[R-meta] Inflated confidence intervals
Wasim Iqbal (UG)
W@Iqb@l @ending from newc@@tle@@c@uk
Sun Sep 16 14:34:32 CEST 2018
Just to clarify,
If we were to obtain the overall summary measure for the model (not the difference between the parameters) we would add the intercept to the difference, like this to obtain the adjusted mean:
A) Y=Mx+b+b
B) Y= intercept + HDL + LDL
We are going to show the adjusted mean with the unadjusted mean in our publication.
Thank you
Kind regards
Wasim
On 16 Sep 2018 12:53 pm, "Viechtbauer, Wolfgang (SP)" <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
Sorry, you just lost me.
To repeat:
For the model you showed (which apparently was fitted with 'mods = ~ measurement', since it includes an intercept term), if you want to know the difference between HDL and LDL, then you should use:
predict(resMV, newmods = c(1,0))
which is identical to:
predict(resMV, newmods = c(1,0), intercept=TRUE)
As I showed, this gives the same point estimate for the difference between HDL and LDL as obtained from the model with only the two outcomes.
Best,
Wolfgang
-----Original Message-----
From: Wasim Iqbal (UG) [mailto:W.Iqbal using newcastle.ac.uk]
Sent: Sunday, 16 September, 2018 13:04
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
Cc: Gavin Stewart; Chris Seal
Subject: Re: Inflated confidence intervals
Dear Wolfgang,
Thank you, however I believe I have done this in the final predict function in the email. I was trying to show how this was different from the full model and a model that included only LDL and HDL.
I did not include the intercept in the initial code:
>rma.mv(x, mods=measurement) as opposed to >rma.mv(x, mods=measurement-1)
Then, when I call the command: >predict (x, newmods=c(1,1), intercept=T), surely I must include intercept as true? Otherwise this does not include it unless I had include it in the initial code.
Kind regards
Wasim
________________________________________
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> on behalf of Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: 16 September 2018 11:42:23
To: Wasim Iqbal (UG); r-sig-meta-analysis using r-project.org
Cc: Gavin Stewart; Chris Seal
Subject: Re: [R-meta] Inflated confidence intervals
Dear Wasim,
It seems like you are trying to obtained the estimated (average) difference between HDL and LDL. Based on the model with only HDL and LDL, this is equal to -0.0394 (with 95% CI: -0.1254 to 0.0466). However, your code to obtain the analogous value from the model with all three outcomes is not correct. It should be:
predict(resMV, newmods = c(1,0))
(intercept=TRUE isn't needed, since this is the default here). Then this will yield: -0.2376 + 0.1982 = -0.0394, which is the exact same value as obtained from the model with only the two outcomes. I cannot tell you what the CI will be based on the output provided, but I suspect you will get something very similar as you obtained from the model with only the two outcomes.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Wasim Iqbal (UG)
Sent: Sunday, 16 September, 2018 8:37
To: r-sig-meta-analysis using r-project.org
Cc: Gavin Stewart; Chris Seal
Subject: [R-meta] Inflated confidence intervals
I have finally understood and a found a simple solution or reason for inflated confidence intervals and I thought I would post it here so that it may help others.
I am conducting a multivariate meta-analysis for an upcoming publication in the Campbell library (campbellcollaboration.org<https://campbellcollaboration.org>). This will involve the analysis of various biomarkers. I had made a list of a posterior contrasts. However, when I had compiled these interactions in a multivariate meta-analysis some models led to wider confidence intervals. At first, I had assumed this was because my model had collapsed or that the variability between studies was great. After a lot of reading and contemplating, I can conclude that this was infact to do with highly correlated outcomes (or multicollinearity).
My multivariate meta-analysis relies on an assumed pearson correlation between outcomes. If a pearson correlation is highly correlated this will lead to either an upward or downward bias of the confidence intervals. This is because the multivariate meta-analysis model restricts the average correlation between -1.00 and +1.00 to produce a symmetric var-cov matrix (or positive definite) so that the model can be formed. Removing outcomes that appear to be highly correlated leads to narrower confidence intervals.
Here is an example of a model with studies looking at serum total cholesterol, high density lipoprotein and low density lipoprotein:
Multivariate Meta-Analysis Model (k = 105; method: REML)
Variance Components:
outer factor: Study (nlvls = 35)
inner factor: Measurement (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 0.0812 0.2850 35 no A LDL
tau^2.2 0.0076 0.0874 35 no HDL
tau^2.3 0.0487 0.2208 35 no TC
rho.ALDL rho.HDL rho.TC ALDL HDL TC
A LDL 1 0.4819 0.8774 - no no
HDL 0.4819 1 0.8432 35 - no
TC 0.8774 0.8432 1 35 35 -
Test for Residual Heterogeneity:
QE(df = 102) = 207.8936, p-val < .0001
Test of Moderators (coefficient(s) 2:3):
QM(df = 2) = 18.1033, p-val = 0.0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -0.2376 0.0648 -3.6655 0.0002 -0.3647 -0.1106 ***
MeasurementHDL 0.1982 0.0513 3.8681 0.0001 0.0978 0.2987 ***
MeasurementTC -0.0148 0.0656 -0.2251 0.8219 -0.1433 0.1138
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
In the above model which includes 35 studies each contributing all three outcomes. Total serum cholesterol (TC) appears to be highly correlated with low density lipoprotein (LDL) and high density lipoprotein (HDL). Therefore, when we use the predict function this leads to a much wider confidence interval than a bivariate model that includes only LDL and HDL.
Model with all three contrasts/interactions:
>predict(resMV,newmods = c(1,1),intercept = T)
pred se ci.lb ci.ub cr.lb cr.ub tau2.level
-0.0541 0.0588 -0.1695 0.0612 NA NA NA
Model with only HDL and LDL:
>pred se ci.lb ci.ub cr.lb cr.ub tau2.level
-0.0394 0.0439 -0.1254 0.0466 NA NA NA
Another reason for multicollinarity or wider confidence intervals is if your multivariate meta-analysis does not include enough studies to estimate an accurate "between study outcomes pearson correlation".
Hope this helps anyone that has experienced a similar problem or confusion.
Kind regards
Wasim Iqbal
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