![\"20140205-091454.jpg\"](\"https:\/\/stg-blogs.bmj.com\/adc\/files\/2014\/02\/20140205-091454.jpg\")
I’ve been struggling to get the concept behind random-effects meta-analysis out for some time – it’s the ‘average effectiveness’ in an ‘average population’ – with the prediction interval being the ‘actual width of where the truth might lie’.<\/p>\nI’ve been struggling to get the concept behind random-effects meta-analysis out for some time – it’s the ‘average effectiveness’ in an ‘average population’ – with the prediction interval being the ‘actual width of where the truth might lie’.<\/p>\n
But .. yes .. but what does that actually mean & why does that matter?<\/p>\n
Well.<\/p>\n
Take\u00a0a primary school. Get the average height of the children in each class. Now use that to tell me\u00a0the average height of a child in that school.<\/p>\n
If I make a tonne of jogging pants for them based on the average height, I might\u00a0provide 15% with an OK fit, but mostly they’ll be too long or too short.<\/p>\n
If the data are very very heterogeneous, the average is “true”, it’s just not very useful.<\/p>\n
Does that make any more sense of how awkward a ‘random effects meta-analysis’ result can be in a highly heterogenous meta-analysis?<\/p>\n
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(ps – The overall average height might be useful to order the right amount of material from the weavers shed though.)<\/p>\n","protected":false},"excerpt":{"rendered":"
I’ve been struggling to get the concept behind random-effects meta-analysis out for some time – it’s the ‘average effectiveness’ in an ‘average population’ – with the prediction interval being the ‘actual width of where the truth might lie’. But .. yes .. but what does that actually mean & why does that matter? Well. Take\u00a0a […]<\/p>\n