HEDS is part of the School of Health and Related Research (ScHARR) at the University of Sheffield. We undertake research, teaching, training and consultancy on all aspects of health related decision science, with a particular emphasis on health economics, HTA and evidence synthesis.

Wednesday, 19 October 2016

Stephen Senn inaugural lecture….

…titled "Numbers needed to mislead, meta-analysis and muddled thinking".  Stephen is an Honorary Professor in the Design, Trials and Statistics section of ScHARR.

The lecture is on Wednesday 26 October 2016, 17.15-18.00 and followed by a wine reception.  The lecture is in Lecture Theatre 4, The Diamond. All are welcome to attend.  Please confirm your attendance, using the on-line booking form here.

The abstract to his lecture is below:
'The ardent espousal by the evidence based medicine movement of numbers needed to treat (NNT) as a way of making difficult statistical concepts simple and concrete, has has the unintended consequence of sowing confusion. Many users, including many in the evidence based movement themselves, have interpreted these statistics as indicating what proportion of patients benefit from treatment. However, they cannot deliver this information.

I shall explain this, with the example of a recent Cochrane Collaboration meta-analysis of paracetamol against placebo in trials of tension headache for which the plain language summary claimed:

'The outcome of being pain free or having only mild pain at two hours was reported by 59 in 100 people taking paracetamol 1000 mg, and in 49 out of 100 people taking placebo (high quality evidence), meaning that only 10 in 100 people benefited because of paracetamol 1000 mg.'

With the aid of a simple model also illustrated (just for fun) by a simulation, I shall show that the plain language conclusion is plain wrong. The observed facts do not necessarily mean that only 10 in 100 people benefited.

I conclude that the combination of arbitrary dichotomies and NNTs has a dangerous ability to deceive and may be leading us to expect much more of personalised medicine than it can deliver'

Image: The Diamond by Kyle Emmerson