The Mccrary Project Appears to Come Back Time and Time Again to the Recognition and Understanding of

J R Soc Med. 2011 Dec; 104(12): 510–520.

The reply is 17 years, what is the question: understanding time lags in translational research

Zoë Slote Morris

¹Institute of Public Health, University of Cambridge, Cambridge CB2 0SR, UK

Steven Wooding

²RAND Europe, Cambridge CB4 1YG, UK

Jonathan Grant

²RAND Europe, Cambridge CB4 1YG, UK

Abstract

This written report aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified fourth dimension lags in the development of wellness interventions. The written report identified 23 papers. Few were comparable as unlike studies apply unlike measures, of different things, at different fourth dimension points. Nosotros concluded that the current state of knowledge of time lags is of express employ to those responsible for R&D and cognition transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively 'blindfolds' investment decisions and risks wasting effort. The study concludes that agreement lags first requires like-minded models, definitions and measures, which tin be applied in practice. A 2nd task would be to develop a procedure past which to gather these information.

Introduction

Timely realization of the benefits of expensive medical research is an international business concern attracting considerable policy endeavour around 'translation'.^¹,² Policy interventions to meliorate translation respond to a vast empirical literature on the difficulties of getting research across inquiry phases and into practice.^{^three–¹¹}

Both literature and policy tend to presume that speedy translation of research into practice is a good thing. Delays are seen as a waste product of scarce resources and a cede of potential patient benefit.^¹² Although some lag will exist necessary to ensure the safety and efficacy of new interventions or advances, in essence we should aim to optimize lags. Ane recent study (of which JG and SW were co-authors) estimating the economic benefit of cardiovascular illness (CVD) enquiry in the UK betwixt 1975 and 2005, plant an internal charge per unit of return (IRR) of CVD enquiry of 39%.^{^xiii} In other words, a £i.00 investment in public/charitable CVD research produced a stream of benefits equivalent to earning £0.39 per year in perpetuity. Of this, nine% was attributable to the benefit from health improvements, which is the focus of this newspaper. (The remaining xxx% arise from 'spillovers' benefiting the wider economy.) This level of do good was calculated using an estimated lag of 17 years. Varying the lag time from x to 25 years produced rates of render of 13% and half-dozen%, respectively, illustrating that shortening the lag between demote and bedside improves the overall benefit of cardiovascular research. What is notable is that all the above calculations depended upon an estimated time lag; estimated considering, despite longstanding concerns near them,^¹⁴ time lags in wellness research are petty understood.

It is ofttimes stated that it takes an average of 17 years for research evidence to attain clinical practice.^ⁱ,³,¹⁵ Balas and Bohen,^¹⁶ Grant^¹⁷ and Wratschko^{^eighteen} all estimated a time lag of 17 years measuring different points of the procedure. Such convergence around an 'average' time lag of 17 years hides complexities that are relevant to policy and practise which would benefit from greater understanding.^{^xiii}

Despite longstanding concerns well-nigh delays in getting research into practice, the literature on time lags seems surprisingly nether-developed. To assistance address this gap, this paper aims to synthesize existing noesis and to offer a conceptual model that can be used to standardize measurement and thus help to quantify lags in future. This would allow efforts to reduce lags to exist focused on areas of particular business or value, or on areas where interventions might be expected to have best effect. It would as well provide the potential for evaluating the cost-effectiveness of translation interventions if their impact on lags can be measured. The aim was to overlay empirical lag data onto the conceptual model of translational research to provide an overview of estimated time lags and where they occur. The showtime office of the paper explores conceptual models of the translation pipeline in order to provide context. The 2nd role of the paper presents a review of the literature on fourth dimension lags to present current estimates and issues. This leads to a discussion on the current state of understanding well-nigh time lags and considers the implications for future do and policy.

Methods

For the first part of the written report we identified literature that described conceptual models of translation. Our search was not intended to be exhaustive, but included key policy documents and searches of Google Scholar, Web of Science, PubMed and EBSCO. Primal words used to retrieve relevant studies included 'valley of expiry', 'bench to bedside', 'translational research' and 'commercialisation'. In general, 'grey' literature was not included in the search, just the HERG written report^¹⁹ was included considering of the authors' involvement in it. The models in the literature establish past these methods were summarized into a simple conceptual model.

For the second role of the study we reviewed the literature on fourth dimension lags in wellness research. Nosotros used the same methods and literature as for the get-go office but included additional search terms such as 'time lag' or 'time-lag', 'delays', 'time factors' (PubMed MESH term) and 'publication bias'. Nosotros found a formal search yielded few relevant papers so combined a number of approaches to increase our confidence that relevant papers had been identified. We undertook backward and forward citation tracking to identify related work and used searches within targeted journals – eastward.g. Scientometrics and Journal of Translational Medicine. To analyse the lag data, we used a data extraction template with the following fields: beginning and end dates for measurement period, range, hateful, median, dates used, topic, country of study. In add-on, the start indicate and endpoint of the time lag measured in each report were mapped onto specific stages in the conceptual model developed in the get-go part of the study.

Findings

Conceptualising translational enquiry

Understanding time lags requires a conceptual model of how inquiry in science is converted to patient benefit so that the durations of activities and waits tin can be measured. This process of conversion of bones science to patient benefit is often chosen 'translation'.^{¹,^two,^eighteen,^xx–²²} Woolf has argued that 'translation research means different things to different people'^²³ and this is reflected in the various models and definitions constitute in the literature. However, equally translational research besides 'seems important to nearly everyone'^²³ there would seem to be benefit in trying to unify models and definitions.

Nosotros have attempted to synthesize these models to identify key features of the translation process and to offer a tentative unified model. This was intended to help stakeholders concord a model which could be used to support future data gathering and better guide policy-making. We recognized that drug development, public health, devices and broader aspects of healthcare do will vary in nature. The translation process is summarized briefly in Figure1. Clearly this model can be critiqued for being linear and we acknowledge the considerable literature that challenges this notion and accept that enquiry translation is a messy, iterative and complex procedure (see Balaconi et al. for a adept review of the liner models critiques and their fractional rebuttal^²⁴). At the same time, nosotros would argue that for the purposes of understanding and conceptualising time lags the model is appropriate in showing common steps plant in the literature.

An external file that holds a picture, illustration, etc. Object name is JRSM-11-018001.jpg

A conceptual model of the journey of wellness (biomedical) research from research into benefit, equally derived from the literature

'Translational research' is typically separated into two phases of enquiry. Blazon ane translation, too somewhat confusingly called 'bench to bedside', refers to the conversion of knowledge from bones scientific discipline research into a potential clinical product for testing on human subjects. Blazon 2 translation, 'enquiry into practice', tends to refer to the process of converting promising interventions in clinical research into healthcare practise (thus is closer to the notion of the 'bedside').^{²,²⁰,²¹,²⁵,²⁶} Each phase of translational enquiry is associated with a set of research activities which contribute to lags.^²⁷ These include processes around grant awards, ethical approvals, publication, phase I, Ii, III trials, approvals for drugs, mail service-marketing testing, guideline preparation and so along. Some of these activities are repeated in different phases – grants and publications well-nigh particularly. Each activity involves a lag, either because the effort required for conveying out the task or every bit a effect of non-value adding waits. The activities are used as 'markers' in studies of lags.

Conceptual models typically include 'translational gaps', which describe the movement from one phase of inquiry to another. Each of these is also associated with delays, although precisely what and where these gaps are, and how long they are, is again not consistent in the literature. Policy measures to expedite the translation procedure typically focus on these gaps.

Estimating time lags in the translation process

Tabular arrayi shows a summary of estimates derived from empirical studies of lags.

Table 1

Summary of studies of time lags in health research

Author	Context	Kickoff of time lag	Terminate of fourth dimension lag	Time lag (years)				Dates	Country	Notes
				Lower range	Median	Mean	College Range
Antman (1992)^³⁸	Treatment for myocardial infarction	Publication of clinical trial	Guideline/ recommendation	six			13	1966–1992	United states of america
Altman (1994)^⁴⁶	Statistical techniques	Beginning publication	Highly cited	iv			6
Balas and Bohen (2000)^¹⁶	Diverse	'Original research'	Implementation			17		1968–1997	International	Calculated from adding a number of studies together
Cockburn and Henderson (1996)^⁵³	Drugs	Date of enabling scientific inquiry	Date to market	xi		28	67	'Narrative histories' of drug discoveries, 1970–1995	US
Comanor and Scherer (1969)^⁵⁵	Drugs	Patent	New entities		3	3			United states
Comroe and Dripps (1976)^³⁶	'Top ten clinical advances in cardiovascular and pulmonary medicine and surgery' – ECG	Publication	Clinical advances				306	Key advances since 1945	Us
Contopoulos- loannidis (2008)^³⁵		Publication (Starting time clarification)	First specific utilize	0			221	High citations in 1990–2004	International	Worked backwards from highly cited (over yard citations on WoS) to the offset description; interquartile range
Contopoulos- loannidis (2008)^³⁵		Publication (Commencement clarification)	Highly cited publication	14	24	24	44	Loftier citations in 1990–2004	International	Worked backwards from highly cited (over 1000 citations on WoS) to the first description; interquartile range
Contopoulos- loannidis (2008)^³⁵		Publication (First description)	Showtime human use	0			28	High citations in 1990–2004	International	Worked backwards from highly cited (over g citations on WoS) to the first description; interquartile range
Decullier et al. (2005)^²⁶	Various	Ethics approval	Appointment for first publication					Ethical approval given in 1994; study conducted in 2000	France	Does non report for all papers, but only by direction of results; does not written report ranges
DiMasi (1991)^⁵⁶	Non mentioned	Clinical testing	Submission to FDA			half-dozen.three			U.s. drugs
DiMasi (1991)^⁵⁶	Not mentioned	Clinical testing	Marketing approval			8.2			US drugs
DiMasi (2003)^²⁹	R&D expenditure from 1980–1999	Clinical testing	Submission to FDA			6		1980–1999	US drugs
DiMasi (2003)^²⁹	R&D expenditure from 1980–1999	Clinical testing	Marketing approval			7.5		1980–1999	US drugs
Grant et al. (2000)^²⁸	Various	Publication	Guideline	0	8		49	1988–1995	Uk guideline	Range estimated from Figure1
Grant et al. (2003)^¹⁷	Neonatal care	Publication	Most recent paper	13		17	21	1995–1999	Uk	Estimated from graph
Harris et al. (2010)^⁴⁰	Cancer drugs	Abstract	Publication	0.4		0.75	one.6	2005–2007	United kingdom	Results changed for abstruse to full publications in iii out of iii cases
HERG et al. (2008)^{^thirteen}	CVD	Publication	Guideline	9		13*	14	1975–2005	Britain guideline	Range varied by topic; presume a iii year lag in publication; and used the aforementioned written report menstruum
HERG et al. (2008)^¹³	Mental health	Publication	Guideline	half dozen		9	xi	1975–2005	UK guideline	Range varied by topic; assume a three year lag in publication; and used the same report period
Ioannidis (1998)^³¹	AIDS	Engagement of trial registration	Publication	three.9	five.5		7	Studies conducted between 1986 and 1996	Usa	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	Date of trial registration	Appointment of completion of study	ii	two.six		3.eight	Studies conducted between 1986 and 1996	US	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	Completion of study	Outset submission	0.7	one.4		two.3	Studies conducted betwixt 1986 and 1996	Us	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	First submission	Publication	0.6	0.8		i.4	Studies conducted betwixt 1986 and 1996	Usa	Uses interquartile range; 'negative studies endure a substantial time lag. With some expectations, nearly of this lag is generated after a trial has been completed.' (p. 284)
Mansfield (1991)^³³	Manufacturing products, including drugs	Academic research	Commercialization			7		1975–1985	U.s.a.	Cites Gellman who calculated a lag of 7.ii year between (1953–1973)
Misakian and Biro (1998)^³⁹	Passive smoking	Funding began	Appointment of commencement publication describing wellness furnishings		iii(+); v–7 (–); 3 (incon)			Studies started betwixt 1981 and 1995; study conducted 1995	US – written report of funding bodies	Does not report for all papers, just merely past management of results; noted that tobacco-affiliated organizations did not respond to requests to have part in the report despite several requests
Pulido et al. (1994)^⁴⁷	Papers published in Medicina Clínica	Submission of paper	Publication	0.81		0.86	0.92	Looked at 12 articles in five-year cycles, from 1962–1992; data for 1982	Spanish journal articles	Written report is in Castilian; only seems to study data from two cycles (1982 and 1992)
Pulido et al. (1994)^⁴⁷	Papers published in Medicina Clínica	Submission of newspaper	Publication	0.32		0.81	0.56	Aforementioned study equally in a higher place but, data for 1992	Castilian periodical articles	Written report is in Spanish; only seems to report information from two cycles (1982 and 1992)
Stern and Simes (1997)^{^viii}	Quantitative studies submitted to Majestic Prince Albert Hospital Ethics Commission	Ethical approval	Date of outset publication	3.9 (+); half-dozen.9 (– or inconc)			v.7 (+); ∞ (– or inconc)	Ethical approval given in 1979–1981; study conducted in 1992	Regal Prince Alfred Hospital Ethics Committee Applicants, Australia	Does not report for all papers, simply only past direction of results
Stern and Simes (1997)^{^eight}	Trials submitted to Royal Prince Albert Hospital Ideals Committee	Ethical approving	Date of beginning publication – trial data	3.vii (+); 7.0 (– or inconc)			5.7 (+); ∞ (– or inconc)	Upstanding approval given in 1979–1981; study conducted in 1992	Royal Prince Alfred Infirmary Ethics Committee Applicants, Commonwealth of australia	Does not report for all papers, but only by direction of results
Sternitzke (2010)^³⁰	'Pharmacuetal products'; drugs approved by FDA	Chemic synthesis	FDA approval			11.five			U.s.a. drugs	Sternitzke's estimates derive from a literature review
Wang-Gilam (2010)^²⁵	Cancer trials	Trial application	Enrolment	0.3			0.44	2001–2008	US; two centres
Wratschko (2009)^{^xviii}	General pharma	Drug discovery	Commercialization	10		12	17		US book	Derived from LR Greenish (2005)

Effigy2 shows these time-lag estimates by research phase. Some additional 'averaging' has been necessary to provide single figures where ranges simply were used in the original paper. The source data are presented in Appendix A (see http://jrsm.rsmjournals.com/content/104/12/510/suppl/DC1).

An external file that holds a picture, illustration, etc. Object name is JRSM-11-018002.jpg

Nautical chart showing the judge range and average fourth dimension lag reported in studies of fourth dimension lags in wellness research. NB – HERG is the Health Economic science Research Group at Brunel University

Problems with measurement and interpretation

As is shown in Table1, studies of time lags in translation of research to practice oftentimes measure different points in the procedure. For case, Decullier et al. ^²⁸ and Stern and Simes^²⁹ measure out fourth dimension between ethical blessing and date of first publication; Grant et al. ^{^xxx} and HERG et al. ^¹³ expect at publication to guideline; DiMasi calculated the length of time within and between phases in US drug development to calculate the costs associated with the phases.^³¹ Sternitzke looked at commercialization of pharmaceutical innovations from 'chemical synthesis' to FDA approval.^³² Ioannidis attempted to gauge the time lag between engagement of trial registration and several milestones to publication.^³³ Grant et al., Mansfield and Comroe and Dripps work backwards from practice to publication.^{¹⁷,³⁴–³⁶}

Not surprisingly given they are measuring different lags, Figure2 helps show that information are by and large sparse and estimates vary.^³⁷ Some studies report longer lags for publication to guideline^¹⁷,³⁸ than others exercise for development to commercialization.^{¹⁸,³²,³⁵} Table1 also points to two substantive gaps in cognition: the time lag involved in and between discovery and development (T1), and the time lag between publication to do. Just one study has 'implementation' into practice as its endpoint.

Measurement and reporting is oftentimes poor. For instance, Decullier et al. report 'mean' lags,^²⁸ and Dwan, in reporting Decullier et al., in their review refer to 'median' lags.^³⁶ Neither reports distributions. Ranges – or fifty-fifty interquartile ranges equally big as 221 years^³⁸ – are seldom reported. Furthermore, where information technology was possible, further investigation of the average revealed wide variation; variation which is not highlighted or discussed in the papers. For example, Hopewell et al. in their review of publication bias conclude that clinical trials with null or negative results 'on average' took 'just over a year longer to exist published than those with positive results'.^³⁹ This average is associated with a range of six to eight years for studies with a negative or null result, compared with 4 to five years for those with positive results. Comparison the slowest negative publication with the fastest positive publication makes a potential difference of iv years – half of the maximum lag.

Some studies aggregate data from earlier studies without disquisitional reflection or recognition of this.^¹⁶,²⁵ For example, Balas and Bohen calculate an average of 17 years from original research to do formed from calculation together a number of single studies of dissimilar phases including one that estimates a lag of vi–thirteen years.^¹⁶ Accounting for this changes their estimate of the time lag between journal submission to use in do from between 17 years to 23 years.

Not surprisingly, studies besides testify variation in time lags by domain^³⁸ and fifty-fifty intervention inside a single domain. For instance, examining enquiry relating to advances in neonatal care, Grant et al. traced research papers back through four 'generations' of publication. They found 'the overall time between generations i to 4 ranges from 13 years (for artificial surfactant) to 21 years (for parenteral diet). The other three advances took 17 years to develop through 4 generations of citations'. Atman et al.'due south study of treatment for myocardial infarction yielded like results: information technology took six years for a review of evidence supporting the use of thrombolytic drugs to result in a standard recommendation, whereas safety lidocaine was used widely in practice for 25 years based on no prove of effectiveness.^⁴⁰

Content likewise appears to influence time lags. A common theme found in the literature concerns publication bias, and their implications for judging effectiveness.^{²⁸,²⁹,³³,³⁷–³⁹,⁴¹–⁴⁷} Altman looked at citations of new statistical techniques applied to wellness and establish that it took 4–vi years for a paper to receive 25 citations if the technique was new. An 'expository article' could accomplish 500 citations over the same menstruum.^⁴⁸ Contopoulos-Ioannidis found unlike publication trajectories for unlike types of invention.^³⁸

Studies also testify that time lags are not stable over time. For example, Pulido noted a difference of 0.9 years or 0.3 years from credence to publication in 1992 and 1982, respectively.^⁴⁹ DiMasi reported a slight shortening of the blessing process between 1991 and 2003.^³¹ Tsuiji and Tsutani reported reduced lags in the drug approval procedure in Japan post-obit a change in policy to endeavour and expedite information technology.^⁵⁰

Single papers raise issues that are not more often than not discussed but do seem relevant to measuring time lags from publications in particular. These problems include 'generations' of research^¹⁹ and overlaps in enquiry publications.^{^xix,³³} For example, Ducullier, of the 649 studies they included, 5 years later 59% had published research findings but most (84%) had more than one newspaper from the same study.^²⁸

Discussion

This paper aimed to synthesize existing knowledge to offer a conceptual model that can be used to standardize measurement and thus help to quantify lags in future. The strengths of the study are that, to our knowledge, this provides the first endeavour to review lags comprehensively, both in terms of using multiple approaches to observe studies, but as well in attempting to quantify time lags forth the translation continuum. The review exposed a number of weaknesses in the literature and gaps in noesis, which are not ofttimes discussed. Despite our attempts to exist comprehensive, yet, we are enlightened that studies of time lags in health inquiry are widely distributed and not hands identified using formal literature searches and nosotros may have failed to capture relevant studies. Nosotros struggled to find research quantifying lags in basic enquiry and the commencement translation gap in detail.

Our aim to understand lags has been limited past the weaknesses of existing information. Limitations of the literature examined include the use of proxy measures. Much of the literature on lags focuses on dissemination and publication in peer-review journals in particular as these are the most measureable. If there are significant lags in, say, the grant or ethics process, this is less likely to be reflected in current total lag estimations. Moreover, the variation in selection of proxy measures means that studies are virtually never measuring the same affair, making valid aggregation and generalization hard.

At that place is a clear trend in the literature to seek a single answer to a single question through the calculation of an average. The variation plant in the literature suggests that this is not possible (or even desirable), and variation matters. Moreover, many of the published 'averages' are derived from adding an empirically derived mean duration for ane section of journey from ane point in time, in 1 topic, and adding it to other parts without reflection. Thus any poor estimates are transferred forward into later analysis, and also hibernate a complexity which is highly relevant to research policy.

There also appears to exist a mismatch betwixt conceptual models of the translation process, and the measuring of lags. For example, the gap between guideline publication and translation into actual practise is often ignored, suggesting an under-estimation of the time lags in some cases. On the other hand, interventions may come into employ before guidelines outlining them have been published – suggesting an over-interpretation of time lags in other cases.

Using dissimilar endpoints, unlike domains and unlike approaches, Balas and Bohen^¹⁶ and Grant et al. ^³⁰ both estimate the time lag in health inquiry being 17 years. Wratschko also suggested 17 years as the highest limit for the fourth dimension taken from drug discovery to commercialization.^¹⁸ It is surprising that 17 is the answer to several related but differing questions. Is this coincidence or not? I possible reason for the convergence is the difficulty of measuring longer lags – considering of limitations of citation indexes, other records and recollections – which provides a ceiling to such estimates and leads to a convergence of average lags.

While not able to adequately quantify time lags in wellness research, this study provides lessons for future research policy and practice. Concerns about lags are non new^{^xiv} simply are unresolved. Based on the review, and our own work on lags,^{¹³,¹⁷,¹⁹,³⁰,⁵¹} we would argue that an essential step to being able to quantify time lags, and thereby make improvements, requires stakeholders to agree definitions, cardinal stages and measures. It also perhaps requires stakeholders to develop a more than nuanced understanding of when time lags are skilful or bad, linked to policy choices effectually ethics and governance for example,^⁵² or reverberate workforce bug.^⁵²,⁵³ Indeed, a recent paper by Trochmin et al. ^⁵⁴ proposes a 'process maker model' whereby they place a set of operational and measureable markers along a generalized pathway like that illustrated in Effigy1. It seems to us that this provides an excellent framework to support future information gathering and analysis and thus provide a more than informed base from which to develop policy to accost fourth dimension lags.

Currently much of the complication, and therefore the potential for improvement, are subconscious in this preference for 'averages'. No attention is given to understanding distributions and variations. This effectively 'blindfolds' investment decisions and risks wasting efforts to reduce lags. As noted in the introduction, some lags are necessary to ensure the safety and efficacy of implementing new inquiry into practice. The give-and-take in the literature fails to consider what is necessary or desirable, tending to assume that all lags are unwelcome. A key question for policy is to place which lags are beneficial and which are unnecessary, merely to answer this question it is necessary to have an authentic and comparable estimate of the lags.

Determination

Translating scientific discoveries into patient benefit more quickly is a policy priority of many health research systems. Despite their policy salience, little is known about time lags and how they should be managed. This lack of cognition puts those responsible for enabling translational inquiry at a disadvantage. An aggressive reason for beingness able to accurately mensurate lags is that it would be possible to look at their distribution to place research that is both slow and fast in its translation. Further investigation of the characteristics of inquiry at both ends of a distribution could aid place actionable policy interventions that could speed up the translation process, where appropriate, and thus increment the render on enquiry investment.

DECLARATIONS

Competing interests

None declared

Funding

This is an independent newspaper funded by the Policy Research Plan in the Section of Wellness. The views expressed are not necessarily those of the Department

Ethical approval

Non applicable

Contributorship

ZSM designed, conducted and analysed the literature review, and drafted and revised the paper; JG initiated the projection, drafted and revised the paper, and has led a number of studies cited that attempted to mensurate lags; SW revised the paper

Acknowledgements

This paper derives from work undertaken by RAND Europe within Heart for Policy Research in Science and Medicine (PRISM), funded by the UK Department of Health. The authors thank NIHR for their back up

References

i. Westfall J, Mold J, Fagnan L Practice-based research – "Blue Highways" on the NIH roadmap. JAMA 2007;297:403–6 [PubMed] [Google Scholar]

2. Cooksey D A review of Uk wellness research funding: Norwich: HMSO, 2006 [Google Scholar]

3. Trochim Westward Translation Won't Happen Without Broadcasting and Implementation: Some Measurement and Evaluation Issues. 3rd Annual Briefing on the Science of Broadcasting and Implementation Bethesda, Dr.: 2010 [Google Scholar]

4. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O Improvidence of Innovations in Service Organizations: Systematic Review and Recommendations. Milbank Q 2004;82:581–629 [PMC free commodity] [PubMed] [Google Scholar]

5. Rogers Eastward Diffusion of innovations. quaternary edn New York, NY: The Free Press, 1995 [Google Scholar]

half dozen. Packer C, Simpson S, Stevens A International diffusion of new health technologies: A ten-country assay of vi health technologies. Int J Technol Assess Health Intendance 2006;22:419–28 [PubMed] [Google Scholar]

seven. Davis D, Evans M, Jadad A, et al. The case for cognition translation: shortening the journey from evidence to consequence. BMJ 2003;327:33–5 [PMC free commodity] [PubMed] [Google Scholar]

eight. Lang ES, Wyer PC, Haynes RB Knowledge translation: Endmost the evidence-to-practise gap. Ann Emerg Med 2007;49:355–63 [PubMed] [Google Scholar]

9. Graham ID, Logan J, Harrison MB, et al. Lost in noesis translation: Fourth dimension for a map? J Contin Educ Health Prof 2006;26:13–24 [PubMed] [Google Scholar]

x. Graham I, Tetroe J, Group KTR Some theoretical underpinnings of noesis translation. Acad Emerg Med 2007;14:936–41 [PubMed] [Google Scholar]

xi. Baumbusch J, Kirkham Southward, Khan Grand, et al. Pursuing common agendas: a collaborative model for knowledge translation between research and do in clinical settings. Res Nurs Health 2008;31:130–40 [PubMed] [Google Scholar]

12. Ward V, House A, Hamer South Developing a framework for transferring knowledge into action: a thematic assay of the literature. J Health Serv Res Policy 2009;xiv:156–64 [PMC costless article] [PubMed] [Google Scholar]

13. Wellness Economic science Research Group, Part of Health Economics, RAND Europe Medical Research: What'due south it Worth? Estimating the Economic Benefits from Medical Research in the UK. London: Britain Evaluation Forum, 2008 [Google Scholar]

fourteen. Yokote G, Utterback R Time lapses in information dissemination: enquiry laboratory to md's function. Bull Med Libr Assoc 1974;62:251. [PMC free article] [PubMed] [Google Scholar]

15. Green L, Ottoson J, García C, Hiatt R Diffusion theory and knowledge dissemination, utilization, and integration in public health. Annu Rev Public Health 2009;xxx:151–74 [PubMed] [Google Scholar]

16. Balas E, Boren S Managing Clinical Knowledge for Health Care Improvement. : van Bemmel JH, McCray AT, Yearbook of Medical Information science. Stuttgart: Schattauer Verlagsgesellschaft mbH, 2000:65–seventy [PubMed] [Google Scholar]

17. Grant J, Greenish L, Mason B Basic research and health: a reassessment of the scientific footing for the support of biomedical scientific discipline. Res Eval 2003;12:217–24 [Google Scholar]

18. Wratschko Chiliad Empirical Setting: The pharmaceutical manufacture. Strategic Orientation and Brotherhood Portfolio Configuration. New York, NY: Springer, 2009 [Google Scholar]

19. Grant J, Green Fifty, Mason B From bedside to bench: Comroe and Dripps revisited. HERG Research Report No thirty. Uxbridge: The Wellness Economics Inquiry Grouping, Brunel University, 2003 [Google Scholar]

20. Khoury Chiliad, Gwinn One thousand, Yoon P, Dowling N, Moore C, Bradley L The continuum of translation inquiry in genomic medicine: how can we advance the appropriate integration of human being genome discoveries into health intendance and disease prevention? Genet Med 2007;9:665. [PubMed] [Google Scholar]

21. Khoury MJ, Gwinn Thou, Ioannidis JP The emergence of translational epidemiology: from scientific discovery to population health touch. Am J Epidemiol 2010;172:517–24 [PMC gratis article] [PubMed] [Google Scholar]

22. Dougherty D, Conway P The "3T's" road map to transform United states health care: the "how" of high-quality care. JAMA 2008;299:2319–21 [PubMed] [Google Scholar]

23. Woolf Due south The meaning of translational research and why information technology matters. JAMA 2008;299:211–13 [PubMed] [Google Scholar]

24. Balconi M, Brusoni S, Orsenigo Fifty In defence of the linear model: An essay. Res Policy 2010;39:ane–13 [Google Scholar]

25. Bricklayer C, Manzotti E The Translation Cycle: round and round in cycles is the only way frontward for regenerative medicine. Regen Med 2010;5:153–five [PubMed] [Google Scholar]

26. Reis S, McDonald Thousand, Byers S Crossing the Inquiry Valleys of Expiry: The University of Pittsburgh Approach. Clin Trans Sci 2008;1:9–10 [PMC complimentary article] [PubMed] [Google Scholar]

27. Wang-Gillam A, Williams K, Novello S, Gao F, Scagliotti Yard, Govindan R Fourth dimension to activate lung cancer clinical trials and patient enrollment: A representative comparison study between ii bookish centers across the Atlantic. J Clin Oncol 2010;28:3803–7 [PubMed] [Google Scholar]

28. Decullier E, Lheritier V, Chapuis F Fate of biomedical research protocols and publication bias in French republic: retrospective accomplice study. BMJ 2005;331:19. [PMC free article] [PubMed] [Google Scholar]

29. Stern J, Simes R Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ 1997;315:640–5 [PMC free article] [PubMed] [Google Scholar]

xxx. Grant J, Cottrell R, Cluzeau F, Fawcett K Evaluating "payback" on biomedical research from papers cited in clinical guidelines: applied bibliometric report. BMJ 2000;320:1107–eleven [PMC complimentary article] [PubMed] [Google Scholar]

31. DiMasi J, Hansen R, Grabowski H The price of innovation: new estimates of drug development costs. J Health Econ 2003;22:151–85 [PubMed] [Google Scholar]

32. Sternitzke C Cognition sources, patent protection, and commercialization of pharmaceutical innovations. Res Policy 2010;39:810–21 [Google Scholar]

33. Ioannidis J Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. JAMA 1998;279:281. [PubMed] [Google Scholar]

34. van Luijn J, Stolk P, Gribnau F, Leufkens H. Gap in publication of comparative information on new medicines. Br J Clin Pharmacol 2008;65:716–22 [PMC free article] [PubMed] [Google Scholar]

35. Mansfield East Bookish research and industrial innovation. Res Policy 1991;20:i–12 [Google Scholar]

36. Cockburn I, Henderson R Public–private interaction in pharmaceutical research. PNAS 1996;93:12725. [PMC free article] [PubMed] [Google Scholar]

37. Dwan K, Altman D, Arnaiz J, et al. Systematic review of the empirical prove of study publication bias and upshot reporting bias. PLoS 1 2008;three:3081 [PMC free article] [PubMed] [Google Scholar]

38. Contopoulos-Ioannidis D, Alexiou G, Gouvias T, Ioannidis J Life cycle of translational research for medical interventions. Science 2008;321:1298–9 [PubMed] [Google Scholar]

39. Hopewell South, Clarke K, Stewart L, Tierney J Time to publication for results of clinical trials. Cochrane Database Syst Rev 2007;(2):MR000011 [PMC gratis article] [PubMed] [Google Scholar]

xl. Antman E, Lau J, Kupelnick B, Mosteller F, Chalmers T A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts: treatments for myocardial infarction. JAMA 1992;268:240–eight [PubMed] [Google Scholar]

41. Misakian A, Bero Fifty Publication bias and research on passive smoking: comparison of published and unpublished studies. JAMA 1998;280:250–3 [PubMed] [Google Scholar]

42. Harris P, Takeda A, Loveman E, Hartwell D Time to full publication of studies of anticancer drugs for chest cancer, and the potential for publication bias. Int J Technol Assess Health Care 2010;26:110–16 [PubMed] [Google Scholar]

43. Takeda A, Loveman East, Harris P, Hartwell D, Welch K Time to full publication of studies of anti-cancer medicines for breast cancer and the potential for publication bias: a short systematic review. Health Technol Assess 2008;12:1–68 [PubMed] [Google Scholar]

44. Easterbrook P, Gopalan R, Berlin J, Matthews D Publication bias in clinical research. Lancet 1991;337:867–72 [PubMed] [Google Scholar]

45. Dickersin G, Chan Due south, Chalmersx T, Sacks H, Smith H. Jr Publication bias and clinical trials. Control Clin Trials 1987;8:343–53 [PubMed] [Google Scholar]

46. Dickersin Chiliad The existence of publication bias and take a chance factors for its occurrence. JAMA 1990;263:1385–9 [PubMed] [Google Scholar]

47. Grol R, Grimshaw J From best bear witness to best practice: constructive implementation of change in patients' care. Lancet 2003;362:1225–30 [PubMed] [Google Scholar]

48. Altman D, Goodman S Transfer of technology from statistical journals to the biomedical literature: past trends and future predictions. JAMA 1994;272:129–32 [PubMed] [Google Scholar]

49. Pulido M, González J, Sanz F Original articles published in Medicina Clínica during thirty years (1962–1992): number of authors, interval between acceptance and publication and bibliographic references. Med Clin 1994;103:770–5 [PubMed] [Google Scholar]

50. Tsuji K, Tsutani K Follow the leader. Nature 2008;453:851–ii [PubMed] [Google Scholar]

51. Grant J, Lewison Thou, May R Government funding of research and development. Scientific discipline 1997;278:878–eighty [Google Scholar]

52. Newby D, Webb D Translational inquiry: a priority for health and wealth. Centre 2010;96:815–16 [PubMed] [Google Scholar]

54. Trochim Westward, Kane C, Graham M, Pincus H Evaluating translation research. A process marker model. Clinical and Translation Science 2011;4:153–62 [PMC complimentary article] [PubMed] [Google Scholar]

55. Comroe J, Dripps R Scientific basis for the support of biomedical science. Science 1976;192:105–11 [PubMed] [Google Scholar]

56. DiMasi J, Hansen R, Grabowski H, Lasagna L Cost of innovation in the pharmaceutical industry. J Health Econ 1991;10:107–42 [PubMed] [Google Scholar]

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