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Evaluating Translational Research: 
A Process Marker Model 
William Trochim , Ph.D. , Cathleen Kane , 
M.P.A., Mark J. Graham, Ph.D. , and 
Harold A. Pincus , M.D.
What is Translational Research? 
 Translating knowledge gained from laboratory 
science into clinical practice to improve health 
 Focus is on the integration of activities from 
bench to bedside.
The aim of translational research is to 
produce new: 
•Therapeutics 
•Medical devices 
•Tools for diagnosing disease 
•Avenues for community engagement research
The Translation Continuum 
Source: National Cancer Institute 
Basic 
Scientific 
Discovery 
Early 
Translation 
Late 
Translation 
Dissemination Adoption 
•Promising 
gene 
•Basic 
epidemiological 
finding 
•Partnerships 
•Intervention 
development 
•Phase III trials 
•Regulatory 
approval 
•Partnerships 
•Health services 
research to 
support 
dissemination 
and adoption 
•To community 
providers 
•To patients and 
public 
•Adoption of 
advance by 
providers, 
patients, and 
public 
•Payment 
mechanisms to 
enable adoption
 The National Institutes of Health (NIH) have 
made it a central priority, part of their 
“Roadmap” initiative. 
 One of their primary programs, the Clinical and 
Translational Science Awards (CTSAs) 
 350 million per year to fund 55 research centers 
 In 2012 - fund 60 centers at a cost of 
approximately a half billion dollars per year, 
making it the largest program at NIH
 One of the most significant motivations comes 
from a relatively small number of studies that 
show that it takes a long time to move basic 
scientific ideas to practice and health impacts 
 “It takes an estimated average of 17 years for 
only 14% of new scientific discoveries to enter 
day-to-day clinical practice”
 This time lag is seen as too long, certainly longer 
than necessary 
 Must be a better way to move research to practice 
more quickly without sacrificing quality or 
increasing costs. 
 Proposed solutions include everything from 
 better management of scientific research 
 increased process efficiency 
 wholesale rethinking of the biomedical research-practice 
endeavor for the 21st century.
Models of Translational 
Research
Westfall et all
Dougherty and Conway •Feedback loops 
•Broader setting over 
time
Khoury et all 
Research that describes, interprets and predicts the impact of various 
influences, especially (but not exclusively) interventions of final endpoints 
that matter to decision makers
Translational Research Model 
Synthesis
 Multiple competing models 
 Different and conflicting numbers and 
definitions of translational research phases 
 Complicate communication about translational 
research generally 
 Risk of confusing interpretations of 
evaluations
 The particular dilemma for translational research 
evaluators 
 translational “Tower of Babel” 
 Same phase label is used for very different 
operational stages in the research practice 
continuum, 
 makes cross-evaluation comparisons and syntheses 
especially problematic.
 First, and perhaps most important, all of them 
characterize translational research as a 
temporal process moving from basic to clinical 
to postclinical research and ultimately to use 
and public health impact 
 All of them also incorporate the idea of 
bidirectionality
 Second, the three alternative models to Sung 
et al.’s T1/T2 model differ in how finely they 
divide their T1 and T2 phases.
 Westfall et al. believe that practice-based 
research needs to be highlighted 
 Dougherty and Conway want to be sure to note the 
distinction between efficacy and effectiveness 
studies in clinical contexts 
 Khoury et al. want to preserve the efficacy– 
effectiveness distinction and make one between 
outcomes research and other types of 
postguidelines work
 Leading to ever more complex models, and 
contradictory classification schemes
 Third, all of the models make a basic 
distinction between research that takes place 
before and after the development of 
synthesized clinical trial knowledge at the 
point of demarcation between T1 and T2. 
 This distinction between pre- and postclinical 
synthesis may represent a critical jump from 
individual clinical studies (before) to more 
synthesized general knowledge that cuts 
across studies (after).
A General Framework for 
Translational Research and Its 
Evaluation
 Process marker model, characterized by the two 
components that constitute its name.
 First, it views translational research as a 
continuous process that moves from basic 
research through clinical, postclinical, and 
practice-based research and ultimately to 
health policies, outcomes, and impacts. 
 Process may be bidirectional, variable, and 
complex
 Second, it assumes that there are many different 
potential markers along this process.
 The focus in this model 
 identifying a set of observable points in the process 
 that can be operationally defined and measured 
 to enable evaluation of the duration of segments of 
the research-practice continuum.
 Model assumes that one would define a number 
of operational markers along a presumed 
process continuum. 
 Assuming that all of the markers use a common 
measurement scale (e.g., dates), it is then 
relatively easy to operationally define the 
difference between any two markers as the 
duration of time between their dates.
 What is the “correct” operational marker to 
use? 
 A simple answer is that there is not a single 
correct marker—different markers simply 
represent different reference points in an 
assumed continuous process. 
 That said, some markers may be better than 
others for different purposes and in different 
contexts
 Another consideration is that some markers 
are likely to be encountered by more protocols 
than others. 
 Since protocols can take different pathways in 
the process of translation, one would generally 
want to select markers that are more likely to 
be commonly passed
 Another consideration is that there are likely 
to be subprocesses that get repeated 
throughout the overall translational process. 
 For instance, the subprocess of conducting, 
replicating, and using a research study follows 
the same basic steps regardless of whether it 
is a basic, clinical, or postclinical study 
 Look at the durations between two steps in 
this subprocess
Characteristics that commend the process marker 
framework over multiphase models: 
 Avoids theoretical presumptions and undefined 
abstractions 
 Emphasizes observable measurable phenomena, 
allowing anyone to readily see how any marker is 
defined 
 Avoids the debates about how many phases there 
are in translational research, while enabling 
evaluators to use phased-based approaches as long 
as they operationally define what they mean.
 Encourages replicability 
 Robust and forgiving ie, missing data and 
variable protocol pathways can be 
accommodated 
 Encourage development of new hypotheses 
that involve more precise operational 
definitions
 Avoids debates about the scope of 
translational research ie, the scope of 
translation being examined in any given process 
marker evaluation is simply the process that is 
encompassed between the first and last 
marker measured 
 Applied prospectively or retrospectively
 The process marker model is firmly rooted in a 
process modeling research tradition in biomedical 
research as well as in other fields such as quality 
control and assurance
Important features of this approach 
 Evaluate translational research at any level of 
scale 
 Provides a foundation for the evaluation of 
interventions designed to improve translational 
research and the integration of these findings 
into a field of translational studies
 The process marker model provides a common 
framework that can link these many and varied 
studies together 
 Assess whether such interventions contribute to 
reducing time to translation
Important challenges 
 May lead to complex and difficult to 
communicate models of translational research 
 Relies upon descriptive statistics—such as 
median durations—as the heart of the results
 Increased application of an operational process 
marker approach to the study of translational 
research is likely to lead to considerable 
evolution and adaption over time. 
 The field will be able to determine empirically 
the degree to which various markers are 
feasible to measure and yield results that have 
value for our understanding of translation
Conclusion 
 Translational research is critical to the evolution 
of biomedical research and practice in the 21st 
century 
 The key problems that led to its emergence—the 
relatively long time from discovery to use and 
impact—and the relatively low proportion of 
discoveries that survive that journey—remain a 
challenge 
 Significant investments in translational research 
are already being made
 Evaluation will be essential for managing 
translational research effectively, learning 
what works and what does not, and being 
accountable for these investments 
 The current state of conceptual models and 
definitions of translational research poses 
significant challenges to our ability to evaluate
 There is considerable disagreement about 
many of the key characteristics associated 
with translational research 
 Translational research involves movement along 
the research-practice continuum, ultimately to 
health impacts.
 It is clear from that the “unit” of translation— 
the “thing” that is being translated—can 
change dramatically across the span of the 
research-practice continuum 
 While there is also wide acceptance that the 
end point of translational research is 
ultimately in health outcomes and impacts, 
many of the translational research activities 
will not directly touch on health impacts
 Stay with the data in the form of operationally 
definable markers (measures) on the pathways 
from basic research to health impacts. 
 If multiphase classification systems are used in 
evaluations, indicate clearly whose definition of 
phases are employed and how the different phases 
are being operationalized. 
 In the meantime broader translational research 
community to collect feedback regarding various 
evaluation measurement challenges
Thank You ..

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Translational research

  • 1. Evaluating Translational Research: A Process Marker Model William Trochim , Ph.D. , Cathleen Kane , M.P.A., Mark J. Graham, Ph.D. , and Harold A. Pincus , M.D.
  • 2. What is Translational Research?  Translating knowledge gained from laboratory science into clinical practice to improve health  Focus is on the integration of activities from bench to bedside.
  • 3. The aim of translational research is to produce new: •Therapeutics •Medical devices •Tools for diagnosing disease •Avenues for community engagement research
  • 4. The Translation Continuum Source: National Cancer Institute Basic Scientific Discovery Early Translation Late Translation Dissemination Adoption •Promising gene •Basic epidemiological finding •Partnerships •Intervention development •Phase III trials •Regulatory approval •Partnerships •Health services research to support dissemination and adoption •To community providers •To patients and public •Adoption of advance by providers, patients, and public •Payment mechanisms to enable adoption
  • 5.  The National Institutes of Health (NIH) have made it a central priority, part of their “Roadmap” initiative.  One of their primary programs, the Clinical and Translational Science Awards (CTSAs)  350 million per year to fund 55 research centers  In 2012 - fund 60 centers at a cost of approximately a half billion dollars per year, making it the largest program at NIH
  • 6.  One of the most significant motivations comes from a relatively small number of studies that show that it takes a long time to move basic scientific ideas to practice and health impacts  “It takes an estimated average of 17 years for only 14% of new scientific discoveries to enter day-to-day clinical practice”
  • 7.  This time lag is seen as too long, certainly longer than necessary  Must be a better way to move research to practice more quickly without sacrificing quality or increasing costs.  Proposed solutions include everything from  better management of scientific research  increased process efficiency  wholesale rethinking of the biomedical research-practice endeavor for the 21st century.
  • 9.
  • 11. Dougherty and Conway •Feedback loops •Broader setting over time
  • 12. Khoury et all Research that describes, interprets and predicts the impact of various influences, especially (but not exclusively) interventions of final endpoints that matter to decision makers
  • 14.  Multiple competing models  Different and conflicting numbers and definitions of translational research phases  Complicate communication about translational research generally  Risk of confusing interpretations of evaluations
  • 15.  The particular dilemma for translational research evaluators  translational “Tower of Babel”  Same phase label is used for very different operational stages in the research practice continuum,  makes cross-evaluation comparisons and syntheses especially problematic.
  • 16.  First, and perhaps most important, all of them characterize translational research as a temporal process moving from basic to clinical to postclinical research and ultimately to use and public health impact  All of them also incorporate the idea of bidirectionality
  • 17.  Second, the three alternative models to Sung et al.’s T1/T2 model differ in how finely they divide their T1 and T2 phases.
  • 18.  Westfall et al. believe that practice-based research needs to be highlighted  Dougherty and Conway want to be sure to note the distinction between efficacy and effectiveness studies in clinical contexts  Khoury et al. want to preserve the efficacy– effectiveness distinction and make one between outcomes research and other types of postguidelines work
  • 19.  Leading to ever more complex models, and contradictory classification schemes
  • 20.  Third, all of the models make a basic distinction between research that takes place before and after the development of synthesized clinical trial knowledge at the point of demarcation between T1 and T2.  This distinction between pre- and postclinical synthesis may represent a critical jump from individual clinical studies (before) to more synthesized general knowledge that cuts across studies (after).
  • 21.
  • 22. A General Framework for Translational Research and Its Evaluation
  • 23.  Process marker model, characterized by the two components that constitute its name.
  • 24.  First, it views translational research as a continuous process that moves from basic research through clinical, postclinical, and practice-based research and ultimately to health policies, outcomes, and impacts.  Process may be bidirectional, variable, and complex
  • 25.  Second, it assumes that there are many different potential markers along this process.
  • 26.  The focus in this model  identifying a set of observable points in the process  that can be operationally defined and measured  to enable evaluation of the duration of segments of the research-practice continuum.
  • 27.  Model assumes that one would define a number of operational markers along a presumed process continuum.  Assuming that all of the markers use a common measurement scale (e.g., dates), it is then relatively easy to operationally define the difference between any two markers as the duration of time between their dates.
  • 28.  What is the “correct” operational marker to use?  A simple answer is that there is not a single correct marker—different markers simply represent different reference points in an assumed continuous process.  That said, some markers may be better than others for different purposes and in different contexts
  • 29.  Another consideration is that some markers are likely to be encountered by more protocols than others.  Since protocols can take different pathways in the process of translation, one would generally want to select markers that are more likely to be commonly passed
  • 30.  Another consideration is that there are likely to be subprocesses that get repeated throughout the overall translational process.  For instance, the subprocess of conducting, replicating, and using a research study follows the same basic steps regardless of whether it is a basic, clinical, or postclinical study  Look at the durations between two steps in this subprocess
  • 31. Characteristics that commend the process marker framework over multiphase models:  Avoids theoretical presumptions and undefined abstractions  Emphasizes observable measurable phenomena, allowing anyone to readily see how any marker is defined  Avoids the debates about how many phases there are in translational research, while enabling evaluators to use phased-based approaches as long as they operationally define what they mean.
  • 32.  Encourages replicability  Robust and forgiving ie, missing data and variable protocol pathways can be accommodated  Encourage development of new hypotheses that involve more precise operational definitions
  • 33.  Avoids debates about the scope of translational research ie, the scope of translation being examined in any given process marker evaluation is simply the process that is encompassed between the first and last marker measured  Applied prospectively or retrospectively
  • 34.  The process marker model is firmly rooted in a process modeling research tradition in biomedical research as well as in other fields such as quality control and assurance
  • 35.
  • 36. Important features of this approach  Evaluate translational research at any level of scale  Provides a foundation for the evaluation of interventions designed to improve translational research and the integration of these findings into a field of translational studies
  • 37.  The process marker model provides a common framework that can link these many and varied studies together  Assess whether such interventions contribute to reducing time to translation
  • 38. Important challenges  May lead to complex and difficult to communicate models of translational research  Relies upon descriptive statistics—such as median durations—as the heart of the results
  • 39.  Increased application of an operational process marker approach to the study of translational research is likely to lead to considerable evolution and adaption over time.  The field will be able to determine empirically the degree to which various markers are feasible to measure and yield results that have value for our understanding of translation
  • 40. Conclusion  Translational research is critical to the evolution of biomedical research and practice in the 21st century  The key problems that led to its emergence—the relatively long time from discovery to use and impact—and the relatively low proportion of discoveries that survive that journey—remain a challenge  Significant investments in translational research are already being made
  • 41.  Evaluation will be essential for managing translational research effectively, learning what works and what does not, and being accountable for these investments  The current state of conceptual models and definitions of translational research poses significant challenges to our ability to evaluate
  • 42.  There is considerable disagreement about many of the key characteristics associated with translational research  Translational research involves movement along the research-practice continuum, ultimately to health impacts.
  • 43.  It is clear from that the “unit” of translation— the “thing” that is being translated—can change dramatically across the span of the research-practice continuum  While there is also wide acceptance that the end point of translational research is ultimately in health outcomes and impacts, many of the translational research activities will not directly touch on health impacts
  • 44.  Stay with the data in the form of operationally definable markers (measures) on the pathways from basic research to health impacts.  If multiphase classification systems are used in evaluations, indicate clearly whose definition of phases are employed and how the different phases are being operationalized.  In the meantime broader translational research community to collect feedback regarding various evaluation measurement challenges