---
title: "Solution Session 5"
author: "Dr. Thomas Pollet, Northumbria University (thomas.pollet@northumbria.ac.uk)"
date: '`r format(Sys.Date())` | [disclaimer](https://tvpollet.github.io/disclaimer)'
output:
html_document:
toc: true
---
## Questions.
Using the `dat.raudenbush1985` from week 3. Rerun the random-effects meta-analysis with REML estimation from the earlier exercises.
* Perform subgroup analyses with tester blindness (`tester`): build one model with a common $\tau^2$ estimate and one without. What do you conclude?
* Perform a meta-regression with publication year (`year`). Center the year variable first. Make a bubble plot illustrating this meta-regression. What do you conclude?
* Perform a permutation test with 5000 shuffles for the meta-regression with publication year. What do you conclude?
* Build a meta-regression model with tester blindness (`tester`) compare it to the meta-regression which contains both tester blindness (`tester`) and centered publication year (`year`). What do you conclude?
* Build an interaction model between tester blindness and publication year. What do you conclude?
## Load and manipulate the data
There are 19 studies.
```{r warning=F, message=F}
library(meta)
library(metafor) # needed later on.
Data<-dat.raudenbush1985
head(Data)
```
I have made the names as in our slides. Note that this is unnecessary duplication (as TE = _yi_ ) but then it maps on nicely to our code.
```{r}
library(dplyr)
Data <- Data %>% mutate(TE=yi, seTE=sqrt(vi))
```
As in the solution of exercise 3, let's combine, author and year.
Let's bracket the year as per convention. Here I rely on base R and [this snippet](https://stackoverflow.com/questions/29884082/apply-parentheses-around-elements-of-r-dataframe).
```{r}
Data$year_b <- paste0("(", format(unlist(Data[,3])),")")
```
Then we combine as done in [here](https://stackoverflow.com/questions/18115550/combine-two-or-more-columns-in-a-dataframe-into-a-new-column-with-a-new-name). Here I have opted for a '[tidyverse](https://www.tidyverse.org/)' solution.
```{r warning=F, message=F}
library(tidyverse)
Data <-Data %>% unite(author_year, c(author, year_b), sep = " ", remove = FALSE)
```
Let's redo our model but now with `author_year`.
```{r}
require(meta)
model_reml2<-metagen(TE,
seTE,
data=Data,
studlab=paste(author_year),
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "REML",
hakn = FALSE,
prediction=TRUE,
sm="SMD")
model_reml2
```
## Subgroup analyses.
Below we perform the models by group.
Interestingly, it seems that the effect is stronger in the blinded group than in the aware group - perhaps they were better studies? However, there is no significant difference between the groups ( _Q_=.84, _p_ =.367).
```{r,warning=F, message=F}
tester_subgroup_common<-update.meta(model_reml2,
byvar=tester,
comb.random = TRUE,
comb.fixed = FALSE,
tau.common=TRUE)
tester_subgroup_common
```
This model allows an estimate of variance $\tau^2$ for each group. The conclusion is the same there is no evidence for a significant difference between these groups ( _Q_=.81, _p_ =.367).
```{r,warning=F, message=F}
tester_subgroup_sep<-update.meta(model_reml2,
byvar=tester,
comb.random = TRUE,
comb.fixed = FALSE,
tau.common=FALSE)
tester_subgroup_sep
```
## Meta-regression with year
### Center year
```{r}
Data <- Data %>% mutate(year_cent = year-mean(year))
```
### Meta-regression
We need to rerun our model as we have added our new moderator variable, note that this overrides our previous model.
```{r}
require(meta)
model_reml2<-metagen(TE,
seTE,
data=Data,
studlab=paste(author_year),
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "REML",
hakn = FALSE,
prediction=TRUE,
sm="SMD")
model_reml2
```
There is no suggestion that publication year is a viable predictor of the effect size (_Q_ = .41, _p_= .52).
```{r}
metareg_pub_year<-metareg(model_reml2,year_cent)
metareg_pub_year
```
### Plot
```{r}
bubble.metareg(metareg_pub_year,
xlab = "Publication Year (centered)",
ylab = "SMD",
col.line = "hotpink",
studlab = TRUE)
```
## Permutation test
We'll use `set.seed()` to ensure we get the same results every single time.
The permutation test corroborates our findings from the meta-regression. There is no substantial evidence for an effect of publication year.
```{r}
set.seed(1981)
permutest(metareg_pub_year, iter=5000, progbar=F)
```
## Compare models
Here we make a switch to `metafor`.
As can be expected this corroborates what we previously found when examining subgroups. Note that I have opted for ML estimation with the Knapp-Hartung estimation. As model comparisons aren't well defined with REML methods, we switched to ML estimation in order to allow
```{r}
metareg_tester<-rma(yi=TE,
sei=seTE,
data=Data,
method = "ML",
mods = ~ tester,
test="knha")
metareg_tester
```
```{r}
metareg_pub_tester<-rma(yi=TE,
sei=seTE,
data=Data,
method = "ML",
mods = ~ tester + year_cent,
test="knha")
metareg_pub_tester
```
```{r}
anova(metareg_tester,metareg_pub_tester)
```
Both the AIC/BIC (as well as the Likelihood ratio test (LRT)) do not suggest superiority of one model over another.
## Interaction model
We reverted back to the REML model estimation. This model does not support an interaction effect between publication year and tester blindness. Even if it had, we should be very wary as we only have 19 studies in this meta-analysis.
```{r}
metareg_interaction<-rma(yi=TE,
sei=seTE,
data=Data,
method = "REML",
mods = ~ tester * year_cent
)
metareg_interaction
```
## Acknowledgments and further reading... .
The example is from [here](http://www.metafor-project.org/doku.php/analyses:raudenbush2009#fixed-effects_model).
Note that throughout I have varied the rounding when I reported, you should make your own decisions on how precise you believe your results to be.
Please see the slides for further reading but a good place to start is Chapter 8 on models and approaches to inference in Koricheva, J., Gurevitch, J., & Mengersen, K. (2013). _Handbook of Meta-Analysis in Ecology and Evolution_. Princeton, NJ: Princeton University Press.
**Cited literature**
Raudenbush, S. W. (1984). Magnitude of teacher expectancy effects on pupil IQ as a function of the credibility of expectancy induction: A synthesis of findings from 18 experiments. _Journal of Educational Psychology, 76(1)_, 85–97.
Raudenbush, S. W. (2009). Analyzing effect sizes: Random effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), _The handbook of research synthesis and meta-analysis_ (2nd ed., pp. 295–315). New York: Russell Sage Foundation.
## The end.
```{r, out.width = "400px", echo=FALSE}
knitr::include_graphics("https://media.giphy.com/media/20NDbrSQYVKAUKiSoz/giphy.gif") # giphy.com fair use.
```
## Session info.
```{r}
sessionInfo()
```