In October 2003, former FDA Commissioner Mark McClellan told an audience in Bethesda, Maryland: "The kinds of information bottlenecks created by poorly accessible paper records is dangerous, inefficient, and expensive, whether it's our own evaluation of a new drug application or a doctor's evaluation of her patient." 

McClellan's criticism came during the keynote presentation he gave at Interchange 2003, the inaugural industry-wide meeting of the Clinical Data Interchange Standards Consortium (CDISC), which develops and supports global, platform-independent data standards that enable information system interoperability in research and related areas of health care. Since that keynote address, CDISC has worked hard to make the paper-based bottlenecks McClellan referred to a thing of the past by not only making it easier to transition from paper to computers, but also making it possible to integrate clinical research data and health care data. Their partner in this Herculean harmonization effort is Ann Arbor, MI-based Health Level 7 (HL7), an ANSI-accredited standards developing organization for health care that's been around for three decades. In February 2008 the two volunteer-driven groups, who first met when CDISC accepted an invitation to speak to HL7's board about their work, celebrated the second renewal of their 2001 associate charter agreement—the official initiation of their collaboration. 

"Our first step was to try to integrate our XML standard (ODM or Operational Data Model) with the HL7 RIM (Reference Information Model)," said Rebecca Kush, PhD, president and chief executive officer of CDISC, "[but] this was a complete failure; the CDISC and HL7 representatives were not speaking the same language." After spending much time figuring out the basic disconnect between its data structure and HL7's and harmonizing its own standards (e.g., ODM and SDTM), domain experts from CDISC created a model to represent clinical research within HL7 RIM called BRIDG (or the Biomedical Research Integrated Domain Group Model). And it's this domain analysis model, which NCI and FDA helped develop, that's setting the stage for the seamless integration of patient care and clinical research, according to Charles Jaffe, MD, PhD, chief executive officer of HL7. Jaffe, who served six years on CDISC's board before joining HL7, says such integration will allow researchers "seamless access to patient level information whether by diagnosis or medication and [to] identify those criteria that accurately reflect inclusion/exclusion criteria." Technicalities aside and erring on the side of simplicity, the resulting benefits would include major improvements in the subject recruitment process (such as lower costs and improved efficiency and speed) and improvements in the real-time reporting and surveillance of adverse events so that postapproval tragedies like Vioxx could be avoided. 

In order for such benefits to be realized, electronic health records (EHRs)—a technology reportedly used by less than 15% of U.S. physicians today—would have to become the norm in hospitals and doctors offices and they would have to use CDISC and HL7 standards with the appropriate processes. "That's the whole idea behind the standards movement," explained Kush, "to get adoption." But while other countries like Canada and the UK pour millions into upgrading their health care systems to include EHRs, the U.S. government has left the decision to the private sector, which is dragging its feet in the face of out-of-pocket costs and interruptions in work flow. Policy issues, including privacy, are also impeding the transition. "Before we can be successful with our most important opportunity to make fundamental use of data, we need to address those policies," said Jaffe. As for how to address them, neither HL7, CDISC nor FDA appear certain. In the meantime, both standards organizations continue to forge ahead with their pursuit of a collaboration on a global scale, most recently including the development of joint educational programs around the world as part of their renewed charter agreement. They also continue to boast the backing of FDA. "The [HL7/CDISC] project is very important in getting us to that single, harmonized, standardized approach to the structured scientific data that we receive," Armando Oliva, MD, CAPT, deputy director for bioinformatics, Office of Critical Path Programs, told 

Articles

News

CDISC and HL7 combine to create Biomedical Research Integrated Domain Group Model.

CDISC and HL7: aBRIDGed

Adaptive design, sometimes referred to as flexible design,

  is more than another new statistical methodology, tool or formula in the hands of biostatisticians. It's a new technology that changes the entire clinical research process. 

 Photography: Marcus Clackson, Getty Images 

Midstudy adaptations and flexibility are new concepts not only for statisticians but also for trial managers, data scientists, and regulatory experts. Adaptive designs challenge medical advisory groups, data monitoring committees (DMCs), drug safety boards, ethics committees, and regulatory agencies. These designs have an impact on drug development strategies, trial protocols, informed consent forms, trial amendments, data analysis, and reporting plans. Adaptive design will also change the study drug supply chain; the patient enrollment, randomization, and data capturing process; monitoring; and data cleaning systems. In other words, adaptive designs represent a totally new technology in the field of clinical research and development. 

As with every new technology, its far reaching consequences are ignored or underestimated in the beginning. In fact, when it comes to adaptive design, there is ongoing debate about statistical optimality, practical feasibility,

So, what are the major benefits? Above all, the ethical benefits of adaptive designs are the ones that count the most. The new technology will provide enormous advantages to individuals who participate in clinical trials by reducing their exposure to ineffective or unsafe treatments. Society benefits because this new technology helps bring better drugs to market faster. The bottom line is that adaptive designs will prevail because they provide primarily clear ethical benefits and not just because they bear the potential for cost savings. 

A prerequisite for successful implementation of adaptive designs is an understanding of the underlying methodology. What follows are three of the most popular misconceptions around adaptive designs. 

Misconception one: There are certain areas of confirmatory clinical research where adaptive designs are more applicable and other areas where adaptive designs are less or not applicable.

 Virtually every clinical trial can fail. There are two main reasons for a negative trial: The compound tested did not work, or the outcome of the trial was inconclusive. The latter has to do with statistics—and with ethics. 

Traditional statistical designs are based on assumptions of prospective effects. Should these be imprecise (and they normally are), the trial's power to provide the desired proof of evidence is reduced and, consequently, patients might be needlessly exposed to a harmful treatment. The ethical aspect here is that the trial could have been prevented from being negative had the statistical design considered the implicit uncertainty of the planning assumptions. This is where adaptive designs come into play and is also the reason why ethical research programs should always comprise an element of adaptation. 

To the extent that virtually every confirmatory trial is based on uncertain effects, every trial design should comprise an element of adaptation—at least a check of the planning assumptions in order to not waste the entirety of the data. Accordingly, adaptive designs are applicable in all kinds of confirmatory research. Once the technical complexities of implementing an adaptive trial are resolved, the disadvantages (i.e., increased workload and more sophisticated statistics) will be outweighed by their advantage: improving the success rate of trials. 

Misconception two: Adaptive trial designs are characterized by unmanageable complexity and less careful planning.

 There is an apprehension—not completely unjustified—that a normal adaptive trial might present itself as a seamless, dose-finding, effect-confirming, combined Bayesian/group-sequential study that allows for treatment arm selection, response-adaptive patient allocation, sample size recalculation, and an adaptive choice of endpoints and hypotheses. Do not be afraid or confused by these high expectations, excited debates or the hype surrounding adaptive designs. The standard adaptive trial is a Phase II, III or IV trial with a small number of preplanned interim analyses that focus on either moderate sample size adjustments to rescue a potentially underpowered study or smarter treatment arm selection. 

Let's say that today's adaptive designs save 10% of all clinical trials that otherwise would have failed. This represents a great deal of cost and time savings, as well as more efficient resource utilization. As the methodology matures, the industry will probably see more complex designs in the future, but only at the pace that the systems in clinical operations improve and adapt to the new technology. However, and most notably, the ideal adaptive trial is a trial without any adaptations and with as few interim analyses as possible. 

Adaptive designs are characterized by thorough preplanning and appropriate use of statistical methods to optimally address the research question. They are also characterized by careful consideration of the principles of good statistical practice in order to maintain the trial's validity and integrity, as well as by exact type I error control. For example, take a look at an adaptive trial protocol—it is quite sizeable. It comprises the effect size assumptions; simulations; justification of stopping rules; number and timing of interim looks; extensive elaboration on type I error control and statistical bias control; description, motivation, and impact of potential design adaptations; and justification of methods to preserve the blind. 

Above all, adaptive trials cannot be conducted without permanent statistical monitoring. Usually there are quite a few  statisticians involved: the blinded project statistician, un-blinded statistician, and independent external statistician. In summary, one can say that adaptive designs require even more preplanning. They are definitely not a "remedy for poor planning."

  By nature, adaptive designs are subject to more intensive external control. In addition, adaptive designs result in an increased demand for qualified statisticians. 

Misconception three: Adaptive designs require smaller sample sizes than traditional designs.

 No, usually they do not. The reason is that human beings lean toward wishful thinking. On average, drug effects are overestimated and the variability of drug effects is underestimated. As yet, trials have either been unknowingly underpowered or intentionally overpowered. In the latter case, an adaptive design is more or less unnecessary (aside from the questionable ethics of overpowered trials). The unknowingly underpowered trial, however, is where adaptive designs come into play. By design, they try to maintain the power at a prespecified level; hence, on average they result in a larger sample size compared to the traditional design. 

However, in the long run, this effect is outweighed by another effect of the adaptive design: If a potentially underpowered trial is rescued, the entire sample is not lost

it contributes to confirmatory evidence and does not need to be collected again. This is a patient-sparing effect. Overall, adaptive designs make better use of the patient as a resource. Trials no longer need to be overpowered, and the number of underpowered trials is reduced. So, from a different perspective, is it true that adaptive designs require smaller sample sizes than traditional trial designs? Yes, in the long run they do. 

A profound understanding of the statistical methodology is the cornerstone of a successful implementation process. At first sight, it sounds so simple. Instead of combining patient data, adaptivists combine p-values from separate stages (i.e., interim analyses). Astonishingly, however, methodological expertise has not spread very far since the idea was first published.

  The group of statisticians around the globe who publish most of the statistical work is still relatively small, and professional training of industry and CRO statisticians is still in its infancy. 

One of the intriguing features of adaptive trials is that they offer a countless number of design options. For example, adaptive group-sequential designs involve a predetermined number of interim analyses, whereas in recursive combination test designs, the number and timing of interim analyses is usually not preplanned—just like other design features (e.g., effect assumptions, stopping rules). The differences between these designs do have an impact on the overall probability of success. They also have an impact on the logistics of the trial, such as data management and monitoring procedures, EDC/IVRS, drug supply management, and DMC operating procedures. The different "behaviors" of the designs need to be well understood and thought through. What this really means is that the design determines the practical performance of the trial. 

Beyond the computerized systems needed for the implementation of adaptive designs, the importance of statistical software cannot be overemphasized. For regulators, it must be a nightmare to check statistical results from a poorly described adaptive trial to find out whether the p-values and confidence intervals provided by the applicant are correct or not. Similarly, it is a nightmare for the applicant should the statistical conclusions not be accepted by the agency—and the burden of proof always falls on the applicant. 

The availability of statistically sound, validated software that mimics the adaptive course of the trial while preserving the type I error rate is crucial, as adaptive designs are far more complex than traditional, nonflexible designs. A proper software package spans a wide range of designs from clinical trial Phases I to IV and integrates tools for:  

With adaptive designs, the era of handmade statistics is over. With traditional designs, an experienced statistician was able to do most calculations (e.g., planned sample size) without a software package. By contrast, this is no longer feasible with adaptive designs. Also, the analysis, previously done almost entirely with SAS, requires a new type of software that considers the various multiplicity issues and provides bias correction tools. For instance, a p-value is no longer a simple p-value in the traditional sense, and the calculation of confidence intervals becomes more sophisticated. Moreover, the adaptive software steers the process of adapting the trial and correctly addresses the impact of an adaptation on the statistical inference. 

As trials become adaptive, their statistics become dynamic. Formerly, p-values were calculated on static data at the end of the trial. An adaptive p-value considers the dynamic character of the data, namely when and how often the data have been unblinded, at what level of inference, based on what kinds of adaptations. Adaptivists should consider three basic rules:  

Use only designs that can be simulated and analyzed with your software.

Use only inferential statistics that were calculated with your software.

All major pharmaceutical companies are preparing themselves for big changes in the clinical research and development environment. Their ultimate goal is to reduce failure rates. Adaptive designs provide the means to achieve this goal. But the industry's usage of adaptive designs provides the real challenge—a technological challenge. The technological hurdles cannot be overcome without the help of clinical technology providers that can facilitate access to trial data and provide the systems to manage adaptive trial data appropriately. These include:  

Electronic systems that give real-time access to clinical data that manage resources and allocate patients in a way that fulfills the requirements of adaptive designs

Software that generates accurate, valid statistical inference and steers the process of design, simulation, and adaptation.

Therefore, technology is likely to play the most important role in addressing the issues that the new methodology has raised. And technology is likely to change the traditional role of pharmaceutical service providers as well. Services companies are about to build new service offerings for a changing clinical trials landscape. The focus will shift from ground force to technology, and the industry will likely see more strategic partnerships between sponsors and service companies. 

 is vice president, North America, at UBC-ClinResearch. 

* is general manager, clinical technologies, for United BioSource Corporation, 55 Francisco Street, Suite 780, San Francisco, CA, 94133, email: 

*To whom all correspondence should be addressed.

1. J.A. Quinlan and M. Krams, "Implementing Adaptive Designs: Logistical and Operational Considerations," 

2. O.T. O'Neill, "FDA's Critical Path Initiative," 

3. P. Bauer, "Multistage Testing with Adaptive Designs," 

Biometrie Informatik in Medizin und Biologie

4. V. Dragalin, "Adaptive Designs: Terminology and Classification," 

Feature Article

A clear grasp of the methodology behind the design is as important as the software that runs it.

Top Three Misconceptions of Adaptive Trials

The main obstacle to patient relevant reporting during the development of new drugs is the recommendation for randomized clinical trials (RCT)—the gold standard. Clinical trials complying with this recommendation represent the most scientifically valid study design for evaluating the safety and efficacy of new medical treatments. However, for patient relevant data, the usual pivotal clinical trial may not always represent the appropriate vehicle for generating information on the safety and cost-related issues of medical treatments.  

Within this artificial setting, which includes lengthy inclusion and exclusion criteria to maximize therapeutic effect, you cannot obtain any meaningful data concerning safety issues associated with the treatment or concerning cost effectiveness. So, how could one solve this catch-22 situation of RCT versus noninterventional study settings? 

The straightforward approach to managing the so-called fourth hurdle (besides quality, efficacy, and safety) is data collection during Phase IIIb studies and in real life settings after marketing authorization (i.e., Phase IV). The real world of patient care offers a substantial number of patient datasets that could be retrieved through a disease or product registry. 

Noninterventional data retrieval is one of the most prominent areas for EDC, as easy-to-use solutions allow for the timely collection of data. But be reminded that EDC implementation is not simply a computer system implementation. In fact, EDC technology is only the tool that enables the critical changes that lead to benefits. 

On the one hand, it is quite easy for a pharmaceutical company to commit to using EDC as its standard for executing clinical research. On the other hand, however, it is very difficult to understand how to make that commitment a reality. To benefit from EDC systems, everyone involved in clinical research has to modify the way they work—since as clinical research processes change, so too do the underlying roles. 

The EDC transition primarily affects data management, site monitoring, and project management (project leaders are responsible for establishing explicit targets for a given EDC project, relative to both timelines and expected benefits). But the role changes will not just affect the core clinical research team; they will also affect everyone involved in the clinical research process. 

For example, when using EDC, data management must be involved much earlier in the study cycle. Also, clinical research associates, investigators, and site staff should all expect a shift in workload from a poststudy peak to a steady flow throughout the life cycle of the clinical trial. And prior to implementation, there is the need to develop a customized training program for EDC (e.g., the optimal approach to training for a given team). 

In conclusion, EDC is more than just a new technology. It is indeed the new way of thinking about clinical research. The implementation of EDC while maintaining the processes that support a paper-based system is a crucial assignment. The most fundamental prerequisite regarding the success of EDC is change. In other words, all clinical research processes have to be redesigned and all key roles have to be updated to integrate efficiently with the EDC solution. Until this is accomplished, the benefit potential of EDC will not be reached. 

A Closing Thought

Norbert Clemens, MD, head of clinical development for CRS Clinical Research Services, explains that EDC is more than just technology; it is indeed the new way of thinking about clinical research.

Make Room for Change

Spain's Agencia Española de Cooperación Internacional,

 based in Madrid, to aid in the research and development of new, effective, and affordable drugs for neglected diseases, including Chagas disease, human African trypanosomiasis, and malaria. DNDi plans to develop two new malaria drugs by the end of the year and is looking to create between six and eight new drugs for neglected diseases by 2014. 

 Public Health Minister Chaiya Sasomsap announced that Siriwat Thiptharadon, head of Thailand's Food and Drug Administration, was removed from his position and moved instead to an inactive post. Thiptharadon is known for establishing the former government's compulsory license program, which aimed to make drugs more affordable and is now vulnerable to a committee review under Sasomsap's orders. Chatree Banchuen was named secretary general of Thailand's FDA following Thiptharadon's departure, but resigned a week later citing that he was "uncomfortable with the politics." 

 (HVO; Washington, DC) hematology training program, created by HVO and the 

 (Washington, DC). The program's purpose is to bolster the hematology training of clinicians, lab technicians, technologists, and students. 

 (Hampshire, UK) is collaborating with brain tumor charity 

 (Hampshire, UK) among others to create the UK's first dedicated laboratory-based brain tumor research center. Brain tumors are the most common form of cancer in people under   40 and the most common cause of death in children after accidents.

The Drugs for Neglected Diseases initiative (DNDi; Geneva, Switzerland) received ?5 million from Spain's Agencia Espa?ola de Cooperaci?n Internacional, based in Madrid, to aid in the research and development of new, effective, and affordable drugs for neglected diseases, including Chagas disease, human African trypanosomiasis, and malaria. DNDi plans to develop two new malaria drugs by the end of the year and is looking to create between six and eight new drugs for neglected diseases by 2014.

April Global News

By the time you read this, the European Union will have announced the formal start of its so-called Innovative Medicines Initiative—and the clinical trials support programs that go with it. 

The initiative, backed by the European Union—with an initial grant of €122 million ($200 million)—and the European pharmaceutical industry, claims to support more efficient discovery and development of better medicines by removing research bottlenecks in the current drug development process. No new medicines will be developed as such. The focus will be on the delivery of new approaches and technologies and improved knowledge management of research results and data. 

Under the broad title of improving the predictivity of efficacy evaluation, it is going to address improved understanding of disease mechanisms, predictive pharmacology, the identification and validation of biomarkers, patient recruitment, and benefit-risk assessment. 

One of the projects to be supported in the first phase deals with the qualification of translational safety biomarkers. The rationale is that a lack of specific and sensitive mechanistic safety markers and their respective assays for human samples is regularly delaying drug development programs. In addition, the predictivity between nonclinical and early clinical studies of currently accepted markers is very poor, says the project outline. 

As the initiative points out—and here, the voice of industry can be heard clearly—there is still no clear process on how to qualify new safety biomarkers for clinical regulatory decision making. Since no official process exists yet, cooperation is vital in the definition of a generic process and in testing several options before one could be accepted by all parties. 

The project will focus on three target organs as examples of critical drug-induced pathologies (liver, kidney, and vascular). Based on previous discoveries and other preclinical qualification exercises, a subset of markers will be identified and their assays developed for human use if they are not yet available. The selection of the markers will be based on their potential or probable success of becoming a useful translational tool. This five-year project is promised assistance in personnel, equipment, and material worth €21 million from European pharmaceutical companies. 

Another five-year project with industry backing worth €20 million will look at surrogate markers for vascular endpoints. It aims to find validated and scientifically justified biomarkers or surrogate endpoints for micro- and macrovascular hard endpoints in diabetes clinical research. Its starting point is that patients with type 1 or type 2 diabetes eventually tend to develop micro- and macrovascular compli-cations—retinopathy, nephropathy, neuropathy or atherosclerosis, with the associated risks of myocardial infarction or stroke. "The factors that contribute to these complications are not well understood," says the project outline. "There is a need to develop ways to reduce the size and timelines for evaluating therapeutic efficacy on the establishment/progression of micro and/or macrovascular complications." 

It adds that the project will also attempt to respond to the lack of animal models that can predict the development of diabetic micro- or macrovascular disease, which remains a major issue in the development of novel therapies. The project will therefore also aim to develop and validate new in vitro or in silico tools to test novel therapies. 

Neurodegenerative disorders are targeted in another five-year project with €7.5 million industry backing. Because of the notorious length and cost of clinical outcome studies in this field, the project will aim to accelerate the successful development of molecules. To do this, it says, it is essential to improve the predictive value of animal models, identify pharmaco-dynamic markers of drug response, and develop pharmacodynamic models that allow early prediction of efficacy and markers to aid in the stratification of the patient population. The project requires the integration of preclinical and clinical science to ensure translation of efficacy from bench to bedside and vice versa. 

The project focuses on the development of translatable animal and human volunteer models predictive of clinical efficacy in patients in the areas of Alzheimer's disease, Parkinson's disease, and multiple sclerosis. One long-term objective will be to exploit similarities in these diseases to develop models that support clinical development across all three diseases. 

Discovery, research, and development of new treatments for severe asthma is the subject of another project. In particular, the project says, there is a need for effective use and further development of diagnostic criteria for mechanistic and therapeutic trials. Without better understanding of disease etiology and pathogenesis, relevant preclinical and clinical models cannot be developed to enable translational research strategies. 

The project aims to build an EU consortium focused on understanding severe asthma. This will set up a large, longitudinal patient cohort that will enable research to validate novel biomarkers and clinical measures and will serve as a vehicle for developing translational models. Industry support of €12.5 million is to be made available over five years. 

Meanwhile, industry rumblings continue over the deficiencies of the European Union's clinical trials directive. The European biotechnology industry association, EuropaBio, is continuing to demand a review of the much-maligned measure so that it actually improves harmonization of procedures to approve the conduct of clinical trials in Europe—thus making industry's life easier. So far, seven years after the directive's emergence and six months since the EU authorities agreed to listen to complaints about it, the procedures remain divergent across the 30 European countries that are supposed to have signed up to simplification. 

Top of the list of EuropaBio's specific complaints is the lack of harmonization for applications for clinical trial authorizations, because national requirements on dossiers still vary from country to country. This leads to increased bureaucracy and to constant uncertainty, particularly when a trial is being conducted in two or more countries. Other persistent EuropaBio complaints include different interpretations of the definition of investigational medicinal products, varying requirements for safety reporting, and some countries' approach to good management practices. 

"As a result, Europe is now regarded as a less attractive location to undertake clinical development. Indeed, some member companies have already decided not to conduct their clinical studies in the EU. If the current situation is not addressed and improved, it will be particularly damaging to the continued viability of the bioscience sector in the EU," says EuropaBio. The impact on smaller firms is significant since they lack resources to cope with the  administrative burdens and consequent delays. 

Quite distinct from the plans for the innovative medicines initiative, a different form of the EU-backed project is already underway that might serve to ease the objections in the clinical trials community to the directive. Under the EU's framework program for support of research of all kinds, a one-year project is aiming to measure and analyze the direct and indirect impact of the directive and related legislation in the EU. Its target is wide-ranging: the impact on all categories of clinical research and on all parties involved—commercial and noncommercial sponsors, ethics committees, and competent authorities. The project is being coordinated by the European Forum for Good Clinical Practice, and involves the European clinical research network (ECRIN), the European cancer research organization (EORTC), the Hospital Clinic of Barcelona, and the ethics committee of the Medical University of Vienna. 

"This initiative fits with the need to adapt the current legislation and will help determine the most relevant pathways for improvement," says the project outline. It cites that although the directive's objective was harmonization of the EU regulatory environment for clinical research and improving the protection of participants, "this legislation only protects participants in clinical trials on medicinal products." 

In addition, continues the project outline, the directive is indiscriminate in its effects. "It requires almost similar procedures for all types of clinical trials with medicinal products, from registration studies on innovative treatments to studies comparing treatment strategies using marketed drugs or applying minimally invasive procedures." As a result, it concludes "academic institutions and the industry, including smaller firms, face major difficulties in fulfilling their sponsor's responsibilities." It shares EuropaBio's view that the directive's transposition into divergent national legislation missed the harmonization goal and makes multinational trials difficult to perform. 

Against this background, it is instructive to read the section dealing with implementation of the clinical trials directive in the just-published 2008 workplan for the European Medicines Agency. The agency says its objectives and main initiatives in this area are to support the implementation of the directive (and the linked good clinical practice directive that appeared in 2005), and the next phase of incorporating aspects of the new pediatric regulation into Europe's EudraCT database of clinical trials. 

As theatre critics say about a show for which they predict a long life, "This one will run and run."

 is a freelance journalist who specializes in European health affairs and is based in Brussels, Belgium.

View from Brussels

With backing from pharma and high hopes, Eu takes steps to improve front-end approaches to R&D.

EU Rolls Out New Medicines Initiative

Adaptive or flexible trial design refers to a conceptual clinical trial methodology that allows for modifications to take place after the trial has started without compromising the scientific method. For all forms of adaptive design, the important distinction that is critical to maintaining the research perspective is that the decision-making rules governing the adaptation must be clearly stated (described and justified) in the study protocol. 

( Photography: Jim Shive    Illustration: Paul A. Belci) 

In an adaptive design setting, sponsors are empowered to make modifications in response to data collected during the trial as long as the response action and decision-making rules are predefined. These Scientific Study Modification Points (SSMPs) can occur frequently (in a fully sequential design) or at other predetermined intervals. 

Adaptive trials by design have midstudy analysis and decision points that will change the way a study is conducted. As adaptive trial methodologies are relatively new to most pharmaceutical and biotech companies and their vendors, there is the potential that midstudy modifications to the study design could have a negative impact on the clinical trial if not properly executed. 

Kesler and Helms summarized some of the logistical and technological needs of adaptive trial designs well,

  including the need to access clean study data as quickly as possible to make trial decisions. Adaptive studies require central, adaptable randomizations (either IVRS or Web-based); the ability to stop randomization quickly; and the ability to mask pruning of treatment arms from sites. Adaptive trial design also requires expedited monitoring to get the most data available for the interim analyses. The team should also have analysis programs ready in order to display interim results quickly. 

The communication and teamwork required for adaptive trials are very different than the linear approach taken in traditional trials (See Figure 1). In an adaptive trial, communication requires that team members are brought together earlier and more often throughout the trial (see Figure 2). The members need to be involved at the SSMPs to analyze the impact of modifications to their areas of study conduct. Once analyzed and decisions made, the predetermined logistics required will be executed by the team members and/or automated systems. 

Also in an adaptive trial setting, a monitoring board might have the option of responding to interim safety and efficacy data. These data may come from the trial itself or other new information in the drug development process that was not available at study's start. The Independent Safety and Monitoring Board can respond to the new information in a number of different ways, including narrowing the trial focus or increasing the patient population. An example of narrowing the trial focus might include removal of one or more of the treatment arms based on predetermined futility rules. Alternatively, if the data available at the time of the review does not allow for a clear decision between utility and futility, it might be decided to expand the enrollment of patients on one or more treatment arms beyond the initially targeted sample size. 

These options are available in an adaptive trial, unlike in a standard trial where safety and efficacy data may be reviewed by a monitoring board during scheduled interim analyses. The response to this data in a standard trial is generally either amending the protocol or stopping the trial and starting a new one. Either response is costly to timelines and budgets. 

The adaptive trial environment is much different than the traditional trial environment, where hierarchy and stepwise communication is the norm. In an adaptive trial, the teams are interactive, synergistic, and highly engaged. This is necessary in order to respond to the SSMPs. Other characteristics of an adaptive clinical trial include the following: 

Early planning with full scenario development.

 Very early in the protocol design process, all possible statistical variations and research outcomes need to be discussed and planned. It is critical that all downstream teams are involved in this early scenario development. Teams to be involved include clinicians, statisticians, project management, data management, drug supply management, and regulatory/quality assurance. All these teams need to have input on the developed scenarios and the impact on their disciplines. Without their early involvement, there is the chance that quality and logistical issues will arise that could lead to abandoning the adaptive trial. 

Flexibility to accommodate midstudy changes.

 People, supplies, systems, and quality must be designed and built so that all components can quickly adapt to SSMP analysis and change. Early decisions must be enacted quickly for maximum benefit. The inability to efficiently adapt to study changes has delayed and reduced the effectiveness of many adaptive trials. 

Periodic study analysis must have access to clean data.

 The data points and delivery must be constructed early on so that prompt review of current data can be made. In the case of response adaptive dose-ranging studies that are fully sequential, "periodically" really means instantaneously; the randomization scheme must be supported by technology (IVRS or Web-based) that accesses previous randomizations and efficacy and/or safety data virtually while the site is on the phone. 

Once decisions have been made, implementation must be done rapidly and with integrity. This often requires changes to be invisible to sponsors, patients, and investigators and the technology and team communication to be quick, complete, and seamless. 

To facilitate the success of adaptive trials, one suggestion is to use scenario analysis with patient and drug management simulation. The key aspects for any scenario development are patient activity and drug supply needs for that activity. A scenario will encompass these areas with simulation and forecasting data. To clarify, known behavior as dictated by the protocol would be considered a simulation (e.g., visit schedules, dispensing regimes, randomization parameters, dosing decisions, titration events); forecasts are those values, activities or behavioral patterns that are predicted based on previous data, prior experiences or calculated assumptions (e.g., new patient enrollment, drop out rates, titration percentages). 

Beyond known protocol information (randomization lists, treatment arms, total patients), forecasting requires patient activity questions and answers for full scenario development. 

 This is a forecast of unknown patient behavior. This data needs to be provided by day, week or month for the length of enrollment. This is a key factor in being able to extrapolate scenario results. This data should be widely understood and agreed upon.  

 These are predicted behaviors critical to either the adaptive decision rules or drug supply. These can include drop out rates, quantity of patients for each dosing decision, percent of patients for titration activity, and any other activity that may impact drug supply management. 

Simulations then respond to those forecasts with the prescribed protocol activity. These simulations will focus around treatment arm assignment and visit schedules as well as drug supply extrapolation from all forecasts and patient simulations. The following key simulations must be developed. 

 Once the new patient enrollment is forecasted, the resultant visit schedule needs to be extrapolated. This can range from a very simple visit schedule with unchanging visit window and drug dispensing to a complex visit schedule where there are large visit windows, complex dosing decisions based on lab values, and adaptive or prescribed treatment decisions based on titration rules. 

Patient required clinical materials simulation.

 With the visit schedule extrapolated, the dispensed kits can be estimated based on protocol and forecasted or simulated values. These quantities can be summed or time-phased depending on drug supply strategies, risks, and availability. 

Supply chain required clinical materials simulation.

 This area focuses on both production planning and safety stock decisions. With the supply chain determined (world depots, country depots, sites) and the patient required clinical materials simulation done, a determination can be made on the amount of safety stock held at each supply point. Safety stock decisions include risk aversion factor; drug stability, expiration, and availability; supply point challenges; and confidence in all phased forecasts. Once the full drug demand is simulated (patient required and safety stock), then production decisions can be made based on the time-phased forecasts and simulations. 

All of these forecasts and simulations work in concert for a variety of scenario developments and analyses, allowing for managers of the various disciplines to review best, worst, and what if scenarios. The ability to forecast and simulate gives greater confidence to the success of the adaptive design and, in many cases, will sort out problems before they happen. Simulations show different implications of patient recruitment, patient randomization schemes, drug supply, product economic impact, clinical outcomes, and sample size. 

The culmination of a successful adaptive trial requires significant coordination of people, information, and technology. Implementation of traditional clinical trials allow for more waterfall and stepwise manners; an adaptive trial requires involvement from all disciplines, all the time. 

 is director, new products and services; 

 is associate director of clinical services; and 

 is vice president of clinical services at ALMAC Clinical Technologies, 1040 Stony Hill Road, Suite 200, Yardley, PA, email: 

1. K.L. Kesler and R.W. Helms, "Using Adaptive Designs: A Case Study and Lessons Learned," DIA Statistics Conference, Washington, DC, March 13-15, 2006. 

2. V. Dragalin, "Adaptive Designs: Terminology and Classification," 

Team and technology benefits are realized at the front-end of the adaptive trial design process.

Dissecting Adaptive Design

It's no secret that the clinical trial process is imperfect. While clinical research paves the way for innumerable medical benefits, it is not without its faults—and according to a recent report, some of these deficiencies are undoubtedly cause for concern. 

 Suggestions to Help Reduce Clinical Trial Disparities 

The EDICT (Eliminating Disparities in Clinical Trials) Initiative, conducted by the Chronic Disease Prevention and Control Center at Baylor College of Medicine (Houston, TX) and the Intercultural Cancer Council (ICC), is a multiyear evaluation of U.S.-based trials aimed at improving policies that directly impact the conduct of such studies. 

"The EDICT Team is not the first to examine disparities in clinical trials, but to our knowledge, no single project has adopted a systems approach designed to assess all relevant factors contributing to such disparities as well as all stakeholders that exert effects on the overall policy context," asserted Daniel Goldberg, a member of the ICC and an EDICT Policy fellow, to 

. And indeed, their research was highly intensive. The project was broken into two segments: the Policy Arm and the Field Research Arm. The Policy Arm held a roundtable and identified nine different "opportunity areas," which were evaluated by teams of about 300 public, private, and nonprofit stakeholders, who then drew up Policy Recommendations meant to help eliminate specific disparities (for further details regarding the EDICT Policy methodology, please visit 

www.bcm.edu/edict/PDF/EDICT_Project_Methodology.pdf

The report identified three areas where clinical trials are particularly deficient: the under-representation of women, minorities, and the elderly; lack of disparity training among institutional review board (IRB) members; and the duplication in research efforts. 

"This under-representation [of women, minorities, and the elderly] is problematic because...it makes no scientific sense to introduce medicines and interventions into populations who are wildly different from the study population," observed Goldberg. "For example, it is difficult to understand the effect of a given biologic in a population with significant co-morbidities if the relevant study systematically excluded all persons with co-morbidities. Excessive focus on ideal study conditions and internal validity may significantly undermine external validity." He went on to note that IRBs' lack of training in disparities helps fuel this problem, and that if they were trained properly, they could accurately cite disparities and help remedy them. 

As for the third prominent disparity identified in the report, Goldberg noted, "Research efforts are often duplicated because there is no standard practice for allocation policy. Economic considerations drive allocation in the private sector, and political considerations drive allocation in the public sector." He recognized that since diseases with "organized and well-funded advocacy efforts" generally garner the most focus, less common diseases receive less attention, regardless of fatality rates. 

To help fix these problems, the report laid out nine policy solutions, which are listed above in the sidebar. 

While these disparities have pervaded clinical trials for quite some time, the clinical trials community is certainly able to come together to eliminate them. 

"There are always barriers in working for policy change, because policy is inevitably contested space," said Goldberg. "Ideally, the report will stimulate the clinical trials community to understand that disparities in clinical trials can no longer be tolerated, for both scientific and ethical reasons. Moreover, the EDICT Team, with its diverse body of collaborators, has taken pains to ensure that its recommen-dations systematically address all levels of the clinical trials enterprise, primarily to avoid recommending unfunded mandates. The hope is that the bulk of the EDICT Team's Policy Recommendations are feasible in the short term." 

EDICT Initiative pinpoints areas in which clinical trials policies need improvement.

Study Assesses Clinical Trial Disparities

For two decades now, companies sponsoring clinical research have openly acknowledged that protocol design negatively impacts clinical trial performance and may well be the single largest source of delays in getting studies completed. But the magnitude of the problem and the challenge of re-engineering how protocols get crafted have squelched the will to attempt a fix. Investigative sites are instead admonished for sluggish enrollment and rising dropout rates. 

The real issue is that many patients no longer qualify for or simply lack the stamina to participate in the kinds of studies being rolled out by drug development companies today. In terms of procedures and their frequency, protocols are more demanding than ever before. That represents a growing burden to study subjects in terms of their time and comfort. It also places a greater workload on investigative sites for fewer relative dollars. 

Key protocol design elements changed dramatically between 1999 and 2005, as was shown in a recent study conducted by the Tufts Center for the Study of Drug Development and soon to be published in an upcoming issue of the 

. More than 10,000 unique Phase I–IV protocols formulated by over 75 pharmaceutical and biotechnology companies across a wide range of therapeutic areas were scrutinized. 

During the six-year period, the number of unique procedures per protocol (e.g., routine physical exams, blood work, and heart activity assessments) and their frequency increased at an annual rate of 6.5% and 8.7%, respectively. In 2005, across all therapeutic areas and research phases, 158 procedures were conducted—an average of 4.5 times the number of unique procedures—during the course of a clinical trial. Unique procedures were conducted on average 5.4 times during the course of a Phase I clinical trial, 6.5 times during a Phase II trial, 4.0 times during a Phase III trial, and 3.1 times during Phase IV trials. 

The Tufts study found that questionnaires and subjective study volunteer assessments were the fastest growing procedure type. While these procedures may provide valuable information, they also require a higher level of volunteer compliance. 

The typical protocol today has nearly 50 eligibility criteria. Whereas the number of exclusion criteria per protocol remained stable over the time period measured, the number of inclusion criteria saw nearly a three-fold increase. 

Study conduct performance on 57 individual Phase II and III protocols investigating chronic illnesses was examined and, predictably, performance worsened virtually across the board. The study controlled for geographic speed differences. The average overall duration of clinical trials increased 74%. Median cycle time from protocol readiness to both drug availability and last patient/last visit increased by a similar margin, as did median elapsed time from protocol readiness to data lock. 

Enrollment rates for volunteers who met the rising number of protocol eligibility criteria dropped from 75% to 59% between the 1999–2002 and 2003–2006 time periods, while retention rates fell from 69% to 48%. Patient enrollment cycle times increased for protocols conducted in the latter time period. The average length of case report forms grew from 55 pages to a whopping 180 pages per protocol. Consent forms also lengthened. Moreover, there were higher numbers of protocol amendments and a dramatic rise in observed adverse events and severe adverse events. This last observation may be due in large part to changes in how adverse events are defined and counted. 

Without a doubt, clinical research professionals put a great deal of care and attention into their study protocols. Yet these same protocols appear to lack adequate consideration for the human subjects participating in them and the investigative sites that must administer them. 

For investigative sites, rising procedural volume is compounded by rising work effort required to administer most procedures. Tufts CSDD created Work Effort Units (WEUs) based on Medicare's well known Relative Value Unit (RVU) methodology, which calculates payment to physicians based on the estimated value of their time and expertise to administer medical procedures. Procedures not assigned a Medicare RVU were assigned a WEU established by a panel of physicians at the Tufts University School of Medicine. 

Based on WEUs, the Tufts CSDD study found that work load on sites increased by 10.5% annually between 1999 and 2005. The burden on investigative sites to administer protocol procedures is rising at a faster rate than the number of unique procedures and their frequency. For Phase I trials, investigative site work burden mushroomed by 14%. Ophthalmology, oncology, and pharmacokinetics protocols place the highest work burden on sites at this time. Work burden has increased most in gastrointestinal, hematology, pain, and anesthesia studies. Yet grant funding per procedure declined over the Tufts CSDD study period by 8% annually. 

The results of the Tufts study undeniably suggest that the current and intense focus of sponsor companies and CROs on micromanaging investigative sites to drive better study conduct performance is to a large extent misplaced. Until the spotlight is shone on the protocols themselves, and the internal processes to design and approve them, the impact of performance improvement strategies will be marginal at best. 

A better way to accelerate clinical trials begins with sponsor companies benchmarking their own protocol designs against those gathered by the Tufts Center analysis of 10,038 protocols. Doing so would give sponsors a starting point for identifying, balancing, and prioritizing what is really necessary to have in a protocol and what is merely nice to have. 

Alternatively, companies could look at the protocols they're now developing and apply the Tufts CSDD WEU methodology to assess the work load those protocols will create for investigative sites. That would go a long way toward raising awareness that every protocol decision has a downstream effect on speed 

As companies that have peeked at these problems can attest, design elements are often brought into protocols because influential staff will champion them or because they ask scientifically intriguing questions. They don't necessarily meet a regulatory obligation and might not even be a good idea. 

The current protocol design process clearly needs attention. The incidence of protocol amendments continues to rise, adding to an already cost intensive activity. Self-reported estimates to implement a single amendment can run a sponsor company anywhere from $250,000 to $450,000, and biopharmaceutical companies estimate that they make an average of three amendments for each Phase I protocol and three to five amendments for each Phase II and Phase III study. 

Assessing the feasibility of protocol designs—with a heightened sensitivity to the impact on investigative sites to administer them and on patient willingness to volunteer for and stay in them—is a critical step that must be taken if sponsor companies hope to achieve sustainable drug development efficiencies. Today's safety sensitive operating environment, coupled with the changing nature of therapies in the pipeline, will encourage yet more growth in the number and frequency of procedures per protocol. But the notion that more is better must be challenged for its scientific value and its impact on performance. The burden of protocol design is mounting. The price of doing nothing may have reached its pinnacle. 

Clinical Trial Insights

More complex and demanding protocols are hurting clinical trial performance and success.

The Heavy Burden of Protocol Design

Oncology trials in the community are making strides for one research network. 

 ACORN Community Oncology member practices consist of over 30 locations and 350 network oncologists. 

The Accelerated Community Oncology Research Network (ACORN), a national oncology clinical trial network, recently achieved two important milestones. The first is a doubling from its projected number of studies for 2007; the second, a doubling of the national average of African-Americans enrolled in its oncology studies. 

Steve Coplon, chief executive officer of ACORN, told 

 that its network model of community-based oncology clinics is the reason for these performance numbers. "We have oncologists that want to do the best trials and be involved in the major advances in oncology, including the new molecules and combination therapies." The oncologists, with access to the patients in the community—on average, 85% of oncology patients nationwide receive their care in a community center—can then utilize the centralized CRO services offered by ACORN to help make clinical trial conduct more seamless. 

The CRO portion of ACORN launched two years ago. It includes centralized clinical trial services, scientific analysis, and provides contract and budgeting services; subject accrual services at the site level; and data collection and monitoring. "Because we have been practicing cancer care in the community for a number of years, on our CRO side we then understand what it takes to make clinical trials more effective. We understand Medicare, and what it will cover, and issues like that," explained Coplon. For him, however, the ultimate compliment came from one network oncologist, who said: "You make research easier." That physician, said Coplon, wouldn't have the time to do the administrative work required for clinical research in his busy clinic. 

ACORN has about 25 lead oncologists who champion clinical trials, with access to over 350 networked colleagues. "Not all centers do the same trials. They go to where they are effective," said Coplon.   

Coplon said it exceeded 100 trials in various stages in 2007. "We tend to look at trials in cycles of 24 months—open, close, accrual." The first quarter of 2008 is also on track to meet or exceed trial projections.  

Although many patients may be interested in participating in an oncology clinical trial, Coplon named three obstacles to that participation: trial availability in that community; physician participation in the trial; and finally, being able to meet the criteria. "We have doubled—and in some cases tripled—our accrual in adult populations," said Coplon. In 2007, African-Americans accounted for 20% of ACORN's patients. And while African-Americans make up 12.9% of the U.S. population, NCI data show that they represent only 7.46% of clinical trial participants. Coplon attributes ACORN's success on this minority enrollment to education and access. "We are focused on educating patients and to help them understand what a clinical trial is and the care they will receive," said Coplon.  

With ACORN's care continuum, it is also able to provide analysis from its data warehouse of both clinical trial data and patient reported outcomes. This capability, according to Coplon, allows ACORN to see differences both scientifically and from a patient perspective. He adds: "It is a unique capability." ACORN is on the Web at 

For more topics related to oncology and clinical trials, please see the supplement "Oncology & Clinical Trials in the 21st Century" enclosed with this issue, or visit us on the Web at 

Oncology trials in the community are making strides for one research network.

Trials Where They Make Sense

As biopharma firms, medical device companies, research institutions, and CROs make the transition to EDC, they must ensure that their IT infrastructures are up to the rigorous regulatory and audit requirements for these applications. Clinical trial data raises the bar for managing business critical data.

 (Photography: Robin BartholiCk, Getty Images) 

Since Phase Forward's IT team manages operations that handle approximately 1.2 million customer transactions per day, the team has evaluated and implemented a full range of technologies and strategies to build a high-performance, scalable, and reliable infrastructure. The team ensures that the clinical trial data for which they're responsible is being entered, stored, accessed, and analyzed on the best available network and leveraging the industry's top technology. They understand that running more efficient clinical trials does not rest solely on the shoulders of the EDC vendor and its proponents. IT must work hard to leverage the latest technological innovations.  

As organizations implement or expand their EDC initiatives or evaluate the option of outsourcing the functionality, the following topics provide a checklist of approaches and solutions to consider to help ensure that trial data is captured accurately and can be accessed reliably—anywhere, anytime. 

SAN architectures create networks of shared storage devices, so that all devices are available to all servers in the network. With SAN technology, remote storage devices such as disk array controllers, tape libraries, and CD arrays need not be physically cabled to a particular server in order to store data from that server. Instead, these remote devices appear to the operating system as locally attached units. Sharing storage in this manner provides greater flexibility because storage capacity can easily be shifted from one server to another. Since storage can be dynamically allocated, resources are better optimized. 

In addition, SANs are architected to facilitate data recovery. Should a database server go down, data can be restored to the point of failure. This approach contrasts with tape backup, which typically captures data on a regular interval (perhaps every two or three hours). If a server fails, it can only be recovered to the last tape backup, and there may be gaps in the data. 

There are specialized monitoring platforms to choose from, each optimized for various components in the technology stack. For example, there are monitoring platforms that are suited for network, security, server, and storage infrastructure monitoring, and there are platforms for applications and database monitoring. 

A new generation of Web site monitoring software provides a view of site performance as experienced by the end user, in real time. Operators can monitor every transaction by every user in real time, enabling the operator to detect and isolate problems and more rapidly resolve them. This helps operators to determine if the issue may be occurring in the data center or at the remote site. Real-user monitoring technology can prove invaluable in troubleshooting problems anywhere in the world. The benefit of this approach for the user is experiencing the upside of improved performance with nominal to no change from their vantage point, since it doesn't require agents on the user level and can all be done on the network level. With a very low footprint, there's no potential for another technology bottleneck. 

Real-time monitoring solutions such as Coradiant's TruSight and Web-I offer the advantage of being easy to implement and have no negative impact on transaction performance. In making the choice for monitoring solutions, a critical factor is making sure the solution you choose doesn't add to the overhead or load on the system, thereby increasing the chance of performance issues with your application or network. The challenge is in integrating infrastructure monitoring tools with end-user experience monitoring tools. 

Aggregating and correlating metrics data from monitoring platforms that are for network, security, server, and storage infrastructure monitoring tools with metrics data captured in real-time, end-user monitoring data provides a point-in-time view of the end user's applications experience in context of utilization, performance, and capacity of the underlying infrastructure supporting that application. With that level of integration in monitoring tools, it provides baseline metrics by which threshold policies can be applied to trigger alerts and proactive escalations and actions to remediate infrastructure problems that may be causing poor end-user applications performance. 

Virtualization allows a single hardware device to run multiple operating systems and applications, which provides greater flexibility and more effective utilization of the hardware capacity. It was pioneered in the 1960s, when early versions of the technology were used to "partition" mainframes into multiple "virtual machines." 

Virtualization software can be thought of as a layer on top of the operating system, which allows the hardware device (typically a server today) to be viewed as multiple virtual machines. When this approach is used across a series of servers in a data center, it provides for higher performance and resource utilization. 

Organizations turn to virtualization to reduce overall operating spend, simplify application deployment, and consolidate servers. This technology delivers greater scalability and efficiency when running large numbers of trials. It also allows for the dynamic reallocation of server processing resources as needed around the world, when particular trials have peak periods of demand. 

There are a number of virtualization software technologies that are available today, including VMWare, Citrix (formerly XenSource), and Microsoft (formerly Connectix). VMWare is the market leader and first to market with a production-ready virtualization platform. 

Phase Forward adopted VMWare ESX server virtualization technology for several reasons, including:  

Robust physical server and virtual machine monitoring and management functionality in its Virtual Infrastructure Center platform

High availability and dynamic load balancing features enabled with VMWare's Distributed Resource Scheduler and VMotion technologies.

Many organizations with high capacity demands turn to data center infrastructure outsourcing options with managed collocation or managed service providers that specialize in data center technologies and with 24/7 availability. These high-availability data center operations deliver Internet connectivity, operational support, redundant power, redundant cooling, security systems, and network monitoring and management. IT professionals term the package these data center operations deliver: ping, power, and pipe. 

Whether you elect to run EDC software in your organization's data center or choose to outsource the data center operations aspect of the project, you'll need to be able to count on a robust data center at or near 100% uptime. Providers such as SunGuard Availability Systems, SAVVIS, Rackspace, and 

Terremark (formerly Data Return) provide both a global footprint along with a 100% network uptime guarantee and a robust disaster recovery solutions suite. 

Leveraging technology to build a scalable and reliable infrastructure for clinical trial data capture.

Applied Clinical Trials-05-01-2008

CDISC and HL7: aBRIDGed

Top Three Misconceptions of Adaptive Trials

Make Room for Change

April Global News

EU Rolls Out New Medicines Initiative

Dissecting Adaptive Design

Study Assesses Clinical Trial Disparities

The Heavy Burden of Protocol Design

Trials Where They Make Sense

From the Ground Up

Business and People Update May 2008

CRAs Rate EDC

Exploring Hong Kong as a Trial Destination