Implementing Evidence-based Practice a Review of the Empirical Research Literature

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

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

Zoë Slote Morris

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

Steven Wooding

²RAND Europe, Cambridge CB4 1YG, UK

Jonathan Grant

²RAND Europe, Cambridge CB4 1YG, Britain

Abstract

This study 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 time lags in the development of health interventions. The study identified 23 papers. Few were comparable every bit unlike studies use dissimilar measures, of different things, at dissimilar time points. We ended that the electric current country of knowledge of time lags is of express use to those responsible for R&D and cognition transfer who confront difficulties in knowing what they should or can practice to reduce fourth dimension lags. This finer 'blindfolds' investment decisions and risks wasting attempt. The study concludes that understanding lags first requires like-minded models, definitions and measures, which can be applied in practise. A second task would be to develop a procedure by which to gather these data.

Introduction

Timely realization of the benefits of expensive medical research is an international concern alluring considerable policy endeavor around 'translation'.^¹,ⁱⁱ Policy interventions to ameliorate translation reply to a vast empirical literature on the difficulties of getting inquiry across research phases and into practice.^³–¹¹

Both literature and policy tend to assume that speedy translation of enquiry into practice is a skillful thing. Delays are seen as a waste matter of scarce resources and a sacrifice of potential patient benefit.^¹² Although some lag will be necessary to ensure the condom and efficacy of new interventions or advances, in essence we should aim to optimize lags. One recent report (of which JG and SW were co-authors) estimating the economic benefit of cardiovascular illness (CVD) research in the Britain between 1975 and 2005, found an internal rate of return (IRR) of CVD inquiry of 39%.^¹³ In other words, a £i.00 investment in public/charitable CVD research produced a stream of benefits equivalent to earning £0.39 per yr in perpetuity. Of this, nine% was attributable to the benefit from wellness improvements, which is the focus of this paper. (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 return of 13% and half dozen%, respectively, illustrating that shortening the lag between bench and bedside improves the overall benefit of cardiovascular inquiry. What is notable is that all the higher up calculations depended upon an estimated time lag; estimated because, despite longstanding concerns virtually them,^{^xiv} time lags in health research are fiddling understood.

It is ofttimes stated that it takes an average of 17 years for enquiry evidence to accomplish clinical practice.^{¹,³,^fifteen} Balas and Bohen,^¹⁶ Grant^¹⁷ and Wratschko^¹⁸ all estimated a time lag of 17 years measuring different points of the process. Such convergence around an 'average' fourth dimension lag of 17 years hides complexities that are relevant to policy and practice which would benefit from greater understanding.^{^xiii}

Despite longstanding concerns about delays in getting research into practise, the literature on time lags seems surprisingly under-developed. To help address this gap, this paper aims to synthesize existing knowledge and to offer a conceptual model that can exist used to standardize measurement and thus assistance to quantify lags in futurity. This would allow efforts to reduce lags to exist focused on areas of particular concern or value, or on areas where interventions might be expected to take best outcome. Information technology would likewise provide the potential for evaluating the toll-effectiveness of translation interventions if their bear upon on lags can exist measured. The aim was to overlay empirical lag information onto the conceptual model of translational research to provide an overview of estimated time lags and where they occur. The first function of the paper explores conceptual models of the translation pipeline in order to provide context. The second part of the paper presents a review of the literature on time lags to present current estimates and issues. This leads to a discussion on the electric current state of understanding virtually time lags and considers the implications for future practice and policy.

Methods

For the first part of the report we identified literature that described conceptual models of translation. Our search was not intended to be exhaustive, but included cardinal policy documents and searches of Google Scholar, Web of Science, PubMed and EBSCO. Key words used to recollect relevant studies included 'valley of death', 'bench to bedside', 'translational research' and 'commercialisation'. In general, 'greyness' literature was not included in the search, only the HERG study^{^xix} was included because of the authors' involvement in information technology. The models in the literature institute by these methods were summarized into a uncomplicated conceptual model.

For the second part of the study we reviewed the literature on time lags in health research. We used the same methods and literature as for the first part but included additional search terms such equally 'fourth dimension lag' or 'fourth dimension-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 astern and forward citation tracking to identify related work and used searches within targeted journals – e.g. Scientometrics and Journal of Translational Medicine. To analyse the lag information, we used a data extraction template with the post-obit fields: start and end dates for measurement menstruation, range, hateful, median, dates used, topic, country of study. In addition, the start point and endpoint of the time lag measured in each study were mapped onto specific stages in the conceptual model developed in the showtime part of the written report.

Findings

Conceptualising translational research

Understanding time lags requires a conceptual model of how research in science is converted to patient do good and so that the durations of activities and waits can be measured. This procedure of conversion of basic scientific discipline to patient do good is often called 'translation'.^{¹,^two,¹⁸,²⁰–²²} Woolf has argued that 'translation research means different things to dissimilar people'^²³ and this is reflected in the various models and definitions found in the literature. Still, every bit translational research too 'seems important to almost everyone'^²³ at that place would seem to be benefit in trying to unify models and definitions.

We have attempted to synthesize these models to place central features of the translation process and to offer a tentative unified model. This was intended to assist stakeholders hold a model which could be used to support future data gathering and meliorate guide policy-making. We recognized that drug development, public health, devices and broader aspects of healthcare practice will vary in nature. The translation process is summarized briefly in Figure1. Clearly this model tin be critiqued for being linear and nosotros acknowledge the considerable literature that challenges this notion and accept that enquiry translation is a messy, iterative and complex process (see Balaconi et al. for a good review of the liner models critiques and their partial rebuttal^²⁴). At the same fourth dimension, we would argue that for the purposes of agreement and conceptualising time lags the model is appropriate in showing common steps found 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 inquiry into benefit, equally derived from the literature

'Translational research' is typically separated into ii phases of research. Type 1 translation, besides somewhat confusingly called 'demote to bedside', refers to the conversion of knowledge from bones scientific discipline research into a potential clinical product for testing on man subjects. Type 2 translation, 'research into exercise', tends to refer to the procedure of converting promising interventions in clinical research into healthcare do (thus is closer to the notion of the 'bedside').^{ⁱⁱ,²⁰,²¹,²⁵,²⁶} Each stage of translational research is associated with a fix 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 forth. Some of these activities are repeated in different phases – grants and publications most particularly. Each action involves a lag, either considering the effort required for carrying out the task or as a issue of not-value adding waits. The activities are used as 'markers' in studies of lags.

Conceptual models typically include 'translational gaps', which describe the motility from one stage of research to another. Each of these is as well associated with delays, although precisely what and where these gaps are, and how long they are, is again not consequent in the literature. Policy measures to expedite the translation procedure typically focus on these gaps.

Estimating time lags in the translation process

Tablei shows a summary of estimates derived from empirical studies of lags.

Tabular array 1

Summary of studies of time lags in health research

Author	Context	Start of time lag	End of time 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	6			xiii	1966–1992	US
Altman (1994)^⁴⁶	Statistical techniques	First publication	Highly cited	4			6
Balas and Bohen (2000)^¹⁶	Various	'Original research'	Implementation			17		1968–1997	International	Calculated from calculation a number of studies together
Cockburn and Henderson (1996)^⁵³	Drugs	Engagement of enabling scientific research	Date to market place	xi		28	67	'Narrative histories' of drug discoveries, 1970–1995	U.s.
Comanor and Scherer (1969)^⁵⁵	Drugs	Patent	New entities		3	3			Usa
Comroe and Dripps (1976)^³⁶	'Tiptop ten clinical advances in cardiovascular and pulmonary medicine and surgery' – ECG	Publication	Clinical advances				306	Central advances since 1945	Usa
Contopoulos- loannidis (2008)^³⁵		Publication (First description)	Commencement specific use	0			221	High citations in 1990–2004	International	Worked backwards from highly cited (over 1000 citations on WoS) to the commencement description; interquartile range
Contopoulos- loannidis (2008)^³⁵		Publication (First description)	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 starting time description; interquartile range
Contopoulos- loannidis (2008)^³⁵		Publication (First description)	Kickoff human use	0			28	Loftier citations in 1990–2004	International	Worked backwards from highly cited (over one thousand citations on WoS) to the first clarification; interquartile range
Decullier et al. (2005)^²⁶	Various	Ethics approval	Engagement for commencement publication					Ethical blessing given in 1994; report conducted in 2000	France	Does not report for all papers, but only by direction of results; does not written report ranges
DiMasi (1991)^⁵⁶	Not mentioned	Clinical testing	Submission to FDA			half-dozen.3			United states of america drugs
DiMasi (1991)^⁵⁶	Not mentioned	Clinical testing	Marketing blessing			8.2			U.s. drugs
DiMasi (2003)^²⁹	R&D expenditure from 1980–1999	Clinical testing	Submission to FDA			6		1980–1999	The states drugs
DiMasi (2003)^²⁹	R&D expenditure from 1980–1999	Clinical testing	Marketing approving			7.5		1980–1999	U.s. drugs
Grant et al. (2000)^²⁸	Various	Publication	Guideline	0	8		49	1988–1995	Uk guideline	Range estimated from Figurei
Grant et al. (2003)^¹⁷	Neonatal care	Publication	Most contempo paper	13		17	21	1995–1999	UK	Estimated from graph
Harris et al. (2010)^⁴⁰	Cancer drugs	Abstruse	Publication	0.iv		0.75	1.half dozen	2005–2007	United kingdom	Results changed for abstruse to full publications in 3 out of iii cases
HERG et al. (2008)^¹³	CVD	Publication	Guideline	9		13*	14	1975–2005	UK guideline	Range varied by topic; assume a 3 year lag in publication; and used the same study flow
HERG et al. (2008)^{^thirteen}	Mental health	Publication	Guideline	6		ix	11	1975–2005	UK guideline	Range varied by topic; assume a three yr lag in publication; and used the same written report catamenia
Ioannidis (1998)^³¹	AIDS	Appointment of trial registration	Publication	iii.9	five.5		7	Studies conducted betwixt 1986 and 1996	US	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	Engagement of trial registration	Date of completion of written report	2	two.6		3.eight	Studies conducted betwixt 1986 and 1996	Us	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	Completion of report	First submission	0.7	1.four		2.3	Studies conducted betwixt 1986 and 1996	Us	Uses interquartile range
Ioannidis (1998)^³¹	AIDS	First submission	Publication	0.6	0.8		one.iv	Studies conducted between 1986 and 1996	The states	Uses interquartile range; 'negative studies suffer a substantial time lag. With some expectations, most of this lag is generated subsequently a trial has been completed.' (p. 284)
Mansfield (1991)^³³	Manufacturing products, including drugs	Bookish research	Commercialization			vii		1975–1985	Us	Cites Gellman who calculated a lag of 7.two year between (1953–1973)
Misakian and Biro (1998)^³⁹	Passive smoking	Funding began	Date of first publication describing health effects		3(+); 5–7 (–); iii (incon)			Studies started between 1981 and 1995; report conducted 1995	US – study of funding bodies	Does not report for all papers, only merely by direction of results; noted that tobacco-affiliated organizations did not respond to requests to have function in the written 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 5-year cycles, from 1962–1992; data for 1982	Castilian journal articles	Study is in Castilian; merely seems to written report 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	Same study every bit above but, data for 1992	Spanish journal articles	Report is in Spanish; only seems to report information from two cycles (1982 and 1992)
Stern and Simes (1997)^⁸	Quantitative studies submitted to Purple Prince Albert Infirmary Ethics Committee	Ethical approval	Date of first publication	3.9 (+); 6.ix (– or inconc)			5.7 (+); ∞ (– or inconc)	Ethical approval given in 1979–1981; study conducted in 1992	Purple Prince Alfred Hospital Ethics Committee Applicants, Australia	Does non report for all papers, but just past direction of results
Stern and Simes (1997)^⁸	Trials submitted to Royal Prince Albert Infirmary Ideals Committee	Upstanding approval	Date of starting time publication – trial data	3.7 (+); 7.0 (– or inconc)			5.7 (+); ∞ (– or inconc)	Ethical approving given in 1979–1981; study conducted in 1992	Royal Prince Alfred Hospital Ethics Committee Applicants, Australia	Does not written report for all papers, but merely past direction of results
Sternitzke (2010)^³⁰	'Pharmacuetal products'; drugs approved by FDA	Chemical synthesis	FDA approval			11.5			U.s. drugs	Sternitzke'southward estimates derive from a literature review
Wang-Gilam (2010)^²⁵	Cancer trials	Trial application	Enrolment	0.3			0.44	2001–2008	U.s.a.; two centres
Wratschko (2009)^¹⁸	General pharma	Drug discovery	Commercialization	10		12	17		US volume	Derived from LR Greenish (2005)

Figure2 shows these time-lag estimates past research phase. Some additional 'averaging' has been necessary to provide single figures where ranges but 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

Chart showing the approximate range and average time lag reported in studies of time lags in health enquiry. NB – HERG is the Health Economic science Inquiry Group at Brunel University

Issues with measurement and interpretation

As is shown in Table1, studies of time lags in translation of research to practice often measure dissimilar points in the process. For case, Decullier et al. ^²⁸ and Stern and Simes^²⁹ mensurate fourth dimension between upstanding approving and date of first publication; Grant et al. ^³⁰ and HERG et al. ^{^thirteen} wait at publication to guideline; DiMasi calculated the length of time inside and betwixt phases in Usa 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 guess the time lag between date of trial registration and several milestones to publication.^³³ Grant et al., Mansfield and Comroe and Dripps piece of work backwards from practice to publication.^{¹⁷,³⁴–³⁶}

Non surprisingly given they are measuring dissimilar lags, Figure2 helps show that data are generally thin and estimates vary.^³⁷ Some studies report longer lags for publication to guideline^¹⁷,³⁸ than others do for development to commercialization.^{¹⁸,³²,³⁵} Table1 likewise points to 2 substantive gaps in knowledge: the fourth dimension lag involved in and between discovery and development (T1), and the time lag between publication to do. But one study has 'implementation' into practice as its endpoint.

Measurement and reporting is oft poor. For example, 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 even interquartile ranges equally large every bit 221 years^³⁸ – are seldom reported. Furthermore, where information technology was possible, farther investigation of the average revealed broad 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 goose egg or negative results 'on average' took 'but over a year longer to be published than those with positive results'.^³⁹ This boilerplate is associated with a range of 6 to viii years for studies with a negative or cipher outcome, compared with 4 to five years for those with positive results. Comparing 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 critical reflection or recognition of this.^¹⁶,²⁵ For case, Balas and Bohen calculate an average of 17 years from original research to practice formed from adding together a number of single studies of different phases including one that estimates a lag of vi–thirteen years.^{^xvi} Bookkeeping for this changes their estimate of the time lag between journal submission to apply in practice from between 17 years to 23 years.

Non surprisingly, studies likewise show variation in fourth dimension lags past domain^³⁸ and fifty-fifty intervention within a single domain. For example, examining inquiry relating to advances in neonatal care, Grant et al. traced research papers dorsum through four 'generations' of publication. They found 'the overall time between generations 1 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 four generations of citations'. Atman et al.'s study of treatment for myocardial infarction yielded similar results: information technology took six years for a review of testify supporting the apply of thrombolytic drugs to result in a standard recommendation, whereas prophylactic lidocaine was used widely in practice for 25 years based on no evidence of effectiveness.^⁴⁰

Content besides appears to influence time lags. A mutual 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 health and institute that it took 4–6 years for a paper to receive 25 citations if the technique was new. An 'expository article' could achieve 500 citations over the aforementioned menses.^⁴⁸ Contopoulos-Ioannidis plant dissimilar publication trajectories for different types of invention.^³⁸

Studies also show 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 approval process between 1991 and 2003.^³¹ Tsuiji and Tsutani reported reduced lags in the drug approving procedure in Nippon following a alter in policy to try and expedite it.^⁵⁰

Single papers heighten issues that are not generally discussed but do seem relevant to measuring time lags from publications in particular. These issues include 'generations' of research^¹⁹ and overlaps in research publications.^¹⁹,³³ For example, Ducullier, of the 649 studies they included, five years later 59% had published research findings only most (84%) had more than one paper from the same written report.^²⁸

Word

This paper aimed to synthesize existing knowledge to offer a conceptual model that can exist used to standardize measurement and thus aid to quantify lags in time to come. The strengths of the written report are that, to our cognition, this provides the first attempt to review lags comprehensively, both in terms of using multiple approaches to observe studies, merely also in attempting to quantify time lags along the translation continuum. The review exposed a number of weaknesses in the literature and gaps in knowledge, which are not often discussed. Despite our attempts to be comprehensive, however, nosotros are aware that studies of fourth dimension lags in health research are widely distributed and not easily identified using formal literature searches and we may have failed to capture relevant studies. Nosotros struggled to observe research quantifying lags in basic research and the first translation gap in particular.

Our aim to sympathize lags has been limited past the weaknesses of existing data. 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 almost measureable. If in that location are significant lags in, say, the grant or ideals process, this is less probable to exist reflected in current total lag estimations. Moreover, the variation in selection of proxy measures ways that studies are well-nigh never measuring the same thing, making valid aggregation and generalization hard.

There is a articulate trend in the literature to seek a single respond to a single question through the calculation of an boilerplate. The variation found 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 1 section of journey from one point in time, in one topic, and adding it to other parts without reflection. Thus any poor estimates are transferred forrard into after assay, and also hide a complication which is highly relevant to research policy.

In that location also appears to be 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 exercise is ofttimes ignored, suggesting an nether-estimation of the fourth dimension lags in some cases. On the other paw, interventions may come up into use earlier guidelines outlining them have been published – suggesting an over-estimation of fourth dimension lags in other cases.

Using different endpoints, dissimilar domains and different approaches, Balas and Bohen^¹⁶ and Grant et al. ^³⁰ both estimate the time lag in health enquiry being 17 years. Wratschko also suggested 17 years as the highest limit for the time 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? One possible reason for the convergence is the difficulty of measuring longer lags – because 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 fairly quantify time lags in health enquiry, this study provides lessons for futurity research policy and practice. Concerns about lags are non new^¹⁴ but are unresolved. Based on the review, and our own work on lags,^{¹³,¹⁷,^xix,³⁰,⁵¹} we would debate that an essential pace to being able to quantify time lags, and thereby brand improvements, requires stakeholders to agree definitions, key stages and measures. It likewise perhaps requires stakeholders to develop a more than nuanced understanding of when time lags are good or bad, linked to policy choices around ideals and governance for case,^⁵² or reflect workforce problems.^⁵²,⁵³ Indeed, a recent paper by Trochmin et al. ^⁵⁴ proposes a 'procedure maker model' whereby they place a ready of operational and measureable markers forth a generalized pathway like that illustrated in Figureane. Information technology seems to us that this provides an excellent framework to back up future data gathering and analysis and thus provide a more informed base from which to develop policy to address fourth dimension lags.

Currently much of the complexity, and therefore the potential for improvement, are subconscious in this preference for 'averages'. No attending is given to understanding distributions and variations. This effectively 'blindfolds' investment decisions and risks wasting efforts to reduce lags. Equally noted in the introduction, some lags are necessary to ensure the condom and efficacy of implementing new enquiry into do. The discussion 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 identify which lags are benign and which are unnecessary, simply to answer this question it is necessary to have an authentic and comparable guess 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 near time lags and how they should be managed. This lack of knowledge puts those responsible for enabling translational research at a disadvantage. An ambitious reason for being able to accurately mensurate lags is that it would exist possible to await at their distribution to identify research that is both ho-hum and fast in its translation. Further investigation of the characteristics of research at both ends of a distribution could help identify actionable policy interventions that could speed up the translation process, where advisable, and thus increase the return on enquiry investment.

DECLARATIONS

Competing interests

None declared

Funding

This is an independent newspaper funded by the Policy Research Programme in the Department of Health. 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 project, drafted and revised the paper, and has led a number of studies cited that attempted to measure out lags; SW revised the paper

Acknowledgements

This paper derives from work undertaken by RAND Europe inside Centre for Policy Research in Science and Medicine (PRISM), funded by the United kingdom of great britain and northern ireland Department of Wellness. The authors thank NIHR for their support

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