[R-meta] Dependent Measure Modelling Question
Grace Hayes
gr@ce@h@ye@3 @end|ng |rom my@cu@edu@@u
Tue Mar 12 05:03:24 CET 2019
Dear James,
Thank you for your response to my previous query. Yes, the effect size estimates are statistically dependent. Therefore, as per your recommendation, I have read over a few tutorials that cover multivariate meta-analysis and robust variances estimations. Specifically, the one that you wrote about using club sandwich to run co-efficient tests followed by Wald-tests. This article was most helpful! I have a follow up question regards the use of the Wald-test, which I have outlined below.
My three potential moderators are: task_design (two levels), Emotion (6 levels) and StimuliType (5 levels). To test the moderating effect of each of these variables I ran the following:
allModerator <- rma.mv( yi, vi, mods = ~ task_design + Emotion + StimuliType, random = ~ 1 | studyID/outcome/effectID, tdist = TRUE, data = dat)
coef_test(allModerator, vcov = "CR2")
#NUMBER OF EMOTIONS
Wald_test(allModerator, constraints = 2, vcov = "CR2")
#EMOTIONTYPE
Wald_test(allModerator, constraints = 3:7, vcov = "CR2")
#STIMULITYPE
Wald_test(allModerator, constraints = 8:11, vcov = "CR2")
The constraints for each Wald test match the coefficients related to each moderator, so I believe these tested for the significance of each moderator while adjusting for the other two moderating variables. However, I was also interested in variance across the estimated average effect produced by each stimuli format for each emotion. I followed the below guide by Wolfgang Viechtbauer, that showed how to parameterize the model to provide the estimated average effect for each factor level combinations.
http://www.metafor-project.org/doku.php/tips:multiple_factors_interactions
My model was:
StimulibyEmotion <- rma.mv(yi, vi, mods = ~ StimuliType:Emotion -1, random = ~ 1 | studyID/outcome/effectID, tdist = TRUE, data=dat)
coef_test(StimulibyEmotion, vcov = "CR2")
Wolfgang then uses anovas to test factor level combination against each other. Can I use the Wald test to do this to my robust variance estimations?
Also, would it be possible for you to please elaborate on what you meant by "a model that allows for different heterogeneity levels for each emotion", or provide a link to an article demonstrating this? As a first time used of R and metafor, I wasn't sure how to go about this.
Many thanks,
Grace
________________________________
From: James Pustejovsky <jepusto using gmail.com>
Sent: Tuesday, 12 February 2019 1:37 PM
To: Grace Hayes
Cc: r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] Dependent Measure Modelling Question
Grace,
It sounds like the data that you're describing has two factors, emotion type and task type, and that both are within-study factors (in other words, a given study might report results for multiple emotion types and/or multiple task types). Are the emotion types and task types also measured within-participant, such that a given participant in a study gets assessed with multiple task types, on multiple emotion types, or both? If so, then one challenge in analyzing this data structure is that the effect size estimates will be statistically dependent. There are several ways to handle this (multivariate meta-analysis, robust variance estimation), which we've discussed in many previous posts on the listserv.
Other than this issue, it sounds to me like it would be possible to analyze both factors---emotion type and task type---together in one big model. The major advantage of doing so is that the joint model would let you examine differences in emotion types *while controlling for task types*, as well as examining differences in task types *while controlling for emotion types*. Controlling for the other factor (and maybe other covariates that are associated with effect size magnitude) should provide clearer, more interpretable results for differences on a given factor. There is also evidence that using a multivariate meta-analysis model can potentially mitigate outcome reporting bias to some extent (see Kirkham, Riley, & Williamson, 2012; Hwang & Desantis, 2018).
A further advantage of using one big model is that it would let you adjust for other potential moderators that might have similar associations for each emotion type and each task type. If you conduct separate analyses for each emotion type (for example), you would have to analyze these moderators separately, so you'd end up with 6 sets of moderator analyses instead of just one.
The main challenge in the "one big meta-analysis model" approach is that it requires careful checking of the model's assumptions. For example, you would need to assess whether the between-study heterogeneity is similar across the six emotion types and, if not, fit a model that allows for different heterogeneity levels for each emotion.
James
Hwang, H., & DeSantis, S. M. (2018). Multivariate network meta$B!>(Banalysis to mitigate the effects of outcome reporting bias. Statistics in medicine.
Kirkham, J. J., Riley, R. D., & Williamson, P. R. (2012). A multivariate meta$B!>(Banalysis approach for reducing the impact of outcome reporting bias in systematic reviews. Statistics in medicine, 31(20), 2179-2195.
On Mon, Feb 11, 2019 at 3:16 AM Grace Hayes <grace.hayes3 using myacu.edu.au<mailto:grace.hayes3 using myacu.edu.au>> wrote:
Hi all,
I have a question regarding a meta-analysis of multiple dependent outcomes that I would like to conduct using metafor.
For this meta-analysis of emotion recognition in ageing, I'm interested in age-effects (young adults vs. older adults) on four different emotion recognition tasks (Task A, Task B, Task C, Task D). Studies in this area typically compare older adults' performance to younger adults' performance on more than one of these emotion recognition task.
For each task there are also multiple outcomes. Each task produces an accuracy age-effect for each emotion type included (I.e., anger, sadness, fear). Up to 6 different emotions are included (Emotion 1, Emotion 2, Emotion 3, Emotion 4, Emotion 5, Emotion 6). I therefore have some studies with, for example, 6 different age-effects from 3 different emotions tasks; a total of 18 dependent outcomes.
Ideally I would like to investigate age-effects for each of the six emotion types seperately (with Tasks A, B, C and D combined), and age-effects for each task type seperately (with Emotions 1-6 combined). I would then like to compare the effects for each emotion type (Emotions 1-6 separately) produced by each task (Measure A, B, C, D separately).
My question is, can I have a model that analyses emotion type and task type all together? Is this possible and statistically appropriate? Will it tell me the age-effects produced for each emotion by each task, or will it only tell me if task type and emotion type are significant moderators?
I am also interested to know if I can add additional moderators such as number of emotions included in the task and year of publication?
One concern that has been brought to my attention is overfitting from too many factors. Another is that output would be difficult too interpret, and thus it has been recommended that I perhaps run separately analyses for each task.
Any advice would be much appreciated.
Sincerely,
Grace Hayes
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