a peculiar view from Vandenbroucke:
Two views exist of medical science: one emphasises discovery and
explanation, the other emphasises evaluation of interventions. This
essay analyses in what respects these views differ, and how they lead
to opposite research hierarchies, with randomisation on top for
evaluation and at bottom for discovery and explanation. The two views
also differ strongly in their thinking about the role of prior
specification of a research hypothesis. Hence, the essay explores the
controversies surrounding subgroup analyses and multiplicity of
analyses in observational research. This exploration leads to a
rethinking of the universally accepted hierarchy of strength of study
designs, which has the randomised trial on top: this hierarchy may be
confounded by the prior odds of the research hypothesis.
http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050067
--
Moacyr
__________________________
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Unidade de Epidemiologia ClĂnica
InCor - HCFMUSP
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Citando Stephen Senn <[log in to unmask]>:
> Dear Ben,
>
> In my opinion the philsophers are deeply confused about this.
>
> It is not necessary for the purpose of issuing a probability
> statement to know the value of unmeasured covariates; it is
> sufficient to know their distribution in probability.
>
> The following analogy may be helpful. Suppose we have to calculate
> the probability that sum of scores on two dice, a red die and a
> black die is 10. Actually one of the dice has alread been rolled,
> the red die, but we have not seen the score. It is sufficient,
> however, that we know that a necessary condition that the total
> score be 10 is that the score for the red die be 4,5, or 6. We know
> that this probability is 1/2 and we know that we now require a
> 6.5,or 4 on the black dies as the case may be and that in each case
> this probabability is 1/6. Hence the probability that the total a
> score is 10 is 1/12.
>
> Of course if somebody now reveals the score on the red die to us we
> can refine our probability assessment of 1/12. If the score is
> 1,2,or 3 the probability is now 0 since no value of the black die
> can make the total 10. if the score is 4,5,6 the probability is now
> 1/6. In fact the probability of 1/12 that we had before can be seen
> as being composed of (1/2 x 0) + (1/2 x 1/6).
>
> Similarly the fact that if confounders were revealed we would modify
> the probability assessment we have from an RCT does not mean that
> the probability assessment is invalid. It is a valid statement given
> what we know at that stage. All probability statements are
> conditional on what is known. The fact that if we knew more we would
> refine them does not make them unvalid when we don't know more.
>
> This is so obvious that I must confess to be underwhelmed by the
> level of understanding of this by certain critics of the RCT.
>
> See
>
> Senn, SJ. Baseline Balance and Valid Statistical Analyses: Common
> Misunderstandings, Applied Clinical Trials 2005; 14: 24-27.
>
> For a discussion.
>
> Stephen
>
> From: Evidence based health (EBH)
> [mailto:[log in to unmask]] On Behalf Of
> Djulbegovic, Benjamin
> Sent: 28 January 2011 16:15
> To: [log in to unmask]
> Subject: Re: Can RCT help establish causation?
>
> I should add that this is the very same argument made by the
> philosopher Worral in 2002 and recently repeated by Borgerson in
> 2009 ...that ..."given that there are indefinitely many possible
> confounding factors...the probability that the groups are unbalanced
> ...is high".
> It seems that this point is not challenged then? Thanks
> Ben
> Ps although my intent is to discuss feasibility of establishing
> causation, since this was brought up in the earlier responses, I
> should mention that Borgerson also challenges inherent capacity of
> RCTs to control biases better than non-RCTs. Re choice of the
> comparator we have published a number of pieces in which we have
> argued that explicit assessment of uncertainty (I.e. whether
> equipoise exists) about relative effects of competing treatment
> alternatives is the only known mechanism to help select adequate
> comparator.
>
>
>
> On Jan 28, 2011, at 10:45 AM, "Paul Elias"
> <[log in to unmask]<mailto:[log in to unmask]>> wrote:
> to add to Carolin's excellent post, Ian, can we also speculate that
> maybe besides randomization to equate the confounding factors across
> trial arms, that maybe once we ensure a large enough sample size, we
> will mitigate the role of confounding factors and ensure that the
> known and unknown factors are evenly spread out. In short, maybe one
> can argue that even if well designed and conducted, a RCT with small
> sample size arms can run the risk of an uneven distribution of
> factors. this issue has always intrigued me...can you comment?
>
> I still refer to Bradford-Hill 9 when interpreting causation....
>
>
>
>
>
>
> Best,
>
> Paul E. Alexander
>
>
>
>
>
>
> ________________________________
> From: Ian Johnson (CMA)
> <[log in to unmask]<mailto:[log in to unmask]>>
> To:
> [log in to unmask]<mailto:[log in to unmask]>
> Sent: Fri, January 28, 2011 10:34:23 AM
> Subject: Re: Can RCT help establish causation?
> Indeed, the fundamental principle in order to ensure that the
> treatment arm demographics are as similar as possible. Peto tried to
> refine this in the ISIS trials, where patients entering the study
> were assigned to a treatment arm based on their characteristics to
> predefine identical populations in each treatment arm, and spawned
> the observed vs expected statistic. This is difficult to achieve in
> an observational study, unless it is extremely large, and has
> sufficient power to segregate the confounding factors, so RCTs are
> likely to remain the method of choice. It is the discussion around
> the comparator treatment and the disconnect between the FDA and EMA
> requirements that is the cause of much debate.
>
> Ian
>
> From: Evidence based health (EBH)
> [mailto:[log in to unmask]] On Behalf Of Caroline
> Boulind
> Sent: 28 January 2011 15:19
> To:
> [log in to unmask]<mailto:[log in to unmask]>
> Subject: Re: Can RCT help establish causation?
>
> As far as I understand it, that is the purpose of randomisation. If
> it is done correctly, randomisation distributes all varying factors
> among the test population evenly between the arms of the study.
> Obviously, one can't identify all factors that might possibly be
> related to the intervention and outcome of interest to you, so the
> important thing about randomisation is that both known and unknown
> 'confounders' are (theoretically) evenly distributed. This means
> that the only thing changing between the arms of a trial is the
> intervention of interest, and differences in outcome between the two
> arms can therefore be interpreted as being a consequence of the
> intervention of interest.
> I suppose the problem is that there is no way of identifying those
> so called unknown confounders and it is therefore impossible to say
> definitively that there are no alternative causative variables that
> are differentially distributed between the arms of the study...
>
> For me the important point is that a well conducted and reported RCT
> is infinitely closer to being able to identify a causal relationship
> between exposure and outcome than an observational study. Until a
> better design is identified EBM is better placed taking its lead
> from well conducted RCTs than observational studies alone.
> Having said this, I think there is a place for observational studies
> to be considered in conjunction with RCTs, especially in areas of
> medicine (such as surgery) where RCTs may be more difficult to
> implement well.
>
> Carolin
>
> Dr. Caroline Boulind
> Clinical Research Fellow
> 01935 384559e
>>>> "Djulbegovic, Benjamin"
>>>> <[log in to unmask]<mailto:[log in to unmask]>>
>>>> 28/01/2011 15:06 >>>
> Dear all
> I'd like to post this question to the group that I have been
> thinking about for some time... Is there a scientific method that
> allows us to LOGICALLY distinguish the cause-effect from the
> coincidence? David Hume, one of the most influential philosophers of
> all times, concluded that there is no such a method. This was
> before RCTs were "invented". Many people have made cogent arguments
> that (a well done) RCT is the ONLY method that can allow us to draw
> the inferences about causation. Because this is not possible in the
> observational studies, RCTs are considered (all other things being
> equal) to provide more credible evidence than non-RCTs. However,
> some philosophers have challenged this supposedly unique feature of
> RCT- they claim that RCTs cannot (on theoretical and logical
> ground) establish the relationship between the cause and effect any
> better than non-RCTs. I would appreciate some thoughts from the group:
> 1. Can RCT distinguish between the cause and effect vs.
> coincidences? (under which -theoretical- conditions?)
> If the answer is "no", is there any other method that can help
> establish the cause and effect relationship?
> I believe the answer to this question is of profound relevance to EBM.
>
> Thanks
> Ben Djulbegovic
>
>
>
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