Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
[R-meta] rma.mv only for better SEs
13 messages · Simon Harmel, Wolfgang Viechtbauer
Generally, two models with different random effects structures will also give you different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
Thank you, Wolfgang. I asked this, because I noticed applying RVE to an rma.mv() model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we go from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed effects? For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents the average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average represents the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Generally, two models with different random effects structures will also give you different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed?applying RVE to an rma.mv() model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we go from rma() to rma.mv(). But is there a principle?regarding how random effects affect the fixed effects? For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents the average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average represents the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also give you different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
I have done it, and in my case the results differ. But my point was, is my explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and
just
modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply
when we go
from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the
outcome-level
effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also
give you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models
collapse
down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only
modifies
their SEs relative to rma()? Thank you, Simon
The random effects structure determines the weight matrix, which in turn affects the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is my explanation?regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed?applying RVE to an rma.mv() model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we
go
from rma() to rma.mv(). But is there a principle?regarding how random effects affect the fixed effects? For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average represents the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
Sure, but didn't you by any chance mean to say: "The random effects structure determines the weight matrix, which in turn affects the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
The random effects structure determines the weight matrix, which in turn affects the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is my explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and
just
modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply
when we
go
from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the
outcome-level
effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also
give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models
collapse
down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only
modifies
their SEs relative to rma()? Thank you, Simon
Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance?mean?to say: "The random effects structure determines the weight matrix, which in turn affects the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn affects the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is my explanation?regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed?applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we
go
from rma() to rma.mv(). But is there a principle?regarding how random effects affect the fixed effects? For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
This is very helpful, thank you so very much. Simon ps. This may be loosely relevant but in ordinary multilevel models, we don't use weights, but still random-effects' structures do have a bearing on the fixed effect estimates. So, aside from weights, something else from random-effects must have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance mean to say: "The random effects structure determines the weight matrix, which in turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn
affects
the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is
my
explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and
just
modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply
when we
go
from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the
outcome-level
effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will
also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models
collapse
down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for
my
fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only
modifies
their SEs relative to rma()? Thank you, Simon
This is not correct. Also ordinary multilevel models have a weight matrix.
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:14 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs This is very?helpful,?thank you so very much. Simon ps. This may be loosely relevant?but in ordinary multilevel models, we don't use weights, but still random-effects' structures do have a bearing on the fixed effect estimates. So, aside from weights, something else from random-effects must have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance?mean?to say: "The random effects structure determines the weight matrix, which in turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn affects the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is my explanation?regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed?applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we
go
from rma() to rma.mv(). But is there a principle?regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
Oh, all I knew was that ordinary multilevel estimates of fixed effect are obtained via empirical Bayes (eb) and have the following algebraic relation to their OLS counterparts. Is there any reference that explains the nature of these weights and refers to them as "weights"? Beta_eb = Lambda * Beta_ols + (1 - lambda) * grand mean where Lambda = Heterogeneity_betw. / [Heterogeneity_betw. + (residual var. / n_clusters)] On Mon, Jan 31, 2022 at 2:27 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
This is not correct. Also ordinary multilevel models have a weight matrix.
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:14 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs This is very helpful, thank you so very much. Simon ps. This may be loosely relevant but in ordinary multilevel models, we
don't use
weights, but still random-effects' structures do have a bearing on the
fixed
effect estimates. So, aside from weights, something else from
random-effects must
have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance mean to say: "The random effects structure determines the weight matrix, which in turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn
affects
the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is
my
explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and
just
modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply
when we
go
from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the
outcome-level
effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will
also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models
collapse
down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for
my
fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only
modifies
their SEs relative to rma()? Thank you, Simon
The fixed effects are estimated using ML/REML estimation. What you seem to be describing there are the EB estimates of the cluster-specific intercepts (e.g., Snijders & Boskers, 1999, p. 58-59) for a simple two-level 'empty' model with just a random intercept. With additional fixed effects and/or random effects, things will get more complex. There are many books that go into this; for example Searle (1992), chapter 6 is very thorough. I will bow out of further replies as typing is aggravating my arm. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:40 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Oh, all I knew was that ordinary multilevel estimates of fixed effect are obtained via empirical?Bayes (eb) and have the following algebraic relation to their OLS counterparts. Is there any reference that explains the nature of these weights and refers to them as "weights"? Beta_eb = Lambda * Beta_ols?+ (1 - lambda) * grand mean where Lambda = Heterogeneity_betw. /? [Heterogeneity_betw. + (residual var. / n_clusters)] On Mon, Jan 31, 2022 at 2:27 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: This is not correct. Also ordinary multilevel models have a weight matrix.
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:14 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs This is very?helpful,?thank you so very much. Simon ps. This may be loosely relevant?but in ordinary multilevel models, we don't use weights, but still random-effects' structures do have a bearing on the fixed effect estimates. So, aside from weights, something else from random-effects
must
have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance?mean?to say: "The random effects structure determines the weight matrix, which in turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn
affects
the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is my explanation?regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed?applying RVE to an
rma.mv()
model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when
we
go
from rma() to rma.mv(). But is there a principle?regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon
Great, thanks! (Truly hope you feel better very soon) Simon On Mon, Jan 31, 2022 at 2:56 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
The fixed effects are estimated using ML/REML estimation. What you seem to be describing there are the EB estimates of the cluster-specific intercepts (e.g., Snijders & Boskers, 1999, p. 58-59) for a simple two-level 'empty' model with just a random intercept. With additional fixed effects and/or random effects, things will get more complex. There are many books that go into this; for example Searle (1992), chapter 6 is very thorough. I will bow out of further replies as typing is aggravating my arm. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:40 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Oh, all I knew was that ordinary multilevel estimates of fixed effect are obtained via empirical Bayes (eb) and have the following algebraic
relation to
their OLS counterparts. Is there any reference that explains the nature of these weights and
refers to
them as "weights"? Beta_eb = Lambda * Beta_ols + (1 - lambda) * grand mean where Lambda = Heterogeneity_betw. / [Heterogeneity_betw. + (residual
var. /
n_clusters)] On Mon, Jan 31, 2022 at 2:27 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: This is not correct. Also ordinary multilevel models have a weight matrix.
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 21:14 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs This is very helpful, thank you so very much. Simon ps. This may be loosely relevant but in ordinary multilevel models, we
don't use
weights, but still random-effects' structures do have a bearing on the
fixed
effect estimates. So, aside from weights, something else from
random-effects
must
have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance mean to say: "The random effects structure determines the weight matrix, which in
turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn
affects
the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was,
is my
explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Just try it out and you will see what happens. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:21 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Thank you, Wolfgang. I asked this, because I noticed applying RVE to
an
rma.mv()
model has no bearing on the estimates of fixed effects themselves,
and just
modifies their SEs. So, I wondered if the same rule, at least "in principle", should
apply when
we
go
from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed
effects?
For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents
the
average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents
the average of study-level effects additionally affected by the
outcome-level
effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Generally, two models with different random effects structures will
also give
you
different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models
collapse
down to the same structure). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs
for my
fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only
modifies
their SEs relative to rma()? Thank you, Simon