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Exciting recent advancements in deep-sequencing technology have enabled a rapid and cost-effective molecular characterization of patient-derived tumor samples. Incorporating these innovative diagnostic technologies into early clinical trials could significantly propel implementation of precision medicine by identifying genetic markers predictive of sensitivity to agents. It may also markedly accelerate drug development and subsequent regulatory approval of novel agents. Particularly noteworthy, a high-response rate in a Phase II trial involving a biomarker-enriched patient cohort could result in a regulatory treatment approval in rare histologies, which otherwise would not be a candidate for a large randomized clinical trial. Furthermore, even if a trial does not meet its statistical endpoint, tumors from a few responders should be molecularly characterized as part of the new biomarker-mining processes. In order to accommodate patient screening and accelerate the accrual process, institutions conducting early clinical trials need to be a part of a multi-institution clinical trials network. Future clinical trial design will incorporate new biomarkers discovered by a 'phenotype-to-genotype' effort with an appropriate statistical design. To help advance such changes, the National Cancer Institute has recently reformed the existing early phase clinical trials network. A new clinical trial network, the Experimental Therapeutics Clinical Trials Network (ET-CTN), was begun and, in addition to its pre-existing infrastructure, an up-to-date clinical trial registration system, clinical trial monitoring system including electronic database and a central Institutional Review Board were formed. Ultimately, these reforms support identifying the most appropriate therapy for each tumor type by incorporating state-of-the-art molecular diagnostic tools into early clinical trials., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: The conduct of clinical trials should be an integral part of routine patient care. Treating patients in trials incurs additional costs over and above standard of care (SOC), but the extent of the cost burden is not known. We undertook a retrospective cost attribution analysis to quantitate the treatment costs associated with cancer clinical trial protocols conducted over a 2 year period., Methods: All patients entered into oncology (non-haematology) clinical trials involving investigational medicinal products in 2009 and 2010 in a single UK institution were identified. The trial protocols on which they were treated were analysed to identify the treatment costs for the experimental arm(s) of the trial and the equivalent SOC had the patient not been entered in the trial. The treatment cost difference was calculated by subtracting the experimental treatment cost from SOC cost. For randomised trials, an average treatment cost was estimated by taking into account the number of arms and randomisation ratio. An estimate of the annual treatment costs was calculated., Results: A total of 357 adult oncology patients were treated on 53 different trial protocols: 40 phase III, 2 randomised II/III and 11 phase II design. A total of 27 trials were academic, non-commercial sponsored trials and 26 were commercial sponsored trials. When compared with SOC, the average treatment cost per patient was an excess of £431 for a non-commercial trial (range £6393 excess to £6005 saving) and a saving of £9294 for a commercial trial (range £0 to £71,480). There was an overall treatment cost saving of £388,719 in 2009 and £496,556 in 2010, largely attributable to pharmaceutical company provision of free drug supplies., Conclusion: On an average, non-commercial trial protocols were associated with a small per patient excess treatment cost, whereas commercial trials were associated with a substantially higher cost saving. Taking into account the total number of patients recruited annually, treatment of patients on clinical trial protocols was associated with considerable cost savings across both the non-commercial and commercial portfolio.

Background: Randomized controlled trials with a survival endpoint are the gold standard for clinical research, but have failed to achieve cures for most advanced malignancies. The high costs of randomized clinical trials slow progress (thereby causing avoidable loss of life) and increase health care costs., Discussion: A malignancy may be caused by several different mutations. Therapies effective vs one mutation may be discarded due to lack of statistical significance across the entire population. Conversely, expensive large randomized trials may have sufficient statistical power to demonstrate benefit despite the therapy only working in subgroups. Non-cost-effective therapy is then applied to all patients (including subgroups it cannot help). Randomized trials comparing therapies with different mechanisms of action are misleading since they may conclude the therapies are "equivalent" despite benefitting different subpopulations, or may erroneously conclude that one therapy is superior simply because it targets a larger subpopulation. Furthermore, minor variances in patient selection may determine study outcome, a therapy may be discarded as ineffective despite substantial benefit in one subpopulation if harmful in another, randomized trials may more effectively detect therapies with minor benefit in most patients vs marked benefit in subpopulations, and randomized trials in unselected patients may erroneously conclude that "shot-gun" combinations are superior to single agents when sequential administration of personalized single agents might work better and spare patients treatment with drugs that cannot help them. We must identify predictive biomarkers early by comparing responding to progressing patients in phase I-II trials. Enriching randomized trials for biomarker-positive patients can markedly reduce required patient numbers and costs despite expensive screening for biomarker-positive patients. Available data support approval of new drugs without randomized trials if they yield single-agent sustained responses in patients refractory to standard therapies. Conversely, new approaches are needed to guide development of drug combinations since both standard phase II approaches and phase II-III randomized trials have a high risk of misleading., Summary: Traditional randomized clinical trials approaches are often inefficient, wasteful, and unreliable. New clinical research paradigms are needed. The primary outcome of clinical research should be "Who (if anyone) benefits?" rather than "Does the overall group benefit?"

Background: Many clinical trials show no overall benefit. We examined futility analyses applied to trials with different effect sizes., Methods: Ten randomised cancer trials were retrospectively analysed; target sample size reached in all. The hazard ratio indicated no overall benefit (n=5), or moderate (n=4) or large (n=1) treatment effects. Futility analyses were applied after 25, 50 and 75% of events were observed, or patients were recruited. Outcomes were conditional power (CP), and time and cost savings., Results: Futility analyses could stop some trials with no benefit, but not all. After observing 50% of the target number of events, 3 out of 5 trials with no benefit could be stopped early (low CP ≤ 15%). Trial duration for two studies could be reduced by 4-24 months, saving £44 000-231 000, but the third had already stopped recruiting, hence no savings were made. However, of concern was that 2 of the 4 trials with moderate treatment effects could be stopped early at some point, although they eventually showed worthwhile benefits., Conclusions: Careful application of futility can lead to future patients in a trial not being given an ineffective treatment, and should therefore be used more often. A secondary consideration is that it could shorten trial duration and reduce costs. However, studies with modest treatment effects could be inappropriately stopped early. Unless there is very good evidence for futility, it is often best to continue to the planned end., (© 2012 Cancer Research UK)

Pediatric oncology is an unrivaled success story in the recent history of medicine. This success is mostly based on a persistent refinement of evidence based therapeutic concepts. With that regard physicians and their staff are highly experience in the conduct of prospective evidence based trials and are therefore competent partners for the pharmaceutical industry. In times of personalized medicine the individual target population is diminishing and the borders of indications are not more disease based. A situation that requires new concepts from the industry. Therefore children with cancer could benefit early from the current developments as well as the pharmaceutical industry could benefit from the legislative incentives through highly recruiting and well conducted prospective trials. Pivotal is a functional platform of communication in order to maintain a close dialogue between academia and pharmaceutical companies., (© Georg Thieme Verlag KG Stuttgart · New York.)

During the early stage of cancer clinical trials, when it is not convenient to construct an explicit hypothesis testing, a study on a new therapy often calls for a response rate (p) estimation concurrently with or right before a typical phase II study. We consider a two-stage process, where the acquired information from Stage I (with a small sample size (m)) would be utilized for sample size (n) recommendation for Stage II study aiming for a more accurate estimation. Once a sample size design and a parameter estimation protocol are applied, we study the overall utility (cost-effectiveness) in connection with the cost due to patient recruitment and treatment as well as the loss due to mean squared error from parameter estimation. Two approaches will be investigated including the posterior mixture method (a Bayesian approach) and the empirical variance method (a frequentist approach). We also discuss response rate estimation under truncated parameter space using maximum likelihood estimation with regard to sample size and mean squared error. The profiles of p-specific expected sample size, mean squared error and risk under different approaches motivate us to introduce the concept of "admissible sample size (design)"., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The emergence of many newer, molecularly targeted anticancer drugs requires that we rethink the way that we conduct phase II trials in oncology. In particular, we can no longer afford to advance drugs (or combinations) to phase III trials with a high risk of failure to improve on outcomes. Drawing on phase II trials of therapeutics for other chronic and progressive diseases in medicine, we find that a randomized design is essential not only for selecting agents for further study but also for optimizing the design (dose, patient population, and endpoints) of the subsequent phase III trials. We use the example of advanced nonsmall cell lung cancer to demonstrate how randomized phase II trials have already made an impact in oncology, whereas single-arm phase II trials have led to negative phase III trials in the same disease. Finally, we make the case that randomized phase II trials are feasible, as long as reasonable statistical standards are applied.

Purpose: Prospectively planned collection and analysis of adverse event (AE) data are essential parts of well-conducted clinical trials. The AE data in a trial sponsor's database should be comparable with what is stipulated in the protocol and with the AE data published. We examined whether the published AE data differ from those in the sponsor's database and from the data collection requirements stated in study protocols., Methods: We searched the National Cancer Institute (NCI) Clinical Data Update System (CDUS) for studies that used the Common Toxicity Criteria version 2.0 and for which a final study publication was available. We extracted from the protocols information pertaining to AE collection and reporting methods and compared it with the methods cited in the article. We also compared the AE data in the trial publication with the AE data submitted by the investigators to CDUS., Results: We identified 22 studies meeting the criteria for this review. There was considerable inconsistency between AE collection and reporting methods cited in the protocols versus final publications. AE data in the article and CDUS were not identical. Twenty-seven percent of article high-grade AEs could not be matched to agent-attributable AEs in the CDUS. Twenty-eight percent of CDUS high-grade AEs could not be matched to AEs in the corresponding article. In 14 of 22 articles, the number of high-grade AEs in CDUS differed from the number in the articles by 20% or more., Conclusion: Lack of consistency in and reporting of AEs are associated with NCI database and trial publication AE data discrepancy.

Purpose: Concern that clinical trials may be too costly has been used to justify traditionally restrictive insurer policies regarding clinical trials. Additionally, fear of insurer reimbursement denial can be a significant barrier to clinical trial participation. In this study, we reviewed the empirical data on costs of clinical trials versus standard care and summarized the current status of policy initiatives related to clinical trial insurance reimbursement., Methods: Electronic and print data sources were searched for studies on the costs of oncology clinical trials. Information on policy initiatives for clinical trial reimbursement was obtained from the American Society of Clinical Oncology, the American Society of Hematology, and the Coalition of National Cancer Cooperative Groups and from searches of World Wide Web sites., Results: Five pilot studies provided information for 377 patients on phase II/III clinical trials matched with controls on standard care. Cost estimates ranged from 10% lower to 23% higher costs/charges for clinical trials in comparison to standard medical care. Medicare, 14 states, and several private insurers now cover the costs of patient care in "qualifying" clinical trials., Conclusion: Findings from small pilot studies suggest that phase II and III clinical trials result in at most modest increases in cost over standard treatment costs. Also, an increasing number of policy makers have decided to support clinical trial reimbursement initiatives. It is hoped that economic data from large observational studies will facilitate widespread and permanent decisions that support reimbursement for phase I, II, and III clinical trial participation.

Purpose: To summarize evidence on the costs of treating patients in clinical trials and to describe the Cost of Cancer Treatment Study, an ongoing effort to produce generalizable estimates of the incremental costs of government-sponsored cancer trials., Methods: A retrospective study of costs will be conducted with 1,500 cancer patients recruited from a randomly selected sample of institutions in the United States. Patients accrued to either phase II or phase III National Cancer Institute-sponsored clinical trials during a 15-month period will be asked to participate in a study of their health care utilization (n = 750). Costs will be measured approximately 1 year after their trial enrollment from a combination of billing records, medical records, and an in-person survey questionnaire. Similar data will be collected for a comparable group of cancer patients not in trials (n = 750) to provide an estimate of the incremental cost., Results: Evidence suggests insurers limit access to trials because of cost concerns. Public and private efforts are underway to change these policies, but their permanent status is unclear. Previous studies found that treatment costs in clinical trials are similar to costs of standard therapy. However, it is difficult to generalize from these studies because of the unique practice settings, insufficient sample sizes, and the exclusion of potentially important costs., Conclusion: Denials of coverage for treatment in a clinical trial limit patient access to trials and could impede clinical research. Preliminary estimates suggest changes to these policies would not be expensive, but these results are not generalizable. The Cost of Cancer Treatment Study is an ongoing effort to provide generalizable estimates of the incremental treatment cost of phase II and phase III cancer trials. The results should be of great interest to insurers and the research community as they consider permanent ways to finance cancer trials.

Background: Payment for care provided as part of clinical research has become less predictable as a result of managed care. Because little is known at present about how entry into cancer trials affects the cost of care for cancer patients, we conducted a matched case-control comparison of the incremental medical costs attributable to participation in cancer treatment trials., Methods: Case patients were residents of Olmsted County, MN, who entered phase II or phase III cancer treatment trials at the Mayo Clinic from 1988 through 1994. Control patients were patients who did not enter trials but who were eligible on the basis of tumor registry matching and medical record review. Sixty-one matched pairs were followed for up to 5 years after the date of trial entry for case patients or from an equivalent date for control patients. Hospital, physician, and ancillary service costs were estimated from a population-based cost database developed at the Mayo Clinic., Results: Trial enrollees incurred modestly (no more than 10%) higher costs over various follow-up periods. The mean cumulative 5-year cost in 1995 inflation-adjusted U.S. dollars among trial enrollees after adjustment for censoring was $46424 compared with $44 133 for control patients. After 1 year, trial enrollee costs were $24645 compared with $23 964 for control patients., Conclusions: This study suggests that cancer chemotherapy trials may not imply budget-breaking costs. Cancer itself is a high-cost illness. Clinical protocols may add relatively little to that cost.