When are observational studies as credible as randomised trials? Ethical challenges in cluster randomized controlled trials: experiences from public health interventions in Africa and Asia. Evidence-based surgery: interventions in a regional paediatric surgical unit Developing and Testing Changes in Delivery of Care Evaluating the effectiveness of using PROs in clinical practice: a role for cluster-randomised trials Impact of a priority-setting consultation on doctor-patient agreement after a geriatric assessment: cluster randomised controlled trial in German general practices. Sham Acupuncture Devices – Practical Advice for Researchers Fusing Randomized Trials With Big Data: The Key to Self-learning Health Care Systems? Inpatient general medicine is evidence based. A-Team, Nuffield Department of Clinical Medicine. The effect of compensation claims on outcomes after injury. Beta-carotene: a cancer chemopreventive agent or a co-carcinogen? A multisensory investigation of the functional significance of the “pain matrix” The relationship between general practitioners, hospital consultants and community nurses when caring for people in the last year of their lives. Evidence based general practice: a retrospective study of interventions in one training practice Doctor-patient interaction in a randomised controlled trial of decision-support tools. Community-led trials: Intervention co-design in a cluster randomised controlled trial Care in specialist medical and mental health unit compared with standard care for older people with cognitive impairment admitted to general hospital: randomised controlled trial (NIHR TEAM trial)
Observational studies have a record of extremely successful contributions to medicine . They are essential for our knowledge about causes and pathogenesis — eg , genetic , environmental , or infectious causes of disease . Additionally , for medical practice we rely on observational studies of prognosis and diagnosis . Nevertheless , over the past years , we have seen recurrent debates about the merit of observational versus r and omised research . The debates have been fuelled recently because of seeming total failures , in which the results of observational studies were completely overturned by r and omised studies . Hormone replacement therapy showed protection from myocardial infa rct ion in observational studies , but a small increase was seen in r and omised trials ; a similar reversal happened for carotene and lung cancer . Such discrepancies raise the question : in what circumstances can observational comparisons be as convincing as r and omised experiments ? To answer that question : I will first recall what is expected from r and omisation . I will then describe two specific issues , adverse effects of drugs and genetic causes of disease , to eluci date under what circumstances evidence from observational research is as good as that from r and omised trials . This description can be generalised to other areas of clinical and epidemiological research , and leads to a proposal for a three-pronged restriction to give observational research the best chance to be as credible as r and omised controlled trials . Benefits of r and omisation Two benefits are expected from r and omisation : unbiased allocation of treatment , because of easier concealment of the allocation scheme , 1 and application of statistical theory on the basis of r and om sampling . Public health interventions usually operate at the level of groups rather than individuals , and cluster r and omized controlled trials ( RCTs ) are one means of evaluating their effectiveness . Using examples from six such trials in Bangladesh , India , Malawi and Nepal , we discuss our experience of the ethical issues that arise in their conduct . We set cluster RCTs in the broader context of public health research , highlighting debates about the need to reconcile individual autonomy with the common good and about the ethics of public health research in low-income setting s in general . After a brief introduction to cluster RCTs , we discuss particular challenges we have faced . These include the nature of - and responsibility for - group consent , and the need for consent by individuals within groups to intervention and data collection . We discuss the timing of consent in relation to the implementation of public health strategies , and the problem of securing ethical review and approval in a complex domain . Finally , we consider the debate about benefits to control groups and the st and ard of care that they should receive , and the issue of post-trial adoption of the intervention under test OBJECTIVES To determine the proportion of paediatric  surgical interventions that are evidence -based and to identify areas where r and omised controlled trials ( RCTs ) or further research are required . DESIGN Prospect i ve review of paediatric general surgical in patients . SETTING A regional paediatric surgical unit . SUBJECTS All consecutive paediatric general surgical patients admitted in November , 1995 . MAIN OUTCOME MEASURES Each patient on whom a diagnosis had been made was allocated a primary diagnosis and primary intervention ( n=281 ) . On the basis of expert knowledge , Plusnet Medline , and ISI Science Citation data base search es , each intervention was categorised according to the level of supporting evidence : category 1 , intervention based on RCT evidence ; category 2 , intervention with convincing non-experimental evidence such that an RCT would be unethical and unjustified ; category 3 , intervention without substantial supportive evidence . RESULTS Of 281 patient interventions , 31 ( 11 % ) were based on controlled trials and 185 ( 66 % ) on convincing non-experimental evidence . Only 23 % of interventions were category 3 . CONCLUSIONS In common with other medical specialties , the majority of paediatric surgical interventions are based on sound evidence . However , only 11 % of interventions are based on RCT data , perhaps reflecting the nature of surgical practice . Further RCTs or research is indicated in a proportion of category 3 interventions The patient 's problem is recurrent cough . Pulmonary function is normal at the moment , but the patient 's symptoms and family history suggest asthma . Three physicians confer . Physician A suggests inhalant therapy . It 's almost surely asthma , he says . We should begin treatment and move on to the next patient . The best therapy for asthma is an inhaled steroid . Case closed . Wait a moment , says Physician B. You ca n't be so sure that the best therapy is a steroid . -adrenergics may be better . We need to enroll this patient in a r and omized , double-blind trial to increase our certainty about the best medication . Let 's get informed consent and draw lots to assign the patient r and omly to a treatment group . I 've got a better plan than either of you , says Physician C. Our task is to find out what will help this patient , recognizing our uncertainty about both the diagnosis and the best treatment . Let 's run a small-scale test . We 'll begin with the assumption that the patient has asthma and , on the basis of the scientific literature , we 'll start with inhaled steroids . We 'll ask the patient to keep a record of her symptoms and to try steroids for a week , and then we 'll review the situation to see what we 've learned . If the steroid helps , we can keep it ; if not , we can change the dose or test other options . We can imagine circumstances in which each of the three physicians sounds wise . If the diagnosis is certain , the therapy is well established , and the risk for error is low , Physician A 's approach sounds fine . Why belabor the obvious ? Appendicitis ? Appendectomy . Case closed . But if the science is uncertain and the risks are high , the best course may be to invest in more scientific knowledge , and Physician B sounds right . Take , for example , the choice of chemotherapeutic agents for leukemia . Before one protocol is established as the best choice , responsible care may involve the enrollment of patients in carefully design ed r and omized trials . This approach always requires that physicians obtain informed consent and keep the individual patient 's needs and wishes foremost , but it includes the growth of general clinical science as major goal . For some questions , formal research design s other than r and omized , controlled trials ( such as quasi-experimental , casecontrol , and cohort studies ) can provide highly satisfactory answers . But most often , physician and the patient meet on a terrain that is neither of certainty nor of scientific ignorance , and the use of either large-scale , formal research studies or point- and -shoot therapy seems inappropriate . The art of medical care involves a continuing , individualized search in which the physician tries to match incomplete scientific knowledge of disease and treatment with incomplete local knowledge about particular patients at particular times in their lives . In caring for patients , we often do not know the way but find the way , step by step . Thus , Physician C sounds wisest to most persons most of the time . So it is with improvement in general , whether the subject is caring for an individual patient or finding a better system of care . Between once- and -for-all action and formal research design lies a vast terrain in which steady investigation in the form of small-scale tests of change can be useful . Children learning to ride a bicycle call what they do practicing . So do physicians giving patient care . In the jargon of quality improvement , this form of learning in action is called the Plan-Do- Study -Act ( PSA ) cycle . This cycle , also called the Plan-Do-Check-Act cycle , is attributed to Walter Shewhart , founding theorist of the field of statistical process control ( related models can be found in the work of John Dewey and others ) . Specific tests in cycles of action and reflection are at the heart of modern approaches to improvement in organizations , just as they are at the heart of modern approaches to education and learning [ 1 ] . The apparent simplicity of the PSA cycle is deceptive : The cycle is a sophisticated , dem and ing way to achieve learning and change in complex systems . Rationale for Plan-Do- Study -Act Cycles The strong rationale for the use of PSA cycles in the process of improvement comes largely from systems theory , which was explored in the first three papers in this series [ 2 - 4 ] . Briefly , a system is a set of interdependent elements interacting to achieve a common purpose .  Bicycle riding  is a system ; so is care of a patient with asthma . From the viewpoint of prediction , systems are unruly . If you change a single element of a system , you may be surprised by the result [ 5 ] . A tiny turn of the h and lebars of a new bicycle can leave a young rider sprawling on the asphalt , bewildered by the invisible connections between her h and s and her balance . A physician who dem and s that his special suture material be placed regularly on an otherwise st and ard surgical set-up tray may find that the same suture later appears on other trays , where it is not needed . Complexity mounts and errors occur as complex , nonlinear systems yield irrational effects from separately rational causes . An accumulation of reasonable changes produces an unreasonable mess . In formal science-planned experiments and tests of hypotheses that physicians are trained to value as investigators and to consume as journal readers- research ers try to mitigate the unruliness of systems by gaining experimental control . R and omization is one way to do this . R and om selection spreads messy system noise evenly , allowing the one thing of true interest-the independent variable-to shine its signal clearly through . The larger the sample , the more reliably the noise is evenly assigned and the clearer the signal of effect ( if any ) . When r and omization is not possible , formal science has other ways to h and le system noise . Epidemiologic methods can help stratify potential confounding factors , either statistically or by design . Cohort studies can use time trends to advantage , and casecontrol design s can highlight the effects of exposures . But how do these formal methods for inference from data apply in the world of PSA cycles ? Suppose that we ask a physician why she has just discontinued penicillin treatment in a patient who developed hives while receiving the drug . Because she is allergic to the medicine , the physician replies . But you do n't really know that , we counter . You need to prove it . Here 's how . Give the patient  penicillin on 20 different days , r and omly interspersed with 20 days without penicillin . Record the presence or absence of hives each day , draw a two-by-two table , and calculate the Fisher exact test for the null hypothesis . To a good physician , we would sound silly . There is nothing wrong with the Fisher exact test or with examining the null hypothesis , but in this scenario , these tools are out of place . I do n't have to do all of that , the physician would say . I 'm sure enough , and for this patient we have plenty of good alternative therapies as options . In drawing her conclusions about which medicines to use , this physician is no less a scientist than we are with our Fisher exact test . She is an inductive scientist , learning constantly from experience reflected upon and informed by her intimate association with the care of this specific patient . She tries , she observes , and she learns through her own PSA cycles . R and omized , controlled trials would not help much ; on most occasions , they would waste her time . The PSA cycles-short-cycle , small-scale tests linked to reflection-are powerful tools for learning in complex systems when the aim is to improve those systems . They are most helpful when inaction seems inappropriate but action without reflection seems unwise . In health care , inaction is inappropriate when practice variation is so great that not all practitioners can be right , when practice departs from scientific knowledge or trusted experience , when a system is performing poorly relative to its potential , or when inaction by some creates an opportunity for action by others with less knowledge or bad motives . Research tells us , for example , that breast-conserving surgery for breast cancer is underused in the United States [ 6 ] and that most elderly persons with myocardial infa rct ion do not receive -blocker therapy in their post-acute management [ 7 ] . These findings call for action : St and ing still is not an acceptable response , but how can we act with prudence as we ab and on the status quo ? Good physicians use learning cycles frequently as they depart from the status quo in sound clinical work . The use of a medication in an individual patient is best approached as a PSA cycle . We know from formal experiments how a drug will work in general against a given disease , but we always use that drug with the underst and ing that its action may differ in particular patients . Try this , says Physician C , and stay closely in touch . Small-scale trials are usually the best approach because systems are unpredictable and their dynamics are nonlinear . Small changes can have large consequences , remote in space and time ; this makes closed-form solutions and secure predictions difficult or impossible . In health services research , a corollary has been that efficacy ( performance of a treatment or device in the laboratory ) is almost always greater than effectiveness ( performance of the same treatment or device in the field ) [ 8 , 9 ] . Research ers in laboratories take pains to isolate the intervention being studied ; practitioners in the field can not avoid the linkage of that intervention to uncountable additional nonr and om influences . This is not to deny the enormous contribution of formal experimental science to knowledge . When we are uncertain about the benefit of a particular drug or operation relative to that of its alternatives , no investigational design can yield a more confident conclusion than the r and omized , controlled trial . In effect , a r and omized , controlled trial is a superb form of final inspection of a well-crafted , unitary change . But the same is Many of us believe there are major benefits to be gained by using  patient-reported outcomes  ( PROs ) in routine clinical practice . However , demonstrating tangible benefits has frequently proved elusive . Although r and omised clinical trials ( RCTs ) have become accepted as the st and ard for comparing alternative forms of clinical interventions , when evaluating the effectiveness of PROs there are a number of challenges both in terms of study design and the subsequent analysis . Despite this , to date , of those investigators who have used RCTs , relatively few have used anything other than simple two-group r and omisation and comparisons . Most of these trials have also failed to demonstrate convincing benefits to patient outcomes . We suggest that the use of PROs may result in modest yet important improvements to patient outcomes , and that these benefits may be obscured in conventional individual patient trials because of contamination effects . The advantages of alternative design s such as cross-over studies and in particular cluster-r and omised trials are illustrated BACKGROUND General Practitioners ( GPs ) often have to simultaneously tackle multiple health problems of older patients . A patient-centred process that engages the patient in setting health priorities for treatment is needed . We investigated whether a structured priority- setting consultation reconciles the often-differing doctor-patient views on the importance of problems . DESIGN Cluster r and omised controlled trial with 40 GPs and their 317 consecutively recruited older patients . PROCEDURE Following a geriatric assessment , patients and doctors independently rated the importance of each uncovered problem . GPs then selected priorities with their patients in a consultation . Trained intervention GPs held a structured consultation and utilised the list of uncovered patient problems with their importance ratings to agree priorities . Untrained control GPs only used the patient 's problem list without importance ratings . MAIN OUTCOME Doctor-patient agreement on independent importance ratings two weeks after the priority- setting consultation . ANALYSIS Weighted kappa ( κw ) and multilevel logistic regression model . RESULTS Intervention GPs and their patients determined mutual priorities for 20 % of individual problems . In this process , GPs often succeeded in convincing their patients of the importance of vaccinations , lifestyle and cognitive issues . Likewise , patients convinced their GPs to prioritise their social and functional problems . Further treatment plans ensued in 84 % of these priority areas . The regression model adjusting for clusters and baseline characteristics did not demonstrate significant differences in doctor-patient agreement on problems between groups a two weeks later . CONCLUSION Differing views on the importance of health problems between GPs and older patients were not sustainably reconciled . The special consultation facilitated identification of priority problems for treatment despite differences in perceived importance of problems between patients and GPs . German clinical trials register drks 00000792 Several vali date d sham acupuncture devices have recently become available . While some debate exists on whether such needles are the best placebo control for an RCT of acupuncture , practical advice based on research experience is missing from the literature . This paper shares our concrete experience using the most commonly used such sham needle ( the ‘ Streitberger needle ’ and its paired verum needle ) in a large RCT ( n=135 ) which included a two-week run-in period . The placebo run-in gave us an opportunity to use the sham device on all participants , who were then re-r and omised to receive genuine acupuncture or to continue treatment with the device . The blinding was successful both at the end of the run-in and at the conclusion of the trial despite the re-r and omisation . We also report our experience with the sham needle in neuroimaging experiments where the magnetic machinery poses considerable challenges for acupuncture research R and omized clinical trials ( RCTs ) have revolutionized medicine by providing evidence on the efficacy and safety of drugs , devices , and procedures . Today , more than 40 000 RCTs are reported annually , their quality continues to increase , and oversight mechanisms ensure adequate protection of participants . However , RCTs have at least 4 related problems : ( 1 ) they are too expensive and difficult ; ( 2 ) their findings are too broad ( average treatment effect not representative of benefit for any given individual ) and too narrow ( trial population and setting not representative of general practice ) ; ( 3 ) r and omizing patients can make patients and physicians uncomfortable , especially when comparing different types of existing care ; and ( 4 ) there are often long delays before RCT results diffuse into practice . The new alternative is “ big data . ” Because medical care is increasingly digitized in electronic health record ( EHR ) data sets and linked biological and genetic data banks , proponents suggest that health care systems are at the dawn of an era in which a patient ’s prognosis and optimal therapy will be generated from rapid analysis of these data sets using sophisticated machinelearning strategies . The information is relatively inexpensive , generated as a by-product of patient care ( overcoming the cost problem ) , and both specific to individuals ( ie , adequately narrow ) and , en masse , descriptive of the entire delivery system ( ie , adequately broad ) . No individuals are r and omized , so the ethical issues appear less complex . The richness and immediacy of these new data could allow tailored treatment decisions in real time , overcoming delays in knowledge translation . As such , although the RCT remains the gold st and ard for evaluation of experimental therapies , big data is proposed as a better approach for the broad swath of comparative effectiveness questions that arise in clinical practice . Indeed , the Institute of Medicine envisions big data as the engine for so-called learning health care systems.1 Yet big data is not a replacement for the RCT . The singular beauty of the RCT is the strength of causal inference that arises from r and om assignment . All known and unknown factors that can influence outcome , other than the treatment assignment , are distributed r and omly— and , if r and omization is effective , evenly — among the groups . Thus , any statistical difference in outcome between groups can be attributed to the assigned interventions . In contrast , big data exploits variation that may appear r and om but is not in fact r and omized . For example , big data may compare patients who did or did not receive a particular drug and then attempt to determine the extent to which any difference in patient outcome may be related to receiving the drug . However , for all the allure of inexpensive access to massive amounts of data , the Achilles ’ heel is lack of causal inference . No matter how detailed the measurement and how sophisticated the adjustment for all known variables , big data can not eliminate unmeasured factors coincident with a particular treatment assignment that could explain an apparent change in outcome .2 Thus , each approach has complementary strengths : RCTs offer causal inference , and big data offers the potential for low-cost , high-volume , nuanced answers with immediate feedback . Rather than debate which is better , the greatest promise may come from fusing them For many years clinicians have had to cope with the accusation that only 10 - 20 % of the treatments they provide have any scientific foundation . Their interventions , in other words , are seldom " evidence based " . Is the profession guilty as charged ? In April , 1995 , a general medical team at a university-affiliated district hospital in Oxford , UK , studied the treatments given to all 109 patients managed during that month on whom a diagnosis had been reached . Medical sources ( including data bases ) were then search ed for r and omised controlled trial ( RCT ) evidence that the treatments were effective . The 109 primary treatments were then classified : 82 % were evidence based ( ie , there was RCT support [ 53 % ] or unanimity on the team about the existence of convincing non-experimental evidence [ 29 % ] ) . This study , which needs to be repeated in other clinical setting s and for other disciplines , suggests that earlier pessimism over the extent to which evidence -based medicine is already practised is misplaced There is now convincing evidence that when an injured patient receives compensation , their long-term outcomes are worse . The effect of compensation is compounded when there is contact with the legal profession , especially if there is protracted litigation . Compensation appears to have a deleterious association with many forms of surgery , with a recent systematic review reporting that only one study out of 211 showed a possible positive effect . It is unacceptable that such an important influence on outcome from injury should continue without further investigation or action . Over the years there have been many reports of compensationrelated illness , mostly related to relatively minor and ill-defined conditions , such as chronic back pain and repetitive strain injury . More recently , there has been concern that the influence of compensation is associated with poor recovery from significant , objective ly characterised injuries such as long bone fractures and head injuries . These injuries would be expected to result in a long convalescence but there is evidence that the length of recovery is greater in the compensated group . This is important because these cl aims of the existence of injury can not be dismissed as fabrication , misdiagnosis by health professionals or malingering . The initial physical injury and treatment are beyond dispute . It is unlikely that the injured person planned or schemed to suffer the initial insult . Despite this , patients have taken longer to recover , have had lower return to work rates and poorer mental health outcomes . The question of whether this is just an association or whether there is a causal relationship is difficult to answer definitively as there is unlikely to ever be a r and omised controlled trial . There are however precedents for accepting that an association is causal when there is such a strong and consistent association . The best public health example is the association of smoking and lung cancer . There has never been a controlled trial , and it would be unethical to conduct one , however few medical practitioners would doubt the influence of smoking on lung cancer , despite the evidence being only that of association . The statistician who initially reported the association , and promoted the causal relationship ( Bradford Hill ) , was responsible for describing a set of criteria that were useful in ascribing causality to an association . Hill ’s criteria for causality of association were : ( i ) temporality ; ( ii ) strength of association ; ( iii ) biological gradient ( dose – response ) ; ( iv ) consistency ; ( v ) specificity ; ( vi ) plausibility ; ( vii ) coherence ; ( viii ) experiment ; and ( ix ) analogy . There is a strong , consistent , temporal relationship between compensation and delayed recovery from injury . There are also plausible reasons for a causal relationship . It is intuitive that protracted involvement with courts and the consequent focus on pain and suffering , could influence recovery . Recent evidence would suggest that there is some direct link with litigation itself , but this does not explain all of the compensation effects . There Evidence from both epidemiological and experimental observations have fueled the belief that the high consumption of fruits and vegetables rich in carotenoids may help prevent cancer and heart disease in humans . Because of its well-documented antioxidant and antigenotoxic properties , the carotenoid beta-carotene ( betaCT ) gained most of the attention in the early 1980s and became one of the most extensively studied cancer chemopreventive agents in population -based trials supported by the National Cancer Institute . However , the results of three r and omized lung cancer chemoprevention trials on betaCT supplementation unexpectedly contradicted the large body of epidemiological evidence relating to the potential benefits of dietary carotenoids . Not only did betaCT show no benefit , it was associated with significant increases in lung cancer incidence , cardiovascular diseases , and total mortality . These findings aroused widespread scientific debate that is still ongoing . It also raised the suspicion that betaCT may even possess co-carcinogenic properties . In this review , we summarize the current data on the co-carcinogenic properties of betaCT that is attributed to its role in the induction of carcinogen metabolizing enzymes and the over-generation of oxidative stress . The data presented provide convincing evidence of the harmful properties of this compound if given alone to smokers , or to individuals exposed to environmental carcinogens , as a micronutrient supplement . This has now been directly verified in a medium-term cancer transformation bioassay . In the context of public health policies , while the benefits of a diet rich in a variety of fruits and vegetables should continue to be emphasized , the data presented here point to the need for consideration of the possible detrimental effects of certain isolated dietary supplements , before mass cancer chemoprevention clinical trials are conducted on human subjects . This is especially important for genetically predisposed individuals who are environmentally or occupationally exposed to mutagens and carcinogens , such as those found in tobacco smoke and in industrial setting Functional neuroimaging studies in humans have shown that nociceptive stimuli elicit activity in a wide network of cortical areas commonly labeled as the " pain matrix " and thought to be preferentially involved in the perception of pain . Despite the fact that this " pain matrix " has been used extensively to build models of where and how nociception is processed in the human brain , convincing experimental evidence demonstrating that this network is specifically related to nociception is lacking . The aim of the present study was to determine whether there is at least a subset of the " pain matrix " that responds uniquely to nociceptive somatosensory stimulation . In a first experiment , we compared the fMRI brain responses elicited by a  r and om sequence of brief nociceptive somatosensory , non-nociceptive somatosensory , auditory and visual stimuli  , all presented within a similar attentional context . We found that the fMRI responses triggered by nociceptive stimuli can be largely explained by a combination of ( 1 ) multimodal neural activities ( i.e. , activities elicited by all stimuli regardless of sensory modality ) and ( 2 ) somatosensory-specific but not nociceptive-specific neural activities ( i.e. , activities elicited by both nociceptive and non-nociceptive somatosensory stimuli ) . The magnitude of multimodal activities correlated significantly with the perceived saliency of the stimulus . In a second experiment , we compared these multimodal activities to the fMRI responses elicited by auditory stimuli presented using an oddball paradigm . We found that the spatial distribution of the responses elicited by novel non-target and novel target auditory stimuli resembled closely that of the multimodal responses identified in the first experiment . Taken together , these findings suggest that the largest part of the fMRI responses elicited by phasic nociceptive stimuli reflects non nociceptive-specific cognitive processes The general practitioners , hospital consultants and community nurses who had cared for a r and om sample of people who died were asked about their relationships with the other professional groups and for their views and experiences of specialist domiciliary terminal care services . Many of the general practitioners and the nurses were critical of hospital communication over discharge . The most frequent criticism made by community nurses of general practitioners was that they did not ask for nursing help early enough for people who were dying . This may be because many general practitioners regarded the community nursing services as overstretched . General practitioners were rather less enthusiastic than the other two professional groups about specialist medical or nursing domiciliary terminal care services . They were , however , more convinced of the helpfulness of these services if they had some experience of them Abstract Objectives : To estimate the proportion of interventions in general practice that are based on evidence from clinical trials and to assess the appropriateness of such an evaluation . Design : Retrospective review of case notes . Setting : One suburban training general practice . Subjects : 122 consecutive doctor-patient consultations over two days . Main outcome measures : Proportions of interventions based on r and omised controlled trials  ( from literature search with Medline , pharmaceutical data bases , and st and ard textbooks ) , on  convincing non-experimental evidence , and without substantial evidence . Results : 21 of the 122 consultations recorded were excluded due to insufficient data ; 31 of the interventions were based on r and omised controlled trial evidence and 51 based on convincing non-experimental evidence . Hence 82/101 ( 81 % ) of interventions were based on evidence meeting our criteria . Conclusions : Most interventions within general practice are based on evidence from clinical trials , but the methods used in such trials may not be the most appropriate to apply to this setting . Key messages Key messages 81 % of general practice can be described as evidence based using this method of assessment Evidence derived from different method ologies may be important for the assessment of the evidence base of general In this paper , we draw on the analytic perspectives of ethno methodology to explore  doctor-patient encounters in an experimental trial of a complex intervention : an efficacy r and omised controlled trial ( RCT ) of decision-support tools in the UK . We show how the experimental context in which these encounters take place pervades the interactions within them . We argue that two interactional orders were at work in the encounters that we observed : ( i ) the ceremonial order of the consultation and ( ii ) the assemblage of the decision-support tool trial . We demonstrate how doctors in the trial oscillate between positions as authoritative clinician and neutralistic decision-support tool-implementer , and patients move between positions as passive recipients of clinical knowledge and as active subjects required to render their experience as calculable in terms of the dem and s of the decision-support tools and the broader trial they are embedded in . We demonstrate how the RCT  coordinates the world of the clinical environment and the world of experimental evidence In conventional r and omised controlled trials ( RCTs ) , research ers design the interventions . In the Camino Verde trial , each intervention community design ed its own programmes to prevent dengue . Instead of fixed actions or menus of activities to choose from , the trial r and omised clusters to a participatory research protocol that began with sharing and discussing evidence from a local survey , going on to local authors hip of the action plan for vector control . Adding equitable stakeholder engagement to RCT infrastructure anchors the research culturally , making it more meaningful to stakeholders . Replicability in other conditions is straightforward , since all intervention clusters used the same engagement protocol to discuss and to mobilize for dengue prevention . The ethical codes associated with RCTs play out differently in community-led pragmatic trials , where communities essentially choose what they want to do . Several discussion groups in each intervention community produced multiple plans for prevention , recognising different time lines . Some chose fast turnarounds , like elimination of breeding sites , and some chose longer term actions like garbage disposal and improving water supplies . A big part of the skill set for community-led trials is being able to st and back and simply support communities in what they want to do and how they want to do it , something that does not come naturally to many vector control programs or to RCT research ers . Unexpected negative outcomes can come from the turbulence implicit in participatory research . One example was the  gender dynamic  in the Mexican arm of the Camino Verde trial . Strong involvement of women in dengue control activities seems to have discouraged men in setting s where activity in public spaces or outside of the home would ordinarily be considered a “ male competence”.Community-led trials address the tension between one-size-fits-all programme interventions and local needs . Whatever the conventional wisdom about how prevention works at a system level , programmes have to be perceived as locally relevant and they must engage stakeholders who make them work . Locally , each participating community has to know the intervention is relevant to them ; they have to want to do it . That happens much more easily if they design the programme themselves Objective To develop and evaluate a best practice model of general hospital acute medical care for older people with cognitive impairment . Design R and omised controlled trial , adapted to take account of constraints imposed by a busy acute medical admission system . Setting Large acute general hospital in the United Kingdom . Participants 600 patients aged over 65 admitted for acute medical care , identified as “ confused ” on admission . Interventions Participants were  r and omised to a specialist medical and mental health unit , design ed to deliver best practice care for people with delirium or dementia , or to st and ard care ( acute geriatric or general medical wards )  . Features of the specialist unit included joint staffing by medical and mental health professionals ; enhanced staff training in delirium , dementia , and person centred dementia care ; provision of organised purpose ful activity ; environmental modification to meet the needs of those with cognitive impairment ; delirium prevention ; and a proactive and inclusive approach to family carers . Main outcome measures Primary outcome : number of days spent at home over the 90 days after r and omisation . Secondary outcomes : structured non-participant observations to ascertain patients ’ experiences ; satisfaction of family carers with hospital care . When possible , outcome assessment was blind to allocation . Results There was no significant difference in days spent at home between the specialist unit and st and ard care groups ( median 51 v 45 days , 95 % confidence interval for difference −12 to 24 ; P=0.3 ) . Median index hospital stay was 11 versus 11 days , mortality 22 % versus 25 % ( −9 % to 4 % ) , readmission 32 % versus 35 % ( −10 % to 5 % ) , and new admission to care home 20 % versus 28 % ( −16 % to 0 ) for the specialist unit and st and ard care groups , respectively . Patients returning home spent a median of 70.5 versus 71.0 days at home ( −6.0 to 6.5 ) . Patients on the specialist unit spent significantly more time with positive mood or engagement ( 79 % v 68 % , 2 % to 20 % ; P=0.03 ) and experienced more staff interactions that met emotional and psychological needs ( median 4 v 1 per observation ; P<0.001 ) . More family carers were satisfied with care ( overall 91 % v 83 % , 2 % to 15 % ; P=0.004 ) , and severe dissatisfaction was reduced ( 5 % v 10 % , −10 % to 0 % ; P=0.05 ) . Conclusions Specialist care for people with delirium and dementia improved the experience of patients and satisfaction of carers , but there were no convincing benefits in health status or service use . Patients ’ experience and carers ’ satisfaction might be more appropriate measures of success for frail older people approaching the end of life . Trial registration Clinical Trials
The Camino Verde trial of community mobilisation for control of dengue reported a significant impact on entomological indices of the Aedes aegypti vector , and on serological dengue virus infection and self-reported dengue cases .
One reason for doing r and omised controlled trials ( RCTs ) is that experiments can be convincing .
Early epidemiological experimenters , such as Jenner and the smallpox vaccine and Snow and his famous Broad Street pump h and le , already knew the answer they were demonstrating ; they used the experiments as knowledge translation devices to convince others .
More sophisticated modern experiments include cluster r and omised controlled trials ( C RCTs ) for experiments in the public health setting .
Those who are convinced by a trial and want to act , need to know how the trial relates to their own context , what contributed to success , and what might make it even more effective .