Very educational and still very high-level. Is there guiding videos/screencasts on meta-analysis with different types of response variables, modulators and outcomes? Perhaps covering the considerations of multivariate and multilevel meta-analysis as well as composite outcome meta-analyses, such as the concept of "borrowing of strength" (BoS) in meta-analysis where one has to deal with multiple outcomes (response variables) or studies with small sample sizes. Thank you
Thank you for this insightful exposition to mirt. I am currently on an academic thesis at the University of Nigeria, Nsukka to study the DIF if a state examination, using many methods of which mirt is one. I find this video very instructive and followed through until I couldn't view the script you engaged to perform DIF. Could you share the script with me, or guide me to write my own script uniquely? Here is my unique case: I coded a 41-column dichotomously scored dataset with the name ESUPE _G.csv with the first column named gender as the grouping variable. In my data columns 2 to 40 are the item responses, while column 1 holds the gender grouping. I intend to run a DIF analysis using mirt package in R. How do I write a good R-script for the DIF analysis to flag DIF items? Thanks
Thank you very much for the video, it is very helpful! I am glad I have a chance to ask you a question here about it as well! I have the following problem. It happened that I have only one level-2 variable and it has several missing values(it was an online survey from two organizations and there was a trouble with the data collection, so I am am sure the missing data is MCAR). I try to use 2lonly.pmm to fill in the gaps. I specify this variable as a -2 in pred: suppressWarnings(suppressMessages( ini <- mice(df, maxit = 0) )) pred <- ini$pred pred [,"school"] <- -2 When running the mice function it gives me the following error: Error in check.cluster(data, predictorMatrix) : Convert cluster variable school to integer by as.integer() I switched the organization variable to an integer and the error message then became as the following: Error in .imputation.level2(y = y, ry = ry, x = x, type = type, wy = wy, : No class variable I added additional variable "city", where both organizations are at, and specified it as a cluster variable in the pred. It didn't help, same error. Then I tried to specify the full pred matrix in excel and read it from a csv file as a matrix, the error changed to: Error in predictorMatrix[h, ] : subgroup out of bounds I consulted your book again and saw the following passage: "It is conceptually straightforward to extend imputations to higher levels (Yucel 2008). If there are two levels, combine all level-2 predictors with an aggregate (e.g., the cluster means) of the level-1 predictors and the level-1 outcomes. Once we have this, we may choose suitable methods from Chapter 3 to impute the missing level-2 variables in the usual way. No new issues arise." Is there a way to fix my problem? Or, given the above, is there maybe a way to not treat this variable as a cluster variable and just impute it with logreg in mice since it's the only one variable? Do you mean by aggregate to compute mean values or cluster variables? Thank you in advance for reading the post and have a great day ahead! Best Regards, Yulia
Thank you very much, very insightful lesson. Could you please tell me where I can also find a lesson or some material on meta-analysis of survival data (Hazard Ratios). It would really help. Thanks
Hi. My first question for MetaSem: do the correlation coefficients have to be given in the primary studies? For example, can't MetaSem be done using d or g effect sizes? My other question is what exactly is the difference between MetaSem and meta-analysis? Thank you very much in advance for your answers...
What about plotting posterior distributions and seeing how well they mirror the observed distribution (ie the PPE check)? Similarly, plotting predicted vs observed scatterplots, like in Shinystan for HLM models? Ty :)
Thank you very much to all presenters! This is a very newbie-friendly video to learn SEM using R! Everything is well organized and well explained! Looking for more good videos:)
Thank you for this video! Do you have any advice on how to organzie data when doing the extracting from studies? Do you use excel or something? I want to do a meta analysis on low birth weight and mental health, but there are sooo many different tests for mental health that I cant think out a smart way to organize it in excel.
Hi, this channel only focuses on publishing content from the UseR Oslo group. For specific questions about the content, we encourage you to contact the speaker or relevant forums.
Hi, this channel only focuses on publishing content from the UseR Oslo group. For specific questions about the content, we encourage you to contact the speaker or relevant forums.
Thank you for the package! It's pretty good! I'm wondering, if you have a citeable source for thresholds of epsilon²? I need to interpret the effect size but can not find any source for thresholds or a rule of thumb for the interpretation... Any help is appreciated.
Hi, this channel only focuses on publishing content from the UseR Oslo group. For specific questions about the content, we encourage you to contact the speaker or relevant forums.
Amazing package. Thank you! I'm wondering how to model the covariance structure in a bayesian longitudinal setting, similar to covariance patterns such as compound symmetry, autoregressive, Topelitz etc. in the frequentist world. In the frequentist world, taking serial correlation into consideration narrows the confidence intervals of the parameters. How to model the covariance structure in a bayesian longitudinal setting? I'm wondering if a bayesian intercept always introduces compound symmetry, similar to a random intercept in a frequentist linear mixed effects model? I suspect taking serial correlation would narrow the posterior distributions of the model parameters, strengthening the bayesian inference. However, I'm not at all sure if my thoughts are anywhere near correct. The brms package is a very valuable resource. However, the parts about covariance structures seem to be still in progress. If anyone has good theoretical (and why not practical) bayesian references regarding these covariance modeling issues (serial correlation etc.), I would appreciate them very much.
Hi, this channel only focuses on publishing content from the UseR Oslo group. For specific questions about the content, we encourage you to contact the speaker or relevant forums.
Thank you so so so much for this AMAZING tutorial!!! This is actually my second time trying to use R, and this video was a huge help in understanding the basic logic behind conducting SEM using R. I could even code the mediation model by myself in 3 minutes, which made me very happy :D You are a great educator
Most likely replaced in a newer version. Version 6.0 came out in April, and could not find the function there. I will reach out to the presenter to see if caretList has been replaced by a similarly working function.
@@UseROslo I'm also wondering what are the nonparametric options for caret? The package is generally pretty easy to use but my data is completely impossible to normalise so it's either use nonparametrics or bootstrap it all to kingdom come.
How to change the font style of part of a string in forestplot? Specifically, I want to italicize the text (ABCX (rs1111111-X)), which is the name of one of the columns.
Hi Yousif, I can't find anything in the metafor documentation that allows you to do that. I would recommend you check out relevant forums for metafor, or contact the package creator.
Is it possible to forest plot odds ratios when only given the ratio and upper and lower limits? Since many studies plainly list their OR with 95%CI and p value without reporting the samples sizes. I cannot seem to find any videos regarding this and would greatly appreciate some advice.
Hi, I am struggling to run a metanalysis. It is my first time. I have continuous data with mean, sample size, and standard deviation for each study (without means for control and exposure groups.) Any ideas on the appropriate argument suitable for this type of data?
Hi, this is outside of our scope of knowledge, as the channel is run by the Oslo UseR organizers, and the individual speakers do not moderate the recordings we upload. Please refer to relevant forums or metafor's own webpage, www.metafor-project.org/
Really great meetup, a little question here, why do we need RVE when the multilevel model is already designed for dependent effect sizes at a certain level?
Hi, this channel is primarily for posting the recordings, and your questions goes beyond my knowledge unfortunately. I recommend you contact the speaker directly with your question.
Hi, I got the same question too, have you got an answer so far? I think multilevel models assume cross-level residuals independence, unless clubSandwich adjusts for that?