changing times in pharmaceutical statistics: 2000-2020

PHARMACEUTICAL STATISTICS

Pharmaceut. Statist. 2002; 1: 75–82 (DOI:10.1002/pst.019)

Changing times in pharmaceutical

statistics: 2000–2020z

Simon Day*,y

Leo Pharmaceuticals, Longwick Road, Princes Risborough, Buckinghamshire, HP27

9RR, UK

In a previous paper we considered how pharmaceutical statistics had changed between 1980 and 2000.

In this paper we go on to consider some of the likely influences and changes in the world of

pharmaceutical statistics over the next twenty years. Statistical research by pharmaceutical

companies is supported, although its direct value in boosting company profits is challenged. The

duality between fraud and conflict of interest is explored, particularly with regard to data and safety

monitoring boards. Political correctness within clinical trials and (allegedly) better patient-oriented

outcomes is questioned and the whole notion of intention-to-treat and per-protocol analyses is rejected

in favour of pragmatic trials and explanatory trials. The dominance of SAS software within the

industry is explored and the possibility for change considered. Finally, whilst the future of

pharmaceutical companies is not addressed, the impact of future mergers, collaborations and take-

overs between clinical research organizations is considered. Copyright # 2002 John Wiley & Sons

Ltd.

INTRODUCTION

In the first issue of Pharmaceutical Statistics Iconsidered some of the changes that have occurredduring the last twenty years [1]. I have no means ofknowing what will happen in years to come (this isnot the section on fraud I alluded to in that issue).I do, however, present some topics that I think

might (or should) change within my workinglifetime.

SCIENTIFIC EXCELLENCE

Let me address a topic which ought to add to ourcredibility – though it does not seem to have doneso. The topic is that of the scientific excellence ofindustry-employed statisticians. For the purposeof this discussion, I take recognition of thatexcellence to be (surrogately) measured by aca-demic recognition and the proliferation of profes-sorships, visiting professorships and lectureshipsawarded to statisticians in the pharmaceutical

Copyright # 2002 John Wiley & Sons, Ltd.

*Correspondence to: Simon Day, Medicines Control Agency,Room 13-205, Market Towers, 1 Nine Elms Lane, LondonSW8 5NQ, UK.

yE-mail: [email protected] paper is an extract from a talk given at the PSI meeting

on Statistics in Drug Development at the Royal Society forArts, London, 8–9 March 2000. The first half covering the pasttwenty years (1980–2000) was published in the first issue.

industry. As well as academic appointments, wehave statisticians working for the pharmaceuticalindustry writing and publishing statistical metho-dology papers and textbooks – even dictionariesfor clinical trials! This all adds to the credibility ofstatistics and statisticians working within theindustry but, sadly, it seems only in the eyes ofthe rest of the pharmaceutical industry and ofother statisticians. In general we (as statisticians)fail at promoting our excellence and that of ourcompanies to the lay public or the wider scientificcommunity. Of course, it is not solely ourresponsibility to enlighten the public, but I believewe could help a bit more. That said, I am not surethat our employers necessarily help us. None ofthe appointments I describe above (mine included)seem to be actively supported by our employers –rather they are passively supported. There is noformal agreement between university and com-pany for any agreed (two-way) exchange of time orexpertise. It seems very much dependent on thewillingness of the individuals to take on somethingover and above their basic job description, and onan unwritten agreement from companies to offersome time and perhaps travel expenses to allowsome of their valued staff to better themselves atthe expense of direct work aimed at boostingcompany profits. Let us not forget what we are allhere to do.

There are some formal links to the universities.In the UK we have two chairs of statistics fullyfunded by pharmaceutical companies, sustainingpharmaceutical sponsors such as Astra Zeneca,Aventis, GlaxoWellcome, Pfizer Central Researchand Roche Products of Research Units such as theMedical and Pharmaceutical Statistics ResearchUnit – and sponsorship of MSc courses atuniversities.

All of these companies are either demonstratingcommitment to their social or moral obligations tosociety or a more selfish ulterior motive becausethey believe that for a relatively modest expendi-ture they may get back some commercial advan-tage. Probably it is a bit of both – a win–winsituation – but undoubtedly it is a long-term view.Some companies have even grown their own,internal, statistical research groups. Merck has had

one for a long time; SmithKline Beecham hasrecently set one up. The motivation for setting upthese groups is varied and multidimensional butthey are certainly expensive and, again, are likelyto serve only a long-term view. Drug developmentis, of course, a long-term, high-risk business – aswe all know. Can a statistical research departmentsignificantly impact on that time scale? I am notconvinced that it can, at least not in the sense ofstatistical research. But ‘research department’ maybe a euphemism for many other things, and I thinkthere is value to be gained from assigning staff(statisticians and others) to non-project work. Thiscan include general advice-giving (‘consultancy’),self-development, training for non-statisticiansand, indeed, training for statisticians. Having thetime to be able to keep up to date with currentmethods is not a luxury but a necessity [2]. Havinga dedicated group filtering the wheat from thechaff in the statistical literature – and then passingthis information on – might, for some companies,be a means of achieving that. Every statistician(indeed, every non-statistician too) needs x% oftheir time dedicated to direct project work but animportant, even if minority ð100� xÞ%; of theirtime dedicated to moving themselves, their com-panies and the industry forward. The balance willchange with time and will vary across companies,but I hope that in no company is it 100% projectsand 0% development. So, to my mind, statisticalresearch by pharmaceutical companies may not begood direct value for money: can we, then, asstatisticians, carry out our roles better? We cancertainly get it wrong and not be well enoughprepared with resources and preparatory workwhen needed; we can certainly fail to make bestuse of current (sometimes quite basic) statisticalmethods. But my view is that, in most companies,the time that could be shaved off (even just fromthe clinical phase of) a project by statisticiansproducing new statistical methods is usuallyminimal compared to other activities. I do notwish to give the impression that there is no roomfor further statistical development to benefit us,but if the pharmaceutical industry wants toimprove its efficiency there are better ways ofdoing it: better ways to get a ‘bigger bang for your

Copyright # 2002 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2002; 1: 75–82

76 S. Day

buck’. If we, as statisticians, are going to have agreater impact then we, as pharmaceutical com-panies, certainly need to use better projectmanagement, resource management and pro-gramme management expertise, and that is anarea where many statisticians would be very wellplaced to offer expertise. I suspect that few of uscontribute much to our companies in this area. Ipredict use of such methods will increase but onlyvery, very slowly.

To support this view I would ask how manycompanies routinely use O’Quigley’s very well-known continuous reassessment methods [3] fordose-finding studies – certainly some, but notmany. Read, for example, K.aall!een’s paper on dosefinding [4] and ask the same question. How manycompanies make best use of hierarchical models oreven meta-analyses? I remember some excellentwork on experimental design presented by RogerMead at the PSI conference in Liverpool [5,6]. Hewas trying to get us to use ‘surplus’ (myparaphrasing) residual degrees of freedom toinvestigate other therapies. Do we use it? Usuallynot. Ebbutt [7] recently reviewed the statisticaljournals most likely to offer us interesting anduseful new methods, but found only a smallproportion of such papers. He went on to suggesta list of areas where companies might benefit bysponsoring research (in whatever way). I largelyagree with his list in principle, but the medicalcommunity to whom we try to sell (ideas and data)is very conservative. We need to question what wedo (and spend) over and above what is required ornecessary. Being first to market with a newchemical entity is commercially advantageous;being first to registration with a new statisticalmethod may not be.

CONFLICT OF INTEREST

Further on the topic of our public image, let mecomment briefly on conflict of interest. No one isindependent. When I worked as a consultant it wasquite clear to me that a positive result ðP50:05Þ ora negative result ðP > 0:05Þ made no difference to

whether I was paid or retained to work on furtherstudies. However, it was equally clear to me thatfor some clients (I hasten to add, not all) asuccession of negative results would lead to me notbeing retained to do further work. I predict a caseof fraud, wrapped up with conflict of interest, byan ‘independent’ person (statistician or other)retained to sit on a data and safety monitoringboard (DSMB). Such a case could totally discreditall DSMBs. Consider a member of such acommittee. The trial is nothing to do with his orher place of work. The committee member is fullyindependent of the trial and the sponsor. Meetingsare infrequent but involve several hours’ flight toget to. Now if this were an attractive scenario – thefrequent flyer programme, the champagne inbusiness class, the duty-free goods (oh yes, andthe nice little fee) – then one might, in the case of avery finely balanced decision, just be tempted infavour of voting to continue the study rather thanvoting to stop it. If, however, it were not soattractive – the length of the journey, the timeaway from the family (oh yes, and the lousy fee) –then one might, again in the case of a very finelybalanced decision, be tempted more in favour ofvoting to stop the study. But of course, thecommittee member is independent. None of thesethings will influence him or her, will they? Asidefrom such immediate and obvious greed orpersonal comfort, the potential for using informa-tion from a DSMB to influence share-buying or tofurther one’s own research must inevitably be toogreat for someone. I hope that particular someonedoesn’t sit on your DSMB. Even taking fraud outof the picture, let me reinforce that ‘conflict ofinterest’ does not equate to dishonesty or fraud: itequates to a conflict, or ‘competing interests’ as theBritish Medical Journal [8] has termed it. In toomany minds, such a conflict is becoming seen asthe first nail in the coffin of a fraudster. In [1] Ireferred to a news item from the British MedicalJournal [9] about drug company bosses beingjailed. This contains a statement about‘supposedly independent academics’. A formerchairman of the Medicines Commission has evenpublicly set out in the Lancet the impossibilityand, indeed, lack of desirability of independence

Changing times: 2000–2020 77


from the pharmaceutical industry of membersof the Commission [10]. No one, I repeat, isindependent.

POLITICALLY CORRECT CLINICALTRIALS

I see the possibility for the entire philosophy ofclinical trials to be shaken up. Along with therebellion against the all-knowing medical profes-sion and the growing knowledge and pressurefrom the general public there is also a challenge toscience and the scientific method. Much of this isfuelled by the Internet. Much of it is fuelledappropriately by the Internet; much of it is justfuelled by the Internet. Whatever the fuel, some ofit is patient-powered [11]. Some of it is physician-powered [12, 13]. Some of it is led by broad-thinking academics wishing to open discussion onchanges for the future [14–17]. In some ways, thesechallenges to our scientific method are a goodthing since we can all slot too comfortably into arut. A shock to the system is not such a bad thingonce in a while. But what we have is not so bad,and the fact that it is not perfect is no reason tojump ship in favour of an unexplored butpresumed Treasure Island. There are already somebeginnings in sight. The Congress of the UnitedStates of America mandated upon the NationalInstitutes of Health the equal inclusion of womenand minorities into all their clinical trials. Thesituation is clearly set out by Piantadosi andWittes [18]. Armitage [19] has taken a less politicalstance.

Further examples are beginning to creep in withthe introduction of more ‘patient-oriented’ end-points. This seems a perfectly reasonable andindeed desirable move, but it has to be introducedcarefully. In a note in Controlled Clinical Trials[20] I questioned the appropriateness of summar-izing peak flow measurements in respiratory trialsas the ‘best of several’ or the ‘mean of several’. If Iwant to see how well a patient can do I want thebest score – but when I assess efficacy in a phaseIII clinical trial I am not interested in individual

patients per se. This direction of interest certainlydoes not carry over to considerations over thedesign of the study or to aspects of safety, but for atypical phase III efficacy trial I am interested intreatments and only subsequently, therefore, intheir benefit to patients. Such phase III efficacytrials are about treatment policies, not the besttreatment for any individual patient [21]. I mightbe able to compare treatments more efficiently bysummarizing as means. Stevens [22] respondedconcluding that so many organizations use thepeak value that it is likely to continue as thepreferred approach – ‘unless of course they see thelight of Day’. A similar exchange of thoughtsbetween McKellar [23] and Appel [24] appeared inthe same journal. The phrase ‘most relevant aspectof the clinical state’ was used by Appel. I justchallenge whether we should necessarily use the‘most relevant aspect of the clinical state’ when wecompare the efficacy of treatments in a trial.

CAN IT WORK? DOES IT WORK?

I would like to see the end of the terms ‘intention-to-treat’ (ITT) and ‘per protocol’. I am strongly infavour of pragmatic trials and (where appropriate)explanatory trials, but in many (possibly most)cases a trial cannot be both at once. The terms‘pragmatic’ and ‘explanatory’ are probably bestknown from Healy’s translation of Schwartz,Flamant and Lellouch’s book [25]. That transla-tion was published over 20 years ago. The originaltext, written in French, is ten years older. Thefundamental principles were published even beforethat [26]. Recently Hollis and Campbell [27]published a survey of how different authors hadimplemented so-called ITT analyses. ‘Many andvaried’ would have to be an appropriate summarycomment: protocol violators were sometimesincluded, sometimes excluded; patients with miss-ing data were sometimes included, sometimesexcluded; patients withdrawing early were some-times included, sometimes excluded – all suchexamples are described. So-called ITT analysesand per-protocol analyses seem to me often


78 S. Day

nothing more than ill-thought-out sensitivityanalyses of a less than ideal study design. I am infavour of sensitivity analyses, but I am more infavour of designing and running pragmatic trials;or at least knowing if we are trying to answer the‘does it work?’ question or the ‘can it work?’question [19, 28]. Let me give you a nice quotefrom Muir Gray, Director of the Institute ofHealth Sciences at Oxford, about ‘efficacy’ versus‘effectiveness’: ‘ ‘‘efficacy’’ is what you get at SloanKettering; ‘‘effectiveness’’ is what you get atKettering’. I think that sums up nicely theimportance of pragmatic trials rather than theimportance of ITT analyses or populations.

STATISTICAL SOFTWARE

One particular vendor has been very successfulover the last 10–20 years in selling statisticalsoftware to the pharmaceutical industry – and tothe phase II and III arenas, in particular. Thatvendor is, of course, SAS1 [29] (formerly, but notnow, known as Statistical Analysis System).Shannon [30], for example, has stated that ‘SASsoftware is the de facto standard for processingclinical trials data’. How has SAS been sosuccessful and, in particular, might things changein the future?

SAS has not been successful because its productsare in any sense ‘approved’ by or ‘required’ by theFood and Drug Administration (FDA). There hasbeen much erroneous hearsay concerning suchalleged approval and requirement, and this willundoubtedly have helped the marketing people atSAS. I would point out though, in their defence,that I have never heard anyone employed by SASmake either of these claims. ‘Approval’ has beencategorically and publicly refuted on severaloccasions. ‘Requirement’ is a slightly differentmatter. Requirement to use SAS for analysessubmitted to FDA has been categorically andpublicly refuted, although some statisticians insome divisions may request that data sent to thembe in an SAS format. However, there is noregulatory need to use SAS software. So why has

it and why does it continue to sell so well? Twomajor factors are that it is of good quality andcomprehensive. There may be other softwarepackages that are of equally high quality, and insome areas probably even greater quality. How-ever, the inconvenience of needing to movebetween different packages to carry out differentanalyses is high and each package has its own(often steep) learning curve (SAS included). One ofthe greatest attributes of the SAS system, andwhere it scores highly over many of its rivals, is itsdata management and data manipulation facilities.Simple calculations of derived variables (such asbody mass index from weight and height) presentno challenge. Transforming datasets between‘normalized’ and ‘denormalized’ forms, complexconditional calculations, date–time handling, andso forth, all present more substantive challenges.SAS will usually rise to such challenges whereother packages may not, and this is a vitalrequirement for any working statistician. We arerarely presented with neat and tidy datasets in theform needed for all the required analyses, and thisdata management capability has, I suggest, greatlyadded to the usefulness of SAS.

But what of statistical software in the future?The success of SAS in the past has, I believe, beenpartially fuelled by the weakness of many of thedata management tools that we have had to use.Even the best have often been the ‘best of a badbunch’. Today, and in the future, that is changing.With modern data management software – typi-cally, though not necessarily, based on Oracle1 –statistical software may often be able to ‘tap in’ todata in an appropriate format and structure to beable to produce the required analyses, tabulations,listings, etc. In parallel, new graduates are oftencoming to companies with knowledge of packagesother than SAS and so the learning curve (at leastfor some people) is not so steep. Of course, manycompanies will not want to move away from SAS,and until recently such a move would not havebeen possible – both for commercial and technicalreasons. Times are changing, and it might bepossible in the future to make such a move.Although Shannon [30] described SAS software asthe ‘de facto standard’, in the same sentence he



also said that ‘there are about as many ways ofdoing things as there are people who do them’.Each company will have to consider the costs andbenefits of ‘staying’ or ‘moving’.

CONTRACT RESEARCHORGANIZATIONS

The future of pharmaceutical companies withregard to mergers, buy-outs and take-overs wouldbe an obvious topic. Presumably there is anasymptote of two pharmaceutical companies;antitrust legislation and the Monopolies andMergers Commission will prevent the numberfalling to one. Beyond this, it is a topic which Ishall avoid. The situation with contract researchorganizations (CROs) is similar, although it has anextra dimension to it.

Undoubtedly, CROs will continue (as willpharmaceutical companies) to merge, acquire andotherwise increase their size and therefore dom-inance. CROs can be as large as pharmaceuticalcompanies (in terms of headcount) but, unliketheir clients, they can be quite small, even down toindividuals working alone. Across this spectrumthere seems every reason why, given time, thelarger organizations will continue to grow. Atpresent, however, some of the benefits (perceivedby the customer and by some of the larger CROs)of smaller CROs over larger ones include greaterflexibility, lower price and a more individualservice. Given such a favourable opinion regardingcertain mid-sized CROs, they become obviousitems on the shopping list of the larger companies.Sadly, what is all too inevitable is that, althoughthe people do not change, as soon as a take-overhappens, the lower price needs to be abolished infavour of the (necessarily) higher prices of thebuying company; the flexibility needs (necessarily)to be replaced by the rigidity imposed by largerorganizations; and the individual contact is also(necessarily) replaced by the possibility that manymore different people may pick up the telephoneor work on ‘your’ project. This is certainly notalways the case, but there are examples where

either it has occurred or, equally importantly,potential customers have perceived that it hasoccurred. The benefits wanted by the larger,buying, company might not then be realized.

Stepping down the chain to consider the ‘verysmall’ CROs, the future, I think, looks bleak.Some have survived in the past, but most have not.The recipe for success is elusive: personality,contacts and luck probably all play a part, as wellas technical and business skills. But with more andmore good clinical practice constraints it becomesever harder for such one-man bands to succeed.Ironically, it seems that the most successfulindividuals are those who do not need to besuccessful: those whose mortgage is already paidoff; those who perhaps have a spouse with a steadyjob; or those lucky enough to be supported by apension after early retirement. These people canafford (financially) to say ‘no’. Others cannot andmay be forced to take on work for which they arenot ideally suited or forced to take on too muchwork for fear of not knowing when the nextcontract may be secured. Demise follows. In theend, the customer (the pharmaceutical company)and the ‘ultimate customer’ (the patient) will loseout. Choice will be diminished.

CLOSING COMMENTS

The ‘future of statistics’ [31] has been speculatedon elsewhere, and I particularly recommend read-ing the thoughts of Gehan [32] and Aalen [33], butoverall I end these thoughts with a suggestion toread the Pharma 2005 document prepared byPricewaterhouseCoopers [34]. I commented earlierthat when I compared treatments in an efficacytrial I am ‘not interested in patients per se’.Pharma 2005 explores ideas and reasons behindneeding to be able to target treatments toindividuals rather than making more generalpolicy statements [21] about the relative benefitsof various treatments. Interest may need to changefrom treatments to patients.

I paint a picture of change in the future. Changewill be the status quo and ‘It’s never too late to


80 S. Day

change it’ [35]. But all I have done is describe smallincrements. What we need to be wary of are large,sudden changes. These might be the result of thetide of reaction against science and scientists(including doctors) that I alluded to earlier; theymay be the result of a major shift in the regulationof medicines (the prospect of regulatory autho-rities conducting some of the phase III work hasrecently been considered [16, 17]); or the result offundamentally new ways of identifying the me-chanisms of how medicines work, potentiallymaking clinical trials in the context that wecurrently know them completely unnecessary.

History tells us that the future is notoriouslydifficult to predict: Lord Kelvin is reported to havedropped a few clangers in the guise of ‘Radio hasno future’ and ‘Heavier-than-air flying machinesare impossible’ [36]. Bill Gates of Microsoft isprobably still stumbling over his statement that he‘couldn’t conceive of anybody needing more than640 Kb of memory in their computer’. Ken Olsen,however, then president of the Digital EquipmentCorporation, can still happily (in my view) standby his 1977 statement that ‘There is no reason forany individual to have a computer in their home’.

Major changes as well as minor changes willundoubtedly take place. We should expect them todo so; but we should also expect them to take usby surprise when they happen. We all must beprepared to expect the unexpected [37].

ACKNOWLEDGEMENTS

The thoughts presented here and in the previous paperhave been moulded over many years through manyconversations with many people; I thank them all andhope those conversations and speculations will continue.Specific thanks for helpful comments regarding thesepapers are due to Claus Bay, Signe Birk Jensen,Professor Stephen Evans, Professor Andy Grieve,Darren Jolliffe, Professor John Lewis, David Lowson,Tony Rees, Dr James Roger, Professor John Whiteheadand Dr Zo.ee Williams. The views expressed are notnecessarily, however, the views of any of those persons. Ialso thank the PSI Scientific Committee for inviting meto give these views at the meeting on Statistical Issues inDrug Development and those at the meeting for theirthoughtful and searching questions.

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changing times in pharmaceutical statistics: 2000-2020

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