`regtest.Rd`

The function can be used to carry out (various versions of) Egger's regression test for funnel plot asymmetry.

regtest(x, ...) # S3 method for rma regtest(x, model="rma", predictor="sei", ret.fit=FALSE, digits, ...) # S3 method for default regtest(x, vi, sei, ni, subset, model="rma", predictor="sei", ret.fit=FALSE, digits, ...)

x | an object of class |
---|---|

vi | vector with the corresponding sampling variances (needed if |

sei | vector with the corresponding standard errors (note: only one of the two, |

ni | vector with the corresponding sample sizes. |

subset | optional (logical or numeric) vector to specify the subset of studies that should be included in the test. Only relevant when passing a vector via |

model | either |

predictor | either |

ret.fit | logical to specify whether the full results from the fitted model should also be returned. |

digits | integer to specify the number of decimal places to which the printed results should be rounded (the default is to take the value from the object or is 4 for the default method). |

... | other arguments. |

Various tests for funnel plot asymmetry have been suggested in the literature, including the rank correlation test by Begg and Mazumdar (1994) and the regression test by Egger et al. (1997). Extensions, modifications, and further developments of the regression test are described (among others) by Macaskill, Walter, and Irwig (2001), Sterne and Egger (2005), Harbord, Egger, and Sterne (2006), Peters et al. (2006), Rücker et al. (2008), and Moreno et al. (2009). The various versions of the regression test differ in terms of the model (either a weighted regression model with a multiplicative dispersion term or a fixed/random/mixed-effects meta-regression model is used), in terms of the predictor variable that the observed effect sizes or outcomes are hypothesized to be related to when publication bias is present (suggested predictors include the standard error, the sampling variance, and the sample size or transformations thereof), and in terms of the outcome measure used (e.g., for \(2 \times 2\) table data, one has the choice between various outcome measures). The idea behind the various tests is the same though: If there is a relationship between the observed effect sizes or outcomes and the chosen predictor, then this usually implies asymmetry in the funnel plot, which in turn may be an indication of publication bias.

The `regtest`

function can be used to carry out various versions of the regression test. The model is chosen via the `model`

argument, with `model="lm"`

for weighted regression with a multiplicative dispersion term or `model="rma"`

for the meta-analytic models. In the latter case, arguments such as `method`

, `weighted`

, and `test`

used during the initial model fitting are also used for the regression test. Therefore, if one wants to conduct the regression test with a random/mixed-effects model, one should first fit a random-effects model with the `rma`

function and then use the `regtest`

function on the fitted model object.

The predictor is chosen via the `predictor`

argument:

`predictor="sei"`

for the standard error,`predictor="vi"`

for the sampling variance,`predictor="ni"`

for the sample size,`predictor="ninv"`

for the inverse of the sample size,`predictor="sqrtni"`

for the square root transformed sample size, and`predictor="sqrtninv"`

for the inverse of the square root transformed sample size.

For predictors based on the sample size, the object `x`

obviously must contain the information about the sample sizes. This will automatically be the case when `measure`

was *not* equal to `"GEN"`

or the `ni`

values were explicitly specified during the initial model fitting.

If the model passed to the `regtest`

function already included one or more moderators, then `regtest`

will add the chosen predictor to the moderator(s) already included in the model. This way, one can test for funnel plot asymmetry after accounting first for the influence of the moderator(s) already included.

One can also pass a vector with the observed effect sizes or outcomes (via `x`

) and the corresponding sampling variances via `vi`

(or the standard errors via `sei`

) directly to the function (in this case, the `regtest.default`

function is used). When the predictor is the sample size or a transformation thereof, then `ni`

needs to be specified here as well.

The outcome measure used for the regression test is simply determined by what measure was used in fitting the original model (or what values are passed to `regtest.default`

).

The model used for conducting the regression test can also be used to obtain a ‘limit estimate’ of the (average) true effect or outcome. In particular, when the standard error, sampling variance, or inverse (square root) sample size is used as the predictor, the model intercept in essence reflects the estimate under infinite precision. This is sometimes (cautiously) interpreted as an estimate of the (average) true effect or outcome that is adjusted for publication bias.

An object of class `"regtest"`

. The object is a list containing the following components:

the model used for the regression test.

the predictor used for the regression test.

the value of the test statistic.

the corresponding p-value

the degrees of freedom of the test statistic (if the test is based on a t-distribution).

the full results from the fitted model.

the limit estimate (only for predictors `"sei"`

`"vi"`

, `"ninv"`

, or `"sqrtninv"`

and when the model does not contain any additional moderators; `NULL`

otherwise)

lower bound of the confidence interval for the limit estimate.

upper bound of the confidence intervals for the limit estimate.

The classical ‘Egger test’ is obtained by setting `model="lm"`

and `predictor="sei"`

. For the random/mixed-effects version of the Egger test, one should first fit a random-effects model to the data and then set `model="rma"`

and `predictor="sei"`

when using the `regtest`

function. See Sterne and Egger (2005) for details on these two types of models/tests.

When conducting a classical ‘Egger test’, the test of the limit estimate is the same as the ‘precision-effect test’ (PET) of Stanley and Doucouliagos (2014). The limit estimate when using the sampling variance as predictor is sometimes called the ‘precision-effect estimate with SE’ (PEESE) (Stanley & Doucouliagos, 2014). A conditional procedure where we use the limit estimate when PET is not significant (i.e., when using the standard error as predictor) and the PEESE (i.e., when using the sampling variance as predictor) when PET is significant is sometimes called the PET-PEESE procedure (Stanley & Doucouliagos, 2014).

All of the tests do not directly test for publication bias, but for a relationship between the observed effect sizes or outcomes and the chosen predictor. If such a relationship is present, then this usually implies asymmetry in the funnel plot, which in turn may be an indication of publication bias. However, it is important to keep in mind that there can be other reasons besides publication bias that could lead to asymmetry in the funnel plot.

Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org

Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. *Biometrics*, **50**(4), 1088--1101. https://doi.org/10.2307/2533446

Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. *British Medical Journal*, **315**(7109), 629--634. https://doi.org/10.1136/bmj.315.7109.629

Harbord, R. M., Egger, M., & Sterne, J. A. C. (2006). A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. *Statistics in Medicine*, **25**(20), 3443--3457. https://doi.org/10.1002/sim.2380

Macaskill, P., Walter, S. D., & Irwig, L. (2001). A comparison of methods to detect publication bias in meta-analysis. *Statistics in Medicine*, **20**(4), 641--654. https://doi.org/10.1002/sim.698

Moreno, S. G., Sutton, A. J., Ades, A. E., Stanley, T. D., Abrams, K. R., Peters, J. L., & Cooper, N. J. (2009). Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. *BMC Medical Research Methodology*, **9**, 2. https://doi.org/10.1186/1471-2288-9-2

Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2006). Comparison of two methods to detect publication bias in meta-analysis. *Journal of the American Medical Association*, **295**(6), 676--680. https://doi.org/10.1001/jama.295.6.676

Rücker, G., Schwarzer, G., & Carpenter, J. (2008). Arcsine test for publication bias in meta-analyses with binary outcomes. *Statistics in Medicine*, **27**(5), 746--763. https://doi.org/10.1002/sim.2971

Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. *Research Synthesis Methods*, **5**(1), 60--78. https://doi.org/10.1002/jrsm.1095

Sterne, J. A. C., & Egger, M. (2005). Regression methods to detect publication and other bias in meta-analysis. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.) *Publication bias in meta-analysis: Prevention, assessment, and adjustments* (pp. 99--110). Chichester, England: Wiley.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. *Journal of Statistical Software*, **36**(3), 1--48. https://doi.org/10.18637/jss.v036.i03

### copy data into 'dat' and examine data dat <- dat.egger2001 ### calculate log odds ratios and corresponding sampling variances (but remove ISIS-4 trial) dat <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=-16) ### fit random-effects model res <- rma(yi, vi, data=dat) res#> #> Random-Effects Model (k = 15; tau^2 estimator: REML) #> #> tau^2 (estimated amount of total heterogeneity): 0.1911 (SE = 0.2106) #> tau (square root of estimated tau^2 value): 0.4371 #> I^2 (total heterogeneity / total variability): 38.19% #> H^2 (total variability / sampling variability): 1.62 #> #> Test for Heterogeneity: #> Q(df = 14) = 20.9184, p-val = 0.1037 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> -0.8745 0.2080 -4.2049 <.0001 -1.2822 -0.4669 *** #> #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>### classical Egger test regtest(res, model="lm")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: weighted regression with multiplicative dispersion #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: t = -3.1783, df = 13, p = 0.0073 #> Limit Estimate (as sei -> 0): b = -0.1512 (CI: -0.5130, 0.2106) #>### random/mixed-effects version of the Egger test regtest(res)#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: z = -2.8062, p = 0.0050 #> Limit Estimate (as sei -> 0): b = -0.1639 (CI: -0.5681, 0.2402) #>### same tests, but passing outcomes directly regtest(dat$yi, dat$vi, model="lm")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: weighted regression with multiplicative dispersion #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: t = -3.1783, df = 13, p = 0.0073 #> Limit Estimate (as sei -> 0): b = -0.1512 (CI: -0.5130, 0.2106) #>regtest(dat$yi, dat$vi)#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: z = -2.8062, p = 0.0050 #> Limit Estimate (as sei -> 0): b = -0.1639 (CI: -0.5681, 0.2402) #>### examples using the sample size (or a transformation thereof) as predictor regtest(res, model="lm", predictor="ni")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: weighted regression with multiplicative dispersion #> Predictor: sample size #> #> Test for Funnel Plot Asymmetry: t = 2.8955, df = 13, p = 0.0125 #>regtest(res, model="lm", predictor="ninv")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: weighted regression with multiplicative dispersion #> Predictor: inverse of the sample size #> #> Test for Funnel Plot Asymmetry: t = -2.3230, df = 13, p = 0.0370 #> Limit Estimate (as ni -> inf): b = -0.3731 (CI: -0.6872, -0.0591) #>regtest(res, model="rma", predictor="ni")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: sample size #> #> Test for Funnel Plot Asymmetry: z = 1.7938, p = 0.0728 #>regtest(res, model="rma", predictor="ninv")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: inverse of the sample size #> #> Test for Funnel Plot Asymmetry: z = -1.4313, p = 0.1524 #> Limit Estimate (as ni -> inf): b = -0.5636 (CI: -1.0712, -0.0559) #>### if dat$yi is computed with escalc(), sample size information is stored in attributes dat$yi#> [1] -0.83034830 -1.05605267 -1.27833981 -0.04348511 0.22314355 -2.40751999 #> [7] -1.28093385 -1.19170271 -0.69574796 -2.20827441 -2.03815988 -0.85015093 #> [13] -0.79323064 -0.29933987 -1.57078846 #> attr(,"ni") #> [1] 76 270 400 94 298 115 48 43 151 54 169 56 252 2316 215 #> attr(,"measure") #> [1] "OR"### then this will work regtest(dat$yi, dat$vi, predictor="ni")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: sample size #> #> Test for Funnel Plot Asymmetry: z = 1.7938, p = 0.0728 #>### otherwise have to supply sample sizes manually dat$ni <- with(dat, n1i + n2i) dat$yi <- c(dat$yi) # this removes the 'ni' attribute from 'yi' regtest(dat$yi, dat$vi, ni=dat$ni, predictor="ni")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: sample size #> #> Test for Funnel Plot Asymmetry: z = 1.7938, p = 0.0728 #>### regression test (by default the standard error is used as predictor) reg <- regtest(res) reg#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: z = -2.8062, p = 0.0050 #> Limit Estimate (as sei -> 0): b = -0.1639 (CI: -0.5681, 0.2402) #>### add regression line to funnel plot se <- seq(0,1.8,length=100) lines(coef(reg$fit)[1] + coef(reg$fit)[2]*se, se, lwd=2)### regression test (using the sampling variance as predictor) reg <- regtest(res, predictor="vi") ### add regression line to funnel plot (using the sampling variance as predictor) lines(coef(reg$fit)[1] + coef(reg$fit)[2]*se^2, se, lwd=2)### testing for asymmetry after accounting for the influence of a moderator res <- rma(yi, vi, mods = ~ year, data=dat) regtest(res, model="lm")#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: weighted regression with multiplicative dispersion #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: t = -3.1057, df = 12, p = 0.0091 #>regtest(res)#> #> Regression Test for Funnel Plot Asymmetry #> #> Model: mixed-effects meta-regression model #> Predictor: standard error #> #> Test for Funnel Plot Asymmetry: z = -2.3198, p = 0.0204 #>