help midas binssealso see: midas

Title

midas binsse -- Regression-based tests for small-study effects and publication bias in diagnostic accuracy meta-analysis

Syntax

midas binsse
tp fp fn tn
[if exp]
[in range]
,
method(code)
[options]

Required
method(code) regression test; see Method codes below

Output
funnel funnel plot (y-axis: precision measure; x-axis: effect)

regplot regression scatter plot (axes transposed from funnel)

graph funnel plot with fitted regression line overlaid

Inference
level(#) confidence level; default level(95)

zcf(#) continuity correction for zero cells; default zcf(0.5)

Styling
pointopts(scatter_options) marker options for study points

regopts(line_options) line options for the fitted regression line

scheme(schemename) graph scheme

graph_options any additional twoway options

Description

midas binsse applies regression-based asymmetry tests to detect small-study
effects and publication bias in a set of diagnostic accuracy studies. The four 2×2
cell count variables must be supplied in the order tp fp fn tn.

All tests regress a transformation of the log diagnostic odds ratio (or a related
effect measure) on a measure of study size or precision. A statistically significant
non-zero intercept indicates asymmetry consistent with small-study effects. The
specific transformation and precision measure differ by method().

A continuity correction of 0.5 (zcf(0.5)) is applied to all cells of any study
that has at least one zero cell count, before computing the effect measure.

Method codes

method(d) Deeks: weighted regression of log DOR on 1/sqrt(ESS),
where ESS = 4n1n2/(n1+n2) is the effective sample size. The recommended
test for diagnostic odds ratio-based meta-analyses.

method(e) Egger: regression of (log OR)/(SE) on 1/SE (standardised
effect on precision). Adapted from the original Egger test for therapeutic
meta-analysis.

method(h) Harbord: modified Egger test using efficient score Z and
Fisher information V; regresses Z/sqrt(V) on sqrt(V). Reduces confounding
between the asymmetry statistic and the effect measure in diagnostic studies.

method(m) Macaskill: regression of log OR on total sample size n,
weighted by 1/(1/n1 + 1/n2). Analogous to the Macaskill test in therapeutic
meta-analysis.

method(p) Peters: regression of log OR on 1/n (inverse sample size),
weighted by 1/(1/n1 + 1/n2). A sample-size-based variant of Macaskill.

method(r) Schwarzer: regression of the arcsine difference
[arcsin(sqrt(Se)) − arcsin(sqrt(1−Sp))] on its standard error
sqrt(1/(4n1) + 1/(4n2)). Uses a variance-stabilising transformation and
is independent of the underlying prevalence.

method(s) Sterne: regression of log OR on SE(log OR). A
log-odds-ratio analogue of the standard Egger test; weight = 1/Var(log OR).

method(t) Stanley: meta-significance test; regresses log|t| on
log(n) where t = log OR / SE. A positive intercept suggests genuine empirical
effect; a negative intercept suggests small-study bias dominates.

Options

funnel draws a funnel plot with the effect measure on the x-axis and the
precision measure on the y-axis, without the regression line.

regplot draws the regression scatter plot with axes transposed relative to
the funnel (effect measure on y, precision on x) and the fitted line overlaid.

graph draws the funnel plot with the fitted regression line overlaid.

level(#) sets the confidence level for regression inference.
Default is level(95).

zcf(#) continuity correction added to all four cells of studies
with at least one zero. Default is zcf(0.5).

pointopts(scatter_options) controls the appearance of study
scatter points, e.g. pointopts(mcolor(navy) msymbol(circle)).

regopts(line_options) controls the appearance of the fitted
regression line, e.g. regopts(lcolor(maroon) lwidth(medium)).

scheme(schemename) sets the graph scheme.

Examples

Deeks funnel + regression plot:

. midas binsse tp fp fn tn, method(d) graph

Harbord test with custom point styling:

. midas binsse tp fp fn tn, method(h) graph pointopts(mcolor(navy)) regopts(lcolor(maroon))

Schwarzer arcsine test, funnel only:

. midas binsse tp fp fn tn, method(r) funnel

Peters test, 90% level:

. midas binsse tp fp fn tn, method(p) graph level(90)

References

Deeks test (method d):

Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias
and other sample size effects in systematic reviews of diagnostic test accuracy
was assessed. Journal of Clinical Epidemiology 2005;58:882–893.
https://doi.org/10.1016/j.jclinepi.2005.01.016

Egger test (method e):

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected
by a simple, graphical test. BMJ 1997;315:629–634.
https://doi.org/10.1136/bmj.315.7109.629

Harbord test (method h):

Harbord RM, Egger M, Sterne JAC. A modified test for small-study effects in
meta-analyses of controlled trials with binary endpoints.
Statistics in Medicine 2006;25:3443–3457.
https://doi.org/10.1002/sim.2380

Macaskill test (method m):

Macaskill P, Walter SD, Irwig L. A comparison of methods to detect publication
bias in meta-analysis. Statistics in Medicine 2001;20:641–654.
https://doi.org/10.1002/sim.698

Peters test (method p):

Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Comparison of two methods
to detect publication bias in meta-analysis. JAMA 2006;295:676–680.
https://doi.org/10.1001/jama.295.6.676

Schwarzer test (method r):

Schwarzer G, Antes G, Schumacher M. Inflation of type I error rate in two
statistical tests for the detection of publication bias in meta-analyses with
binary outcomes. Statistics in Medicine 2002;21:2465–2477.
https://doi.org/10.1002/sim.1235

Rücker G, Schwarzer G, Carpenter J, Schumacher M. Undue reliance on I² in
assessing heterogeneity may mislead. BMC Medical Research Methodology
2008;8:79.
https://doi.org/10.1186/1471-2288-8-79

Sterne test (method s):

Sterne JAC, Egger M. Funnel plots for detecting bias in meta-analysis:
guidelines on choice of axis. Journal of Clinical Epidemiology
2001;54:1046–1055.
https://doi.org/10.1016/S0895-4356(01)00377-8

Stanley meta-significance test (method t):

Stanley TD. Wheat from chaff: meta-analysis as quantitative literature review.
Journal of Economic Perspectives 2001;15:131–150.
https://doi.org/10.1257/jep.15.3.131

Also see

midas, midas pubbias