# OpenMX status1: 5 and SEs of estimates?

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Joined: 04/03/2021 - 19:00
OpenMX status1: 5 and SEs of estimates?

Hi there. I'm using metaSEM::meta() to conduct some single-level meta-analyses.

In some subset analyses, as k gets smaller, OpenMX seems to struggle to estimate the variance, estimating Tau2 as low as I will set RE.lbound. I can resolve the error message by running a fixed-effects model by setting RE.constraint = 0. However, when I do this, the standard error for the intercept increases by 33%. This changes the statistical significance of the intercept.

Although I don't have a vested interest in the statistical significance of the intercept, I would like to make a defensible decision whether to constrain Tau2 to zero or not. Is the SE of the intercept still valid given Status1: 5, or should I ignore those numbers and proceed directly to interpreting the fixed-effects model?

Thanks in advance! Reproducible example below.

library(metaSEM)

dat <- data.frame(yi = c(0.143581358989816, 0.0454437737469923, 0.508973319897135,
-0.140707411290326, 0.13348282996416, 0.169176989775847,
0.329109861753571),
vi = c(0.0570362059695423, 0.0428930499852486, 0.0904348502317336,
0.0565782183215856, 0.0607967470232705, 0.0892508158115509,
0.0506686006240954))

# run with defaults
mymeta <- meta(y = yi, v = vi, data = dat)
summary(mymeta)
# Ends in Status1: 5
# Intercept1 estimated as 0.148, SE = 0.069, p = 0.032
# Tau2_1_1 goes to RE.lbound

# run with extended lower bound in tau2
mymeta2 <- meta(y = yi, v = vi, data = dat, RE.lbound = 1e-100)
summary(mymeta2)
# Ends in Status1: 5
# Intercept1 estimated as 0.148, SE = 0.069, p = 0.032
# Tau2_1_1 again goes to RE.lbound

# run fixed-effects to restrict (allow?) RE to zero
mymeta3 <- meta(y = yi, v = vi, data = dat, RE.constraints = 0)
summary(mymeta3)
# Ends in Status1: 0
# Intercept1 estimated as 0.148, SE = 0.092, p = 0.110
# SE of intercept increases by 33%!
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Joined: 10/08/2009 - 22:37
You should not trust the

You should not trust the results in mymeta and mymeta2 as the status code is 5. See https://openmx.ssri.psu.edu/wiki/errors

A fixed-effect model is a special case of a random-effects model when the heterogeneity variance is 0. Even if you fit a random-effects model, the final result may suggest a fixed-effect model.

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Joined: 04/03/2021 - 19:00
OK, thanks!

Sounds good. I'll avoid interpretation of the random-effects models because of their status code 5. I just thought it was interesting how the fixed-effect and random-effects models reached similar values of Tau^2 but rather different SEs for the intercept!