And here I can trust the output

from the fixed effect model versus the random effects model.

So on top of the first figure shows you the results from the fixed effect model,

and the port odds ratio is 0.5485 with a 95% confidence interval.

And if you remember the force study,

the lane study takes up 41% of the weight based on a fixed effect model.

And if you look a the same study,

on the random effects model it takes up only 25% of the weight.

So the random effects model basically assume all these studies

have a distribution of effects so

you're adding another source of variance to each individual study.

So the idea is the random effects model are pulling study or

shrinking the estimate together.

So the random effects model is giving a little bit of more weight to smaller study

or cut a little bit of weight from the larger study, so

it's basically pulling them towards the center.

So under the random effects model, the poll estimated.

Is 0.568 and you have an estimate of the confidence interval.

And I want to go back to the basic idea.

So the basic idea is what?

We have a bunch of studies and in this example six of them.

And you have odds ratio from each individual study.

So if you look at the second column from both figures

those are the odds ratio you get from individual study.

Those are the data you collected as part of your data abstraction for

your systematic review.

Regardless of which model you're going to use,

those numbers remain the same, because that's the data from what you observed.

And the only differences here is, now we have the observed data,

we're trying to guess or we're trying to estimate where that diamond is.

So the diamond is pulled estimate.

We have two diamonds, one under the fixed effect model and

one under the random effects model.

But by using these two different models, the diamond one lies slightly differently

on the plot, and it will have a smaller, a little bit wider confidence interval.

As you can imagine,

on the random effects model, You're less certain about the center of the diamond

because now you're saying we have two source of variation.

One is the within and one is between.

So your data you have has been partitioned

to estimate those two sources of variation.

That's why it's a little bit wider than the fixed effects model.

So again you're using what you observed to figure out where the diamond is.

And you can do it through making two different assumptions.

One is all the studies are identical.

That's why I'm getting a very precise estimate under the fixed effect.

Or you are saying the studies are slightly different from one another.

That's usually the case.

That's why you're getting a less precise estimate under the random effects model.