Thanks, that's enlightening. I am definitely not a statistician, as you've no doubt guessed! >I think what you may be misunderstanding, and therefore misrepres

Thanks, that's enlightening. I am definitely not a statistician, as you've no doubt guessed! >I think what you may be misunderstanding, and therefore misrepresenting, is the idea that a combination of statistically sampled tests (ONS) plus biased targeted tests (NHS, key workers, Test & Trace etc) results in "skewed" or more misleading R estimates than just the statistically sampled tests (ONS) by themselves. Not quite, and I apologise if I've communicated poorly. Totally agree that multiple sampling methods lead to more convincing results. I avoid optimising until I've profiled with both a sampling AND an instrumenting profiler, for example. What I was trying (failing!) to get across is that I think that if the distribution of 'generally available' tests is sufficiently non-representative, then the statistically sampled test model potentially couldn't correct for it enough for the derived estimate to be reliable. Obviously the responsible subgroup at SAGE will have tried to weight accordingly, and the people running the individual studies will as you say, be much more skilled at accounting for potential bias than I am. I'm absolutely not saying that I've stumbled on some insight that they missed! Here's where I struggle - the pool of generally available tests at the time fall into two relatively narrow groups: patients exhibiting symptoms bad enough to require hospitalisation, and people at high risk of transmission. The random longitudinal studies don't have that problem of course and should be used to correct for the bias. I realise there are methods for quantifying bias and uncertainty, but isn't this more at risk of inaccuracy than simply having a higher N? N matters, surely? The higher confidence we have that any one set of data is representative, the higher confidence we can have in the final derived estimate. I just can't see any angle from which having tested more people we wouldn't have improved our ability to estimate R.