[R-meta] When should I use metaregression without interception?

[Martin Lobo]

I need help you !!!


Hi. I don't know when I should use the model without the intercept. In my data the significance changes

Mixed-Effects Model (k = 8; tau^2 estimator: DL)

tau^2 (estimated amount of residual heterogeneity):     1.0260 (SE = 3.1913)
tau (square root of estimated tau^2 value):             1.0129
I^2 (residual heterogeneity / unaccounted variability): 19.10%
H^2 (unaccounted variability / sampling variability):   1.24
R^2 (amount of heterogeneity accounted for):            0.00%

Test for Residual Heterogeneity:
QE(df = 6) = 7.4162, p-val = 0.2841

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0004, p-val = 0.9841

Model Results:

         estimate      se     zval    pval    ci.lb    ci.ub
intrcpt   -3.7436  1.4907  -2.5113  0.0120  -6.6653  -0.8218  *
xxx        0.0007  0.0354   0.0200  0.9841  -0.0687   0.0702

---
Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1

> metareg(MetaTAV, xxx, intercept = F)

Mixed-Effects Model (k = 8; tau^2 estimator: DL)

tau^2 (estimated amount of residual heterogeneity):     5.9922 (SE = 6.3402)
tau (square root of estimated tau^2 value):             2.4479
I^2 (residual heterogeneity / unaccounted variability): 62.90%
H^2 (unaccounted variability / sampling variability):   2.70

Test for Residual Heterogeneity:
QE(df = 7) = 18.8661, p-val = 0.0086

Model Results:

     estimate      se     zval    pval    ci.lb    ci.ub
xxx   -0.0917  0.0332  -2.7595  0.0058  -0.1568  -0.0266  **


another question.
If I want to make a meta-regression with the cholesterol variable and I have the difference in cholesterol in the two branches, should I put the two differences in the model? I don't know how to assemble the model in that case.
Best



Lorenzo Mart�n Lobo MTSAC, FACC, FESC
Especialista Jerarquizado en Cardiolog�a
Jefe de Cardiolog�a Hospital Militar Campo de Mayo
Ex Jefe de Unidad Coronaria Hospital Militar Campo de Mayo
Miembro Titular de la Sociedad Argentina de Cardiolog�a
Fellow American College of Cardiology
Fellow European Society of Cardiology
Miembro del Area de Investigaci�n de la SAC
Ex Director del Consejo de Aterosclerosis y Trombosis de la SAC
Miembro Asesor del Consejo de Aterosclerosis y Trombosis de la SAC
Ex Director del Consejo de Epidemiolog�a y Prevenci�n Cardiovascular de la SAC

Miembro Asesor del Consejo de Epidemiolog�a y Prevenci�n Cardiovascular de la SAC


Instructor de ACLS de la American Heart Association


________________________________
De: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Enviado: jueves, 7 de noviembre de 2019 11:30
Para: Martin Lobo <mlobo4370 using hotmail.com>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Asunto: RE: Bubble plot in regresion whith two variables

Hi Lorenzo,

So, if I understand you correctly, you want to show the line for one variable while holding the other variable constant. Here is the same example from the metafor website extended to this case:

########################################

library(metafor)

dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)

size <- 1 / sqrt(dat$vi)
size <- size / max(size)

plot(NA, NA, xlim=c(10,60), ylim=c(0.2,1.6),
     xlab="Absolute Latitude", ylab="Risk Ratio",
     las=1, bty="l", log="y")

symbols(dat$ablat, exp(dat$yi), circles=size, inches=FALSE, add=TRUE, bg="black")

preds <- predict(res, newmods=cbind(0:60, 1969), transf=exp)
lines(0:60, preds$pred)

abline(h=1, lty="dotted")

########################################

So, here, I plot the line for 'ablat' while holding year constant at 1969 (which is the median value of the year variable).

Best,
Wolfgang

-----Original Message-----
From: Martin Lobo [mailto:mlobo4370 using hotmail.com]
Sent: Thursday, 07 November, 2019 15:12
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
Subject: RE: Bubble plot in regresion whith two variables

Thank's Wolfgang.

The bubble function of the target package only graphs the first variable and does not allow adding the adjustment line of the multivariate model. I don't know if it explains well to me, I need to add the model adjustment line with two variables. The example you have given me has not been able to adapt it to work with my data.
Thank you

Lorenzo Mart�n Lobo MTSAC, FACC, FESC

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