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Effort is being expended in investigating efficiency measures (i.e., doing trials right) through achievement of accrual and endpoint goals for clinical trials. It is time to assess the impact of such trials on meeting the critical needs of cancer patients by establishing effectiveness measures (i.e., doing the right trials). Clin Cancer Res; 18(1); 3–5. ©2011 AACR.

Background: A low proportion of adults with cancer are recruited to clinical trials. Cancer Council Victoria provides funding to clinical trial sites through its statewide Cancer Trials Management Scheme (CTMS). Historically, there appeared to be a relationship between budget-allocated funding and the number of patients recruited. A randomized controlled trial was conducted to test whether additional funding in 2013 would increase trial recruitment. Methods: A total of 18 trial centers ("sites") received usual CTMS funds, whereas 16 intervention sites received usual funds plus additional funds, proportional to recruitment in 2011; additional payments to sites in the intervention group ranged from $6,750 to $234,000 AUD (≈$6,750-$234,000 USD at the time). This represented an average 11.8% (interquartile range [IQR], 8.0%, 12.3%) increase in sites' budgets. Sites were required to use the funds with the aim of increasing recruitment. The study end point was the number of new participants recruited to trials in 2013. An online survey assessed strategies used to increase recruitment. Results: The median number of new trial recruits per site in 2013 was 21 (IQR, 5-39) in the control arm and 12.5 (IQR, 3.5-44.5) in the intervention arm. The ratio of new trial recruitment numbers at the intervention sites compared with control sites in 2013, adjusting for respective 2012 numbers and institution type, was 0.99 (95% CI, 0.69, 1.43; P=.96). The survey revealed most intervention sites used funding to increase staffing. Conclusions: Additional funding at a site level did not lead to a contemporaneous increase in trial recruitment.

Purpose Measuring the efficiency of resource allocation for the conduct of scientific projects in medical research is difficult due to, among other factors, the heterogeneity of resources supplied (e.g., dollars or FTEs) and outcomes expected (e.g., grants, publications). While this is an issue in medical science, it has been approached successfully in other fields by using data envelopment analysis (DEA). DEA has a number of advantages over other techniques as it simultaneously uses multiple heterogeneous inputs and outputs to determine which projects are performing most efficiently, referred to as being at the efficiency frontier, when compared to others in the data set. Method This research uses DEA for the evaluation of supported translational science projects by the Oregon Clinical and Translational Research Institute (OCTRI), a NCATS Clinical & Translational Science Award (CTSA) recipient. Results These results suggest that the primary determinate of overall project efficiency at OCTRI is the amount of funding, with smaller amounts of funding providing more efficiency than larger funding amounts. Conclusion These results, and the use of DEA, highlight both the success of using this technique in helping determine medical research efficiency and those factors to consider when distributing funds for new projects at CTSAs.

We report on the thoughts of a large group of scholars in the field of operations and supply chain management (O/SCM) regarding current and future issues facing our profession. Broad issues raised and addressed include a perceived lack of relevance in our research, calls from business school deans for faculty to increasingly fund their own research, greater demand for use of large data sets and methodological rigor, along with higher expectations for publishing. We invited four scholars who discussed these issues during an Academy of Management conference session in 2014, to present their perspectives within this essay. We then distributed the perspectives of each of these authors to O/SCM scholars globally so that they could add support, counterpoints, and extensions. Collectively, they raise important points regarding a need for greater innovation and creativity in O/SCM research, the challenges and opportunities of increased complexity and “big data,” the value of working in other research domains and collaborating with others, the promise of new technology, and the importance of improving how we communicate our value to business school colleagues. Finally, our contributors provide recommendations on how we may address these issues and continue to adapt and move our profession forward.

Combination therapy has proven to be an effective strategy for treating many of the world's most intractable diseases. A growing number of investigators in academia, industry, regulatory agencies, foundations and advocacy organizations are interested in pursuing a combination approach to treating Alzheimer's disease. A meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer's Disease Coalition, the Critical Path Institute and the Alzheimer's Association addressed challenges in designing clinical trials to test multiple treatments in combination and outlined a roadmap for making such trials a reality.

Many challenges to clinical trial accrual exist, resulting in studies with inadequate enrollment and potentially delaying answers to important scientific and clinical questions.The National Cancer Institute (NCI) and the American Society of Clinical Oncology (ASCO) cosponsored the Cancer Trial Accrual Symposium: Science and Solutions on April 29-30, 2010 to examine the state of accrual science related to patient/community, physician/provider, and site/organizational influences, and identify new interventions to facilitate clinical trial enrollment. The symposium featured breakout sessions, plenary sessions, and a poster session including 100 abstracts. Among the 358 attendees were clinical investigators, researchers of accrual strategies, research administrators, nurses, research coordinators, patient advocates, and educators. A bibliography of the accrual literature in these three major areas was provided to participants in advance of the meeting. After the symposium, the literature in these areas was revisited to determine if the symposium recommendations remained relevant within the context of the current literature.Few rigorously conducted studies have tested interventions to address challenges to clinical trials accrual. Attendees developed recommendations for improving accrual and identified priority areas for future accrual research at the patient/community, physician/provider, and site/organizational levels. Current literature continues to support the symposium recommendations.A combination of approaches addressing both the multifactorial nature of accrual challenges and the characteristics of the target population may be needed to improve accrual to cancer clinical trials. Recommendations for best practices and for future research developed from the symposium are provided.

Assessing the value of clinical and translational research funding on accelerating the translation of scientific knowledge is a fundamental issue faced by the National Institutes of Health (NIH) and its Clinical and Translational Awards (CTSAs). To address this issue, the authors propose a model for measuring the return on investment (ROI) of one key CTSA program, the clinical research unit (CRU). By estimating the economic and social inputs and outputs of this program, this model produces multiple levels of ROI: investigator, program, and institutional estimates. A methodology, or evaluation protocol, is proposed to assess the value of this CTSA function, with specific objectives, methods, descriptions of the data to be collected, and how data are to be filtered, analyzed, and evaluated. This article provides an approach CTSAs could use to assess the economic and social returns on NIH and institutional investments in these critical activities.

PurposeThe authors assessed the prevalence and associated economic impact of low-enrolling clinical studies at a single academic medical center.MethodThe authors examined all clinical studies receiving institutional review board (IRB) review between FY2006 and FY2009 at Oregon Health & Scien

Objectives Improvements in electronic health record (EHR) system development will require an understanding of psychiatric clinicians' views on EHR system acceptability, including effects on psychotherapy communications, data-recording behaviors, data accessibility versus security and privacy, data quality and clarity, communications with medical colleagues, and stigma. Design Multidisciplinary development of a survey instrument targeting psychiatric clinicians who recently switched to EHR system use, focus group testing, data analysis, and data reliability testing. Measurements Survey of 120 university-based, outpatient mental health clinicians, with 56 (47%) responding, conducted 18 months after transition from a paper to an EHR system. Results Factor analysis gave nine item groupings that overlapped strongly with five a priori domains. Respondents both praised and criticized the EHR system. A strong majority (81%) felt that open therapeutic communications were preserved. Regarding data quality, content, and privacy, clinicians (63%) were less willing to record highly confidential information and disagreed (83%) with including their own psychiatric records among routinely accessed EHR systems. Limitations single time point; single academic medical center clinic setting; modest sample size; lack of prior instrument validation; survey conducted in 2005. Conclusions In an academic medical center clinic, the presence of electronic records was not seen as a dramatic impediment to therapeutic communications. Concerns regarding privacy and data security were significant, and may contribute to reluctances to adopt electronic records in other settings. Further study of clinicians' views and use patterns may be helpful in guiding development and deployment of electronic records systems.

This paper investigates the dynamics of a complex adaptive supply network (CASN), focusing on understanding stability of the structural evolution of a supply network and supplier population emergence. Supply network evolution data collected from simulated responses of the U.S. automobile industry are used in multivariate statistics and time series analysis to identify patterns of network evolution. This analysis reveals that the type of environment a supply network evolves in appears to be a major factor in determining critical timing of structural changes during the evolution of a CASN. Further, time series analysis of firm population evolution highlights how supply networks evolve due to path dependencies in the CASN system. Information about these two aspects of supply network evolution can prove useful to a decision maker in determining how to respond to supply network changes.

While ISO 9000 has been shown to improve internal metrics of firm performance, external measurements may be unaffected. This paper examines the economic value of successive generations of the ISO 9000 standard by assessing the equity returns of 204 firms certified between 1999 and 2002. This study also examines the economic effects of ISO 9001:2000 versus the superseded 1994 standards. The complete sample experienced no significant changes in stock price. The market reaction to ISO 9001:2000 certification is significantly more positive than the reaction to ISO 9000:1994 registration.

Supply chains, or supply networks (SNs), exist in a multitude of different topologies, yet little is known concerning how such topologies grow, evolve, and adapt over time. To study this complex phenomenon, we begin by identifying some primary topological structures that SNs may form. Then, to investigate the evolution of such structures, a theory-based framework is developed that combines aspects of complex adaptive systems theory, industrial growth theory, network theory, market structure, and game theory. This framework specifies categories of rules that may evoke different behaviors in the two fundamental components of any adaptive SN, i.e., the environment and the Arms in that environment. The framework is implemented as a multiparadigm simulation utilizing software agents and it joins discrete-time with discrete-event simulation formalisms. This methodology allows the spontaneous generation of network structures so that it is possible to examine the potential factors behind the evolution of different SN topologies. Using data and parameters extracted from 80 years of the U.S. automobile industry, we have been able to "grow" a wide range of SN topologies and preliminary results show that certain environmental and firm-level factors may impact the eventual evolution of such structures.

Due to a lack of valid and reliable scales, few studies have sought to describe and measure the business incubation process. After rigorously developing and pre-testing scales intended to measure the incubation process (Study A), we collected data from 53 incubators operating in the US in order to (a) systematically examine the incubation process, and (b) validate the scales. Accordingly, this study offers (1) new, validated scales for measuring the process of incubating new ventures, (2) empirically-based refinements to a theoretical model of the incubation process, and (3) data on business incubation outcomes that are very useful for incubator planning and benchmarking purposes.

Measuring the efficiency of resource allocation for the conduct of scientific projects in medical research is difficult due to, among other factors, the heterogeneity of resources supplied (e.g., dollars or FTEs) and outcomes expected (e.g., grants, publications). While this is an issue in medical science, it has been approached successfully in other fields by using data envelopment analysis (DEA). DEA has a number of advantages over other techniques as it simultaneously uses multiple heterogeneous inputs and outputs to determine which projects are performing most efficiently, referred to as being at the efficiency frontier, when compared to others in the data set.This research uses DEA for the evaluation of supported translational science projects by the Oregon Clinical and Translational Research Institute (OCTRI), a NCATS ClinicalTranslational Science Award (CTSA) recipient.These results suggest that the primary determinate of overall project efficiency at OCTRI is the amount of funding, with smaller amounts of funding providing more efficiency than larger funding amounts.These results, and the use of DEA, highlight both the success of using this technique in helping determine medical research efficiency and those factors to consider when distributing funds for new projects at CTSAs.

It is acknowledged that progress in combined therapeutic approaches for Alzheimer’s disease (AD) will require an unprecedented level of collaboration. At a meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer’s Disease Coalition and the Critical Path Institute, investigators from industry, academia and regulatory agencies agreed on the need for combinatorial approaches to treating AD. The need for advancing multiple targets includes recognition for novel adaptive trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination. A combination trial now being planned may test drugs targeting different pathogenic pathways or multiple targets along a common pathway. Collaborations and consortia-based strategies are pivotal for success and a regulatory framework is recommended for success.

Our research examines factors used by public service personnel and their contractors to reach the decision to terminate a project. Two decision-making roles are studied: executives, those with the authority to start or cancel a project, and project managers, those who direct the day-to-day operations of the project. Our exploratory survey research, an initial step in this topic, suggests that different roles cluster critical termination factors uniquely and use different decision weighting levels on these factors. On a second construct, information gathering actions, such as scanning and interpretation, show an indication of bias, particularly sunk cost bias, and this bias is experienced differently by executives and project managers. Interestingly, the scale of a project, in terms of labor-hours, calendar time, or budget, does not appear to be related to perception of ‘failure’. The implications for the management of government safety projects are that care must be taken to understand the differing viewpoints of the two types of decision-makers and that the initial presentation of information concerning a project may influence the perception of a project's outcome. While there are several implications for operations management research, the most critical is that care must be given when surveying “managers” concerning subjective measures of ‘success’ or ‘failure’ as there can be radical perceptual differences by role.

Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the organic and mechanistic management control structures. The structural impacts on cost and fill rate performance are studied in two-echelon and two-supply-chain network organization models under different market coordination conditions using system dynamic simulations. Our results show significant effects of demand and network structural factors, and their interactions, on these measures. As demand becomes dynamic, the cooperative interaction model, where supply chains cooperate to satisfy customer demand, is found to have better system performance than the competitive supply chain model. The analysis also suggests that increasing the responsiveness at the downstream plant is particularly important to the overall system performance improvement.

This article systematically reviews the literature on business incubators and business incubation. Focusing on the primary research orientations—i.e. studies centering on incubator development, incubator configurations, incubatee development, incubator-incubation impacts, and theorizing about incubators-incubation—problems with extant research are analyzed and opportunities for future research are identified. From our review, it is clear that research has just begun to scratch the surface of the incubator-incubation phenomenon. While much attention has been devoted to the description of incubator facilities, less attention has been focused on the incubatees, the innovations they seek to diffuse, and the incubation outcomes that have been achieved. As interest in the incubator-incubation concept continues to grow, new research efforts should focus not only on these under-researched units of analysis, but also on the incubation process itself.

This article employs real options-theoretic reasoning to develop a theory of business incubation. This theory seeks to predict and explain how business incubators and the process of business incubation increase the likelihood that new ventures will survive the early stages of development. It conceptualizes the incubator as an entrepreneurial firm that sources and macro-manages the innovation process within emerging organizations, infusing these organizations with resources at various developmental stage-gates while containing the cost of their potential failure. The incubator is the unit of analysis while incubation outcomes—measured in terms of incubatee growth and financial performance at the time of incubator exit—provide indicators of success. Our model of the incubation process and specification of the range of possible incubation outcomes offer implications for managerial practice and policy-making vis-a-vis incubator management and good entrepreneurial failure.

Assessing impact of poor accrual on premature trial closure requires a relevant metric. We propose defining accrual sufficiency on apparent ability to address primary endpoints (PE) rather than attaining accrual targets.All phase III trials open January 1, 1993, to December 31, 2002, by five U.S. oncology Clinical Trials Cooperative Groups (CTCG) were evaluated for accrual sufficiency and scientific results. Sufficient accrual included meeting accrual target, CTCGs documentation attesting adequate accrual, or conclusive results at interim analysis; insufficient accrual included poor accrual as cited closure reason or other reasons rendering a trial unable to address its primary endpoints. Closure rates based on our accrual sufficiency definition are compared with rates of meeting accrual targets and addressing the primary endpoints. A percentage of target accrual above which trials commonly answer the intended scientific question was identified to serve as an alternative to meeting full target accrual in designating accrual success.Of 238 eligible trials, 158 (66%) closed with sufficient accrual. Among 80 trials with insufficient accrual, 70 (29%) closed specifically because of poor accrual. Inadequate accrual rates are overemphasized when defining accrual success solely by meeting accrual targets. Nearly 75% of trials conclusively addressed the primary endpoints with positive results in 39% of trials. Exceeding 80% of target accrual serves as a reliable proxy for answering the intended scientific question.Approximately one third of phase III trials closed with insufficient accrual to address the primary endpoints, primarily due to poor accrual. Defining accrual sufficiency broader than meeting accrual targets represents a fairer account of trial closures.

A number of models have been proposed to predict optimal setup times, or optimal investment in setup reduction, in manufacturing cells. These have been based on the economic order quantity (EOQ) or economic production quantity (EPQ) model formulation, and have a common limitation in that they neglect work-in-process (WIP) inventories, which can be substantial in manufacturing systems. In this paper a new model is developed that predicts optimal production batch sizes and investments in setup reduction. This model is based on queuing theory, which permits it to estimate WIP levels as a function of the decisions variables, batch size and setup time. Optimal values for batch size and setup time are found analytically, even though the total cost model was shown to be strictly non-convex.

Reply to the “Comment: Using Product Profiling to Illustrate Manufacturing-Marketing Misalignment” by Richard J. Schonberger (Interfaces vol. 29, no. 6, November–December 1999, pp. 127–129) referring to the Interfaces July–August 1998 article “Using product profiling to illustrate manufacturing-marketing misalignment” by Hill, Menda, and Dilts, where they described Rumack Pharmaceutical Company (disguised name) as an example of misalignment between marketing and manufacturing.

Ordinal data is prevalent in medical outcomes research; for example, gender, living condition, and use of assistive devices are all both critical explanatory factors in determining the efficacy of medical procedures and are nominal or ordinal data. One basic objective of this study was to compare four assignment (ordinal analysis techniques') prediction of expected patient resource requirements in a specialty ambulatory (outpatient) health care setting. Data from 2427 patient discharges from 7 specialty low vision clinics were collected. Biographical and discharge characteristics of patients were used to develop homogeneous patient groups on the basis of resource use. Resource-use features were then stripped from the data. The four ordinal analysis techniques were subsequently applied to the reduced data set to predict iso-resource group membership for each patient in the data. Chance criterion was used as a benchmark in gauging the predictive ability of each ordinal analysis technique. Prediction results obtained were clinic-specific. This may largely be explained by the fact that the initial iso-resource groups were unique to each clinic and no meaningful iso-resource groups could be obtained from combined data across clinics. No technique was found to be universally superior at all clinics, however, each technique's performance across clinics was consistently better than the benchmark. Contrary to initial expectations, neural networks, in some cases, significantly underperformed the more traditional techniques.

As companies choose among process alternatives, they need a clear understanding of the changing alignment between manufacturing and the needs of their markets. Assessing how well existing processes fit these market requirements and making choices to meet future needs are critical strategic responsibilities for manufacturing. Product profiling can be used to examine the degree of alignment between the needs of a company's markets and the characteristics of its existing manufacturing process and infrastructure investments. We compare product profiling with another tool, the product-process matrix, and examine the applicability of both in a typical mismatch situation. In-depth analysis in one firm indicates that product profiling is a valuable tool to uncover the origins of misalignments that occur over time and to illustrate the phenomenon to executives.

While there has been considerable research on the content of manufacturing strategy, there is a paucity of literature concerning the process of manufacturing strategy formulation [Ward, P.T., Brickford, D.J., Leong, G.K., 1996. Configuration of manufacturing strategy, business strategy, environment, and structure, J. Manage., 22(4) 597–626; Leong, G.K., Snyder, D.L., Ward, P.T., 1990. Research in the process and content of manufacturing strategy, Omega, 18(2) 109–122]. Many researchers have highlighted the need to overcome this deficiency by studying the process of developing manufacturing strategy [Adam, E.E., Swamidass, P.M., 1989. Assessing operations management from a strategic perspective, J. Manage., 15(2) 181–203; Anderson et al., 1989; Leong, G.K., Snyder, D.L., Ward, P.T., 1990. Research in the process and content of manufacturing strategy, Omega, 18(2) 109–122]. To effectively link the manufacturing strategy of a firm to the needs of the marketplace, critical competitive factors or order-winning criteria must be understood and agreed upon both by operations and marketing managers [Hill, T.J., 1983. Manufacturing's strategic role, J. Operational Res. Soc., 34(9) 853–860; Hill, T.J., 1994. Manufacturing Strategy—Text and Cases, 2nd edn., Irwin, Homewood, IL]. For this study, we created and examined a process of establishing a set of order-winning criteria for a consumer pharmaceuticals firm which involved the participation of sixteen managers from seven functional areas over four months. The foundation of the process was developed by Hill [Hill, T.J., 1989. Manufacturing Strategy—Text and Cases. Irwin, Homewood, IL; Hill, T.J., 1994. Manufacturing Strategy—Text and Cases, 2nd edn., Irwin, Homewood, IL], however it was soon evident that additional steps were required. The expanded process we developed both exposed significantly differing views among the managers and raised several questions with important managerial and research implications.

Clinical trials are essential to cancer care, and data about the current state of research in oncology are needed to develop benchmarks and set the stage for improvement.To perform a comprehensive analysis of the national oncology clinical research portfolio.All interventional clinical studies registered on ClinicalTrials.gov between October 2007 and September 2010 were identified using Medical Subject Heading terms and submitted conditions. They were reviewed to validate classification, subcategorized by cancer type, and stratified by design characteristics to facilitate comparison across cancer types and with other specialties.Of 40 970 interventional studies registered between October 2007 and September 2010, a total of 8942 (21.8%) focused on oncology. Compared with other specialties, oncology trials were more likely to be single arm (62.3% vs 23.8%; P.001), open label (87.8% vs 47.3%; P.001), and nonrandomized (63.9% vs 22.7%; P.001). There was moderate but significant correlation between number of trials conducted by cancer type and associated incidence and mortality (Spearman rank correlation coefficient, 0.56 [P = .04] and 0.77 [P = .001], respectively). More than one-third of all oncology trials were conducted solely outside North America.There are significant variations between clinical trials in oncology and other diseases, as well as among trials within oncology. The differences must be better understood to improve both the impact of cancer research on clinical practice and the use of constrained resources.

Clinical research is burdened by inefficiencies and complexities, with a poor record of trial completion, none of which is desirable. The Clinical and Translational Science Award (CTSA) Consortium, including more than 60 clinical research institutions, supports a unified national effort to become, in effect, a virtual national laboratory designed to identify, implement, evaluate, and extend process improvements across all parts of clinical research, from conception to completion. If adequately supported by academic health centers, industry, and funding agencies, the Consortium could become a test bed for improvements that can dramatically reduce wasteful complexity, thus increasing the likelihood of clinical trial completion.

Preventing the disclosure, modification or destruction of information in a database has been the subject of considerable recent research (see, for example, [1-3]). While mandatory access control (MAC) assigns security clearance levels (e.g. top secret, secret) to all data for access control, discretionary access control (DAC) assigns privileges to users tailored to their responsibilities within an application. Both of these mechanisms have the fundamental limitation that they are unable to deal with the changing roles of a user (based on the occurrence of an event) within an application. As a result, user-role-based security (URBS) has been proposed [4, 5]. This paper demonstrates how URBS can be used to augment the existing security mechanisms. First the URBS concept, originally proposed for the object-oriented model, is extended to the relational model. Second, the extended model is augmented with the capability to respond to dynamic events. Finally, an integrated method is presented for the design of a dynamic, user-role-based security system.

Purpose: Present the design and initial evaluation of a unique, Web-enabled platform for the development of a community of practice around issues of oncology clinical trial accrual. Methods: The National Cancer Institute (NCI) conducted research with oncology professionals to identify unmet clinical trial accrual needs in thefield. In response, a comprehensive platform for accrual resources, AccrualNet, was created by using an agile development process, storyboarding, and user testing. Literature and resource searches identified relevant content to populate the site. Descriptive statistics were tracked for resource and site usage. Use cases were defined to support implementation. Results: AccrualNet has five levels: (1) clinical trial macrostages (prestudy, active study, and poststudy); (2) substages (developing a protocol, selecting a trial, preparing to open, enrolling patients, managing the trial, retaining participants, and lessons learned); (3) strategies for each substage; (4) multiple activities for each strategy; and (5) multiple resources for each activity. Since its launch, AccrualNet has had more than 45,000 page views, with the Tools & Resources, Conversations, and Training sections being the most viewed. Total resources have increased 69%, to 496 items. Analysis of articles in the site reveals that 22% are from two journals and 46% of the journals supplied a single article. To date, there are 29 conversations with 43 posts. Four use cases are discussed. Conclusion: AccrualNet represents a unique, centralized comprehensive-solution platform to systematically capture accrual knowledge for all stages of a clinical trial. It is designed to foster a community of practice by encouraging users to share additional strategies, resources, and ideas.

Purpose: The Institute of Medicine report on cooperative groups and the National Cancer Institute (NCI) report from the Operational Efficiency Working Group both recommend changes to the processes for opening a clinical trial. This article provides evidence for the need for such changes by completing the first comprehensive review of all the time and steps required to open a phase III oncology clinical trial and discusses the effect of time to protocol activation on subject accrual. Methods: The Dilts and Sandler method was used at four cancer centers, two cooperative groups, and the NCI Cancer Therapy Evaluation Program. Accrual data were also collected. Results: Opening a phase III cooperative group therapeutic trial requires 769 steps, 36 approvals, and a median of approximately 2.5 years from formal concept review to study opening. Time to activation at one group ranged from 435 to 1,604 days, and time to open at one cancer center ranged from 21 to 836 days. At centers, group trials are significantly more likely to have zero accruals (38.8%) than nongroup trials (20.6%; P < 0.0001). Of the closed NCI Cancer Therapy Evaluation Program–approved phase III clinical trials from 2000 to 2007, 39.1% resulted in Conclusions: The length, variability, and low accrual results demonstrate the need for the NCI clinical trials system to be reengineered. Improvements will be of only limited effectiveness if done in isolation; there is a need to return to the collaborative spirit with all parties creating an efficient and effective system. Recommendations put forth by the Institute of Medicine and Operational Efficiency Working Group reports, if implemented, will aid this renewal. Clin Cancer Res; 16(22); 5381–9. ©2010 AACR.

Cancer clinical trials in the US are at a major crossroads. Current government-sponsored research is some of the best in the world, but the field shares a worrying number of similarities with the American auto industry in its heyday. For clinical research to survive, the field must transform itself now to prevent a similar decline.

Purpose To examine the processes and document the calendar time required for the National Cancer Institute's Cancer Therapy Evaluation Program (CTEP) and Central Institutional Review Board (CIRB) to evaluate and approve phase III clinical trials. Methods Process steps were documented by (1) interviewing CTEP and CIRB staff regarding the steps required to activate a trial from initial concept submission to trial activation by a cooperative group, (2) reviewing standard operating procedures, and (3) inspecting trial records and documents for selected trials to identify any additional steps. Calendar time was collected from initial concept submission to activation using retrospective data from the CTEP Protocol and Information Office. Results At least 296 distinct processes are required for phase III trial activation: at least 239 working steps, 52 major decision points, 20 processing loops, and 11 stopping points. Of the 195 trials activated during the January 1, 2000, to December 31, 2007, study period, a sample of 167 (85.6%) was used for gathering timing data. Median calendar days from initial formal concept submission to CTEP to trial activation by a cooperative group was 602 days (interquartile range, 454 to 861 days). This time has not significantly changed over the past 8 years. There is a high variation in the time required to activate a clinical trial. Conclusion Because of their complexity, the overall development time for phase III clinical trials is lengthy, process laden, and highly variable. To streamline the process, a solution must be sought that includes all parties involved in developing trials.

This paper is addressed to an audience of managers and senior analysts involved with planning and controlling the knowledge engineering life cycle. A general framework for understanding the sociotechnical implications of expert systems is provided. It is argued that expert systems comprise two distinct but entwined subsystems. There is a technical component that concerns the task domain and the knowledge engineering process. There is a social dimension of how users and managers relate to the system and how the system fits with the organization. The authors believe that understanding the associations between these factors is essential in evaluating the efficiency and effectiveness of expert systems. >

Purpose: We examine the processes and document the calendar time required to activate phase II and III clinical trials by an oncology group: the Eastern Cooperative Oncology Group (ECOG). Methods: Setup steps were documented by (a) interviewing ECOG headquarters and statistical center staff, and committee chairs, (b) reviewing standard operating procedure manuals, and (c) inspecting study records, documents, and e-mails to identify additional steps. Calendar time was collected for each major process for each study in this set. Results: Twenty-eight phase III studies were activated by ECOG during the January 2000 to July 2006 study period. We examined a sample from 16 of those studies in detail. More than 481 distinct processes were required for study activation: 420 working steps, 61 major decision points, 26 processing loops, and 13 stopping points. Median calendar days to activate a trial in the phase III subset was 783 days (range, 285-1,542 days) from executive approval and 808 days (range, 435-1,604 days) from initial conception of the study. Data were collected for all phase II and phase III trials activated and completed during this time period (n = 52) for which development time represented 43.9% and 54.1% of the total trial time, respectively. Conclusion: The steps required to develop and activate a clinical trial may require as much or more time than the actual completion of a trial. The data shows that to improve the activation process, research should to be directed toward streamlining both internal and external groups and processes.

We thank Schoen for her comments and insights into accrual performance. The primary purpose of our research was to reveal the relationship between development time of National Cancer Institute Cancer Therapy Evaluation Program (NCI-CTEP)-supported trials and a standard accrual milestone that was

The objective of this research is to investigate the effects of setup-cost estimating methods on the lot sizing and scheduling of multiple products in multiple periods. These initial setup cost estimators (ISCEs) are used to estimate sequence-independent initial setup costs from sequence-dependent setup costs. A search of the literature reveals that, although sequence-dependent setup costs are frequently found in practice and ISCEs are frequently used, there is a dearth of research concerning the effect of using ISCEs. After a review of the literature, a mixed integer formulation of the joint problem of lot sizing and scheduling is presented, followed by a discussion of the difficulty in solving the formulation. Next, the six ISCEs evaluated are presented. These ISCEs range from simple (select the minimum setup cost) to complex (use the branch-and-bound solution to a traveling salesman-type problem). Each ISCE is evaluated using a full factorial design with five independent variables: demand distribution (three levels), demand trend (three levels), setup to inventory level (six levels), setup distribution (three levels), and setup variability (two levels). Two hypotheses are researched. Do the more computationally complex ISCEs produce lower overall costs than do the simpler ISCEs? Does the reduction in total cost justify the additional computation cost? The results of this study demonstrate that it may be incorrect to use “conventional wisdom'’when selecting an ISCE.

We examine a new algorithm developed by Kuzdrall and Britney [5] for locating the optimal order quantity in the presence of quantity discounts. Their algorithm, based on a model for the supplier's formulation of the price schedule, involves a regression analysis to identify the supplier's variable cost per unit and the fixed cost that the supplier seeks to recover, followed by an iterative search for the optimum. The authors describe this method as a “convenient alternative to the aimless searching of traditional approaches” [5, p. 101]. We examine the allegation of superiority of their total setup lot-sizing model over the classical method and dispute their claim of superiority.

A framework is presented based on the MRP Evolution‐Information System Evolution (MEISE) grid to classify MRP users in terms of the diagonal band along the dimension of information processing system development. The classification scheme provides a guideline for the information system specialist to make necessary adjustments of information systems when they decide to upgrade their MRP systems. Furthermore, the MEISE grid also facilitates the organisation of information and production functions while maintaining overall co‐ordination. Finally, the operational implications of deviating from the diagonal band are discussed.