satisfied that the weights are meaningful. {p 4 8 2} {cmd:second({it:model})} A second analysis may be performed using another method, using {cmd:fixed} or {cmd:random}. Note that if {cmd:by} is used then sub-estimates from the second method are not displayed with user defined estimates. {dlgtab:Output} {p 4 8 2} {cmd:power(#)} indicates the power of ten with which to multiply the estimates. # is any real value. The default is 0 which reports proportions, power(2) would report proportion*100=percentages. The x-axis labels (and dp if necessary) should be adjusted accordingly when power(#) is adjusted. {p 4 8 2} {cmd:dp(#)} indicates the number of decimal places to display in the table and graph. {p 4 8 2} {opt by(byvar)} specifies that the meta-analysis is to be stratified according to the variable declared. {p 4 8 2} {cmd:sgweight} specifies that the display is to present the percentage weights within each subgroup separately. By default {cmd:metaprop} presents weights as a percentage of the overall total. {p 4 8 2} {cmd:nosubgroup} indicates that within-group pooled results are to be ommitted when {cmd:by()} is used. By default {cmd:metaprop} presents both within group and overall pooled results. {p 4 8 2} {cmd:ilevel(}{it:#}{cmd:)} specifies the confidence interval level (e.g., 90, 95, 99 percent) for the individual study confidence intervals. The default is {cmd:$S_level}. {cmd:ilevel()} and {cmd:olevel()} need not be the same. See {helpb set level}. {p 4 8 2} {cmd:olevel(}{it:#}{cmd:)} specifies the confidence interval level (e.g., 90, 95, 99 percent) for the overall (pooled) study confidence intervals. The default is {cmd:$S_level}. {cmd:ilevel()} and {cmd:olevel()} need not be the same. See {helpb set level}. {p 4 8 2} {cmd:sortby(}{it:varlist}{cmd:)} sorts by variable(s) in {it:varlist}. {p 4 8 2} {cmd:label([namevar=}{it:namevar}{cmd:], [yearvar=}{it:yearvar}{cmd:])} labels the data by its name, year, or both. Either or both option/s may be left blank. For the table display, the overall length of the label is restricted to 20 characters. The {cmd:lcols()} option will override this if specified. {p 4 8 2} {cmd:nokeep} prevents the retention of study parameters in permanent variables (see saved results below). {p 4 8 2} {cmd:notable} prevents display of table of results. {p 4 8 2} {cmd:nograph} prevents display of graph. {p 4 8 2} {cmd:nosecsub} ({it:v9 update}) prevents the display of subestimates using the second method if {cmd:second()} is used. Note that this is invoked automatically with user-defined estimates. {dlgtab:Forest plot} {p 4 8 2} {cmd:nooverall} prevents display of overall effect size on graph (automatically enforces the {cmd:nowt} option). {p 4 8 2} {cmd:nowt} prevents display of study weight on the graph. {p 4 8 2} {cmd:nostats} prevents display of individual study statistics on graph. {p 4 8 2} {cmd:xlabel()} defines x-axis labels. No checks are made as to whether these points are sensible. So the user may define anything if the {cmd:force} option is used. Points must be comma separated. {p 4 8 2} {cmd:xtick()} adds tick marks to the x-axis. Points must be comma separated. {p 4 8 2} {cmd:force} forces the x-axis scale to be in the range specified by {cmd:xlabel()}. {p 4 8 2} {cmd:boxsca()} controls "weighted box" scaling. The default is 100 (as in a percentage) and may be increased or decreased as such (e.g., 80 or 120 for 20% smaller or larger respectively). {p 4 8 2} {cmd:nobox} prevents a "weighted box" being drawn for each study and markers for point estimates only are shown. {dlgtab:Further options for the forest plot} {p 4 8 2} {cmd:lcols(}{it:varlist}{cmd:)}, {cmd:rcols(}{it:varlist}{cmd:)} define columns of additional data to the left or right of the graph. The first two columns on the right are automatically set to effect size and weight, unless suppressed using the options {cmd:nostats} and {cmd:nowt}. {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 table output. {p 4 8 2} {cmd:astext(}{it:#}{cmd:)} specifies the percentage of the graph to be taken up by text. The default is 50 and the percentage must be in the range 10-90. {p 4 8 2} {cmd:double} allows variables specified in {cmd:lcols} and {cmd:rcols} to run over two lines in the plot. This may be of use if long strings are to be used. {p 4 8 2} {cmd:nohet} prevents display of heterogeneity statistics in the graph. {p 4 8 2} {cmd:summaryonly} shows only summary estimates in the graph (may be of use for multiple subgroup analyses). {p 4 8 2} {cmd:rfdist} displays the confidence interval of the approximate predictive distribution of a future study, based on the extent of heterogeneity in the random effects model. This incorporates uncertainty in the location and spread of the random effects distribution using the formula {cmd: t(df) x sqrt(se2 + tau2)} where t is the t-distribution with k-2 degrees of freedom, se2 is the squared standard error and tau2 the heterogeneity statistic. The CI is then displayed with lines extending from the diamond. Note that with <3 studies the distribution is inestimable and effectively infinite, thus displayed with dotted lines, and where heterogeneity is zero there is still a slight extension as the t-statistic is always greater than the corresponding normal deviate. For further information, see Higgins and Thompson (2001). {p 4 8 2} {cmd:rflevel(}{it:#}{cmd:)} specifies the level (e.g., 90, 95, 99 percent) of the confidence interval of the predictive distribution. The default is {cmd:$S_level}. See {helpb set level}. {p 4 8 2} {cmd:boxopt()}, {cmd:diamopt()}, {cmd:pointopt()}, {cmd:ciopt()}, {cmd:olineopt()} specify options for the graph routines within the program, allowing the user to alter the appearance of the graph. Any options associated with a particular graph command may be used, except some that would cause incorrect graph appearance. For example, diamonds are plotted using the {help twoway pcspike} command, so options for line styles are available (see {help line options}); however, altering the x-y orientation with the option {cmd:horizontal} or {cmd:vertical} is not allowed. So, {cmd:diamopt(lcolor(green) lwidth(thick))} feeds into a command such as {cmd:pcspike(y1 x1 y2 x2, lcolor(green) lwidth(thick))}. {p 8 8 2} {cmd:boxopt()} controls the boxes and uses options for a weighted marker (e.g., shape, colour, but not size). See {it:{help marker_options}}. {p 8 8 2} {cmd:diamopt()} controls the diamonds and uses options for pcspike (not horizontal/vertical). See {it:{help line_options}}. {p 8 8 2} {cmd:pointopt()} controls the point estimate using marker options. See {it:{help marker_options}} and {it:{help marker_label_options}}. {p 8 8 2} {cmd:ciopt()} controls the confidence intervals for studies using options for pcspike (not horizontal/vertical). See {it:{help line_options}}. {p 8 8 2} {cmd:olineopt()} controls the overall effect line with options for an additional line (not position). See {it:{help line_options}}. {p 4 8 2} {cmd:classic} specifies that solid black boxes without point estimate markers are used. {p 4 8 2} {cmd:nowarning} switches off the default display of a note warning that studies are weighted from random-effects anaylses. {p 4 8 2} {it:graph_options} specifies overall graph options that would appear at the end of a {cmd:twoway} graph command. This allows the addition of titles, subtitles, captions, etc., control of margins, plot regions, graph size, aspect ratio, and the use of schemes. See {it:{help twoway_options}}. {p 4 8 2} {cmd:wgt(}{it:weightvar}{cmd:)} specifies alternative weighting by the specified variable (default is inverse of the variance). {p 4 8 2} {cmd:texts(}{it:#}{cmd:)} increases or decreases the text size of the label by specifying {it:#} to be more or less than unity. The default is usually satisfactory but may need to be adjusted. {title:Stored} By default, {cmd:metaprop} adds the following new variables to the dataset: _ES Estimated proportiop/prevalence (ES). _seES Standard error of ES. _LCI Lower confidence limit for ES. _UCI Upper confidence limit for ES. _WT Study percentage weight. {title:Examples} {p 4 8 2} Pooling proportions from raw cell counts, grouped by triage group, with "label" specification, x-axis label set, ticks on x-axis added, suppressed weights, increased text size, changes on the box effect estimate, a red diamond for the confidence intervals of the pooled effect estimate, a vertical line at zero, a red dashed line, for the pooled effect estimate, e.t.c. {p 4 8 2} The dataset used in this example has been to produce figure one in Marc Arbyn et al. (2009). {p 8 12 2} {stata "use http://fmwww.bc.edu/repec/bocode/a/arbyn2009jcellmolmedfig1.dta":. use http://fmwww.bc.edu/repec/bocode/a/arbyn2009jcellmolmedfig1.dta} {p 8 12 2} {cmd:. metaprop num denom, random by(tgroup) cimethod(exact)} {p_end} {p 12 12 2} {cmd: label(namevar=author, yearvar=year)} {p_end} {p 12 12 2} {cmd: xlab(.25,0.5,.75,1)xline(0, lcolor(black)) } {p_end} {p 12 12 2} {cmd: subti("Atypical cervical cytology", size(4)) } {p_end} {p 12 12 2} {cmd: xtitle("Proportion",size(2)) nowt } {p_end} {p 12 12 2} {cmd: olineopt(lcolor(red)lpattern(shortdash)) } {p_end} {p 12 12 2} {cmd: plotregion(icolor(ltbluishgray)) } {p_end} {p 12 12 2} {cmd: diamopt(lcolor(red)) } {p_end} {p 12 12 2} {cmd: pointopt(msymbol(x)msize(0))boxopt(msymbol(S) mcolor(black)) } {p_end} {p 12 12 2} {cmd: astext(70) texts(150)} {p_end} {p 12 12 2} {it:({stata "metaprop_examples metaprop_example_one":click to run})} {p 4 8 2} Pooling proportions from raw cell counts with Freeman-Tukey double arcsine transformation and exact confidence intervals for the individual studies. {p 4 8 2} The dataset used in this example produced the top-left graph in figure one in Ioanna Tsoumpou et al. (2009). {p 8 12 2} {stata "use http://fmwww.bc.edu/repec/bocode/t/tsoumpou2009cancertreatrevfig2WNL.dta":. use http://fmwww.bc.edu/repec/bocode/t/tsoumpou2009cancertreatrevfig2WNL.dta} {p 8 12 2} {cmd:. metaprop p16p total, random ftt cimethod(exact)} {p_end} {p 12 12 2} {cmd: label(namevar=author, yearvar=year) sortby(year author)} {p_end} {p 12 12 2} {cmd: xlab(0.1,.2, 0.3,0.4,0.5,0.6,.7,0.8, 0.9, 1) xline(0, lcolor(black)) } {p_end} {p 12 12 2} {cmd: ti(Positivity of p16 immunostaining, size(4) color(blue)) } {p_end} {p 12 12 2} {cmd: subti("Cytology = WNL", size(4) color(blue)) } {p_end} {p 12 12 2} {cmd: xtitle(Proportion,size(3)) nowt nostats } {p_end} {p 12 12 2} {cmd: olineopt(lcolor(red) lpattern(shortdash)) } {p_end} {p 12 12 2} {cmd: diamopt(lcolor(black)) } {p_end} {p 12 12 2} {cmd: pointopt(msymbol(x)msize(0)) boxopt(msymbol(S) mcolor(black)) } {p_end} {p 12 12 2} {cmd: astext(70) texts(100)} {p_end} {p 12 12 2} {it:({stata "metaprop_examples metaprop_example_two":click to run})} {p 4 8 2} The dataset used in this example produced the top-left graph in figure one in Ioanna Tsoumpou et al. (2009). {p 8 12 2} {stata "use http://fmwww.bc.edu/repec/bocode/t/tsoumpou2009cancertreatrevfig2HSIL.dta":. use http://fmwww.bc.edu/repec/bocode/t/tsoumpou2009cancertreatrevfig2HSIL.dta.dta} {p 8 12 2} {cmd:. metaprop p16p total, random ftt cimethod(exact)} {p_end} {p 12 12 2} {cmd: label(namevar=author, yearvar=year) sortby(year author)} {p_end} {p 12 12 2} {cmd: xlab(0.1,.2, 0.3,0.4,0.5,0.6,.7,0.8, 0.9, 1) xline(0, lcolor(black)) } {p_end} {p 12 12 2} {cmd: ti(Positivity of p16 immunostaining, size(4) color(blue)) } {p_end} {p 12 12 2} {cmd: subti("Cytology = HSIL", size(4) color(blue)) } {p_end} {p 12 12 2} {cmd: xtitle(Proportion,size(3)) nowt nostats } {p_end} {p 12 12 2} {cmd: olineopt(lcolor(red) lpattern(shortdash)) } {p_end} {p 12 12 2} {cmd: diamopt(lcolor(black)) } {p_end} {p 12 12 2} {cmd: pointopt(msymbol(x)msize(0)) boxopt(msymbol(S) mcolor(black)) } {p_end} {p 12 12 2} {cmd: astext(70) texts(100)} {p_end} {p 12 12 2} {it:({stata "metaprop_examples metaprop_example_three":click to run})} {title:Authors} {p 4 4 2} Victoria Nyaga, Marc Arbyn. Unit of Cancer Epidemiology, Scientific Institute of Public Health, Juliette Wytsmanstreet 14, B1050 Brussels, Belgium. {p 4 4 2} Marc Aerts. Center for Statistics, Hasselt University, Agoralaan Building D, 3590 Diepenbeek, Belgium. {title:Acknowledgements} {p 4 4 2} Edited code from metan.ado by Michael J Bradburn, Jonathan J Deeks, Douglas G Altman. Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Linton Road, Oxford, OX2 6UD, UK {title:References} {phang} Higgins, J. P. T., S. G. Thompson, J. J. Deeks, and D. G. Altman. 2003. Measuring inconsistency in meta-analyses. {it:British Medical Journal} 327: 557-560. {phang} Higgins, J. P. T., and S. G. Thompson. 2001. Presenting random effects meta-analyses: Where we are going wrong? 9th International Cochrane Colloquium, Lyon, France. {phang} Miller, J. J. 1978. The inverse of the Freeman-Tukey double arcsine transformation. {it:The American Statistician} 32: 138. {phang} Freeman, M. F., and Tukey, J. W. 1950. Transformations related to the angular and the square root. {it: Annals of Mathematical Statistics} 21: 607-611. {phang} Newcombe, R. G. 1998. Two-sided confidence intervals for the single proportion: comparison of seven methods. {it:Statistics in Medicine} 17: 857-872. {phang} Arbyn, M., et al. 2009. Triage of women with equivocal or low-grade cervical cytology results. A meta-analysis of the HPV test positivity rate. {it:Journal for Cellular and Molecular Medicine} 13.4: 648-59. {phang} Tsoumpou, I., et al. 2009. p16INK4a immunostaining in cytological and histological specimens from the uterine cervix: a systematic review and meta-analysis. {it:Cancer Treatment Reviews} 35: 210-20. {title:Also see} {psee} Online: {help metan} (if installed), {help metannt}(if installed) {help metareg} (if installed), {help metabias} (if installed), {help metatrim} (if installed), {help metainf} (if installed), {help galbr} (if installed), {help metafunnel} (if installed)