Thanks Nick, for pointing us in the direction of that interesting (and provocative) article from Sir Michael Rawlins.
QUOTE - "Sir Michael rejects the trend to grade various kinds of clinical trials and studies on scales of merit which he says has come to dominate the development of some aspects of clinical decision making." This is an odd statement coming from the chair of an organisation which is increasingly using Bayesian Evidence Synthesis and Cost-Effectiveness arguments to underwrite its decisions. In these approaches weighting RCTs and Observational studies allows us to trade off the rigour of RCTs with the "real world" results of Observational trials. To paraphrase a well known Clinical Pharmacologist "Haven't we learned anything from 100 years of RCT design"? (Actually - please don't respond to that one!) We *are* moving (slowly) from the confirm-confirm-confirm paradigm of the "old world" statistical designs into more quantitative drug development where we *do* learn from previous studies, carry forward information quantitatively, predict outcomes, select designs which help address gaps in our knowledge and move on. Drug development in industry is a business and we are cost and time-constrained. We need to balance good science with more efficient drug development strategies. If we can't do this then drug development as a business is unsustainable. QUOTE - "Generalisability - RCTs are often carried out on specific types of patients for a relatively short period of time, whereas in clinical practice the treatment will be used on a much greater variety of patients - often suffering from other medical conditions - and for much longer. There is a presumption that, in general, the benefits shown in an RCT can be extrapolated to a wide population; but there is abundant evidence to show that the harmfulness of an intervention is often missed in RCTs." I agree with some aspects of his critique around the limitations of RCTs. In drug development we aim to learn about aspects of the drug and utilise Sheiner's "Learn and Confirm" cycles. However we can't get away from the fact that robust, quantitative proof of effect needs to come from a comparison of like with like - and RCTs are the best source to provide that evidence. We know that once the drug is licensed it will go out "into the wild": how it is prescribed by the MDs, how it is used by patients means that more often than not the drug is not taken "per protocol" in the real world. MDs and patients sometimes do strange things with medicines. Some of them we can't even dream of... No amount of Pop PK/PD, RCTs, learning or confirming will be able to address that issue. I guess the issue is that regulatory proof of efficacy using RCTs doesn't square with a body like NICE's needs for assessing cost-effectiveness in the real world setting where observational studies might be more informative. As for his comments about cost, unnecessary RCTs or impossible indications - unfortunately drugs which have a marked effect that appear quickly and where safety concerns pop out clearly are getting harder and harder to find. If only 1 in 30 drugs that enter clinical trial testing make it to registration then we're more likely to find drugs that FAIL than stop early due to efficacy (sadly). So our job should be to kill drugs early and cheaply as possible. With increased burden of evidence on safety from regulatory (and the public) I think it is inevitable that clinical trial costs will go up. I'm not sure whether Sir Michael was intending to be provocative, but his comments are sure to stir up debate, which is probably a good thing. Regards, Mike -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Nick Holford Sent: 16 October 2008 21:28 To: nmusers Subject: Re: [NMusers] Why population PK approach is good for pooling data compared to Classical statistical analysis? Ayyappa, <<SNIP>> Finally as Sir Michael Rawlins (Chairman of the NICE in the UK) pointed out yesterday the traditional statistical approach to clinical trials does not adequately describe the clinical pharmacology and benefits of medicines. The flexibility of the population approach allows it to used for 'learning' as well as 'confirming' (Sheiner 1997). This combination of approaches is in keeping with the broader philosophy posed by Rawlins. http://www.politics.co.uk/opinion-formers/press-releases/royal-college-p hysicians-sir-michael-rawlins-attacks-traditional-ways-assessing-evidenc e-$1245035$365674.htm Sheiner LB. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev. 1984;15(1-2):153-71. Sheiner LB. Learning versus confirming in clinical drug development. Clinical Pharmacology & Therapeutics. 1997;61(3):275-91.
