*! version 1.4.6 15dec2022

Title

mar -- Meta-Analysis: OR,RR,RD,IR,ID,B,MD,R combined

Syntax

mar data stat [if] [in] [, options]

This command doesn't receive a list of variables or numbers; instead it receives two strings:

data Type of data matrix; valid values are:

raw Raw data

sst Summary statistics data

mix Mixed data

stat Risk Measure; valid values are:

or Odds Ratio

rr Relative Risk

ir Rate Ratio

rd Risk difference

id Rate difference

b Slope

md Mean difference

r Correlation coefficient

RAW data
tables print crosstab tables (only for raw data)

zero(correct) continuity correction for cells with zero events (only for raw data):

n None default is zero(n)

c Constant, add k=0.5 to each cell

p Proportional, add k1=n1/n and k0=n0/n

e Empirical, add k1= ORmh*n1/(n0 + ORmh*n1) and k0= n0/(n0 + ORmh*n1)

ORmh is the Mantel-Haenszel Odds Ratio, estimated with the studies without zero events

ALL data matrix
method(string) method of weighting of the combined statistic included in Influence
Analysis, Cumulative meta-analysis, bias and in some Plots:

fem IoV Fixed Effects Model default is method(fem)

rem IoV Random Effects Model

mh Mantel-Haenszel method (available for data type raw)

peto Peto's odds ratio method (available for data type raw)

The command computes weighted statistic for each method.

influ print Influence Analysis

cum(string) print Cumulative meta-analysis. Cumulative variable is needed:

date studies are accumulated according to the date daily variable order

varname studies are accumulated according to the varname variable order, any numeric variable
from the dataset. For raw data the command generates the following accumulated variables:

pres Proportion of response in Total sample (m1/n or m1/t)

pres_ue Proportion of response in Unexposed (a0/n0 or a0/t0)

pres_ex Proportion of response in Exposed (a1/n1 or a1/t1)

pexp Proportion of exposure in Total sample (n1/n or t1/t)

pexp_nc Proportion of exposure in Non Cases (b1/m0)

pexp_ca Proportion of exposure in Cases (a1/m1)

The most frequent order variables to accumulate are: date, pres (cohort studies) and pexp_nc (case-control studies).

Any other generated variable generated by the command may be used. For example, the standard error -se- on a
SST dataset with CI -lo,up- data.

By default the studies are accumulated in descending order, except for date or n cumulative variables, which use
ascending order. A minus sign - before the variable allows to reverse the cumulative order, for example: cum(-se).

date(string) print format for date variable (if available):

YMD print year-month-day: 2010-04-23 (default)

YM print year-month: 2010-04

Y print year: 2010

exp for slope (b) measures print exponential values, Exp(b)

keepusing do not exclude studies with missing values. By default, studies with missing data are excluded.

keep add variables to the current dataset with command results.
If not present, no variables are added.

level(#) confidence level(%) of CI weighted statistic; default is level(95)

nst(string) name of the study (label)

Mean Difference Standardization
cohen Cohen's d

hedges Hedges' adjusted g

glass Glass's Delta

Publication bias
begg print Begg publication bias

egger print Egger publication bias

macask(weight) print Macaskill publication bias. Weight variable is needed. The most frequent weight variables are:
pres (cohort studies) and pexp (case-control studies). Variable n is also needed.

nus(pcut) print Number of Unpublished Studies (Rosenthal & Gleser-Olkin methods). pcut is the level of significance

Graphics
forest(sort) Forest plot; forest(se) sorts in descending order by SE. It is possible to sort by any other variable

interval(sort) Interval plot; interval(se) sorts in descending order by SE. It is possible to sort by any other variable

funnel Funnel or Christmas tree plot for assessing bias

funnelh Funnel or Christmas tree plot for assessing bias (horizontal)

cumulative Cumulative meta-analysis (option cum needed)

abbe l'Abbé plot (raw data needed)

radial Radial plot

galbraith Radial plot for assessing heterogeneity

histogram Weighted Histogram; plots the weighted statistic

Description

Meta-Analysis: OR,RR,RD,IR,ID,B,MD,R combined.

The command expects fixed variable names for each type of data matrix. Fixed variable names must be lowercase.
Variable names are listed below for each type of data. Variables enclosed in Square Brackets [ ] are optional.
Variables enclosed in Braces { } indicate a choice between variables.

RAW data

study Study name (string; <= 11 characters)

[date] Study date (daily)

a1 Cases Exposed

{b1 | n1} NonCases or Total Exposed (if stat = or, rr or rd)

{i1 | t1} Incidence rate or Person-Time Exposed (if stat = ir or id)

a0 Cases UnExposed

{b0 | n0} NonCases or Total UnExposed (if stat = or, rr, rd)

{i0 | t0} Incidence rate or Person-Time UnExposed (if stat = ir, id)

RAW data - Mean difference

study Study name (string; <= 11 characters)

[date] Study date (daily)

m1 Mean response (intervention)

sd1 Standard deviation of the mean (intervention)

n1 Group size (intervention)

m0 Mean response (control)

sd0 Standard deviation of the mean (control)

n0 Group size (control)

SST (Summary Statistics) data

study Study name (string; <= 11 characters)

[date] Study date (daily)

{or Odds Ratio

| rr Relative Risk

| ir Rate Risk

| rd Risk Difference

| id Rate Difference

| b Slope

| md} Mean Difference

{lo up Lower & Upper bound of Risk measure CI

| se} Standard error of Risk measure: SE(lnOR); SE(lnRR); SE(lnIR); SE(RD); SE(ID); SE(b); SE(MD)

[cl] Summary Confidence Interval level(%) for se data; by default 95

[n] Study size (needed for some procedures and plots)

[pres] Proportion of response in Total sample (m1/n)

[pres_ue] Proportion of response in Unexposed (a0/n0)

[pexp] Proportion of exposure in Total sample (n1/n)

[pexp_nc] Proportion of exposure in Non Cases (b1/m0)

SST data - Correlation coefficient

study Study name (string; <= 11 characters)

[date] Study date (daily)

r Correlation Coefficient

n Study size

MIX (Mixed) data

For mixed type of data the dataset may contain raw and sst variables. The command automatically detects
which variables are present and computes new values as necessary. The results for this case are equivalent to the sst type. Although
there can be mixed types of data on the dataset, there can't be mixed types of data on the same observation: raw
and sst data may not appear on the same observation.

You can click here to pop up a dialog or type db mar.

Execute net from http://metodo.uab.cat/stata for install.

It is important to keep the commands updated. Execute adoupdate mar, update to update the mar command.

Execute adoupdate, update to update all the user-written commands.

If you find any bugs or want to suggest any improvements, please send an e-mail to: leam@uab.cat.

Examples

The zip file available for download on http://metodo.uab.cat/stata/Comando_mar_para_Stata.zip contains a pdf file with
examples of meta-analysis solved with the mar command (in spanish). The zip file also contains an excel file with example data. If
the direct download fails, copy the download link (http://metodo.uab.cat/stata/Comando_mar_para_Stata.zip) and paste in the
address bar of your web browser.

RAW data

. use http://metodo.uab.cat/stata/AngMio.dta

. mar raw or, forest(se) method(fem) keep nst("Enzima convertidora de la angiotensina e infarto de miocardio")

SST data

. use http://metodo.uab.cat/stata/CancerUtero.dta

. mar sst or, influ cum(date) method(rem) forest(se) funnel cumulative macask(pres) level(95) nst("Anticonceptivos orales y adenocarcinoma de cuello uterino")

Stored results

mar stores the following scalars in r():

Inverse of Variance, Fixed Effects Model

r(stat_fem) estimated statistic

r(se_fem) standard error

r(stat_fem_lo) confidence interval lower bound

r(stat_fem_up) confidence interval upper bound

r(w_fem) total weight sum

r(tau2) variance between studies

Inverse of Variance, Random Effects Model

r(stat_rem) estimated statistic

r(se_rem) standard error

r(stat_rem_lo) confidence interval lower bound

r(stat_rem_up) confidence interval upper bound

r(w_rem) total weight sum

Mantel-Haenszel

r(stat_mh) estimated statistic

r(se_mh) standard error

r(stat_mh_lo) confidence interval lower bound

r(stat_mh_up) confidence interval upper bound

r(w_mh) total weight sum

Peto

r(stat_peto) estimated statistic

r(se_peto) standard error

r(stat_peto_lo) confidence interval lower bound

r(stat_peto_up) confidence interval upper bound

r(w_peto) total weight sum

Mantel-Haenszel results are stored for raw data and or, rr, ir risk measure.

Peto results are stored for raw data and or risk measure.

Version

Version 1.4.6 -- 15 December 2022

Authors

JM.Doménech

Programmer: R.Sesma

Laboratori d'Estadística Aplicada

Universitat Autònoma de Barcelona

leam@uab.cat

Vancouver reference

Doménech JM. Meta-Analysis OR, RR, RD, IR, ID, B, MD & R Combined: User-written command mar for Stata [computer program].

V1.4.6. Bellaterra: Universitat Autònoma de Barcelona; 2022.

Available executing from Stata: net from http://metodo.uab.cat/stata

References

Delgado M. Revisión sistemática de estudios. Metaanálisis. 5ª ed. Barcelona: Signo; 2015.

Egger M, Smith GD, Altman D. Systematic reviews in health care. Meta-analysis in context. London: BMJ Books; 2001.

Palmer TM & Sterne JAC (Eds). Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition. College Station (Texas): Stata Press; 2016.

Sweeting MJ, Sutton AJ, Lambert PC. What to add to nothing? Use and avoidance of continuity corrections
in meta-analysis of sparse data. Stat Med. 2004; 23:1351-75.