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Basket, umbrella, and platform trial designs (master protocols) have emerged over the last decade to study precision medicine approaches in oncology. First-generation trials like NCI-MATCH (Molecular Analysis for Therapy Choice) have proven the principle that studying targeted therapies on a large scale is feasible both from the laboratory and clinical perspectives. However, single-agent targeted therapies have shown limited ability to control metastatic disease, despite careful matching of drug to target. As such, newer approaches employing combinations of targeted therapy, or targeted therapy with standard therapies, need to be considered. The NCI has recently embarked on three second-generation precision medicine trials to address this need: ComboMATCH, iMATCH, and myeloMATCH. The design of these trials and necessary infrastructure are discussed in the following perspective., (©2023 The Authors; Published by the American Association for Cancer Research.)

Importance: The Patient Protection and Affordable Care Act (ACA) Medicaid expansion resulted in increased use of Medicaid insurance nationwide. However, the association between Medicaid expansion and access to clinical trials has not been examined to date., Objective: To examine whether the implementation of ACA Medicaid expansion was associated with increased participation of patients with Medicaid insurance in cancer clinical trials., Design, Setting, and Participants: Data for this cohort study of 51 751 patients were from the SWOG Cancer Research Network. All patients aged 18 to 64 years and enrolled in treatment trials with Medicaid or private insurance between April 1, 1992, and February 29, 2020, were included. Interrupted time-series analysis with segmented logistic regression was used. The monthly unemployment rate and presidential administration were adjusted to reflect potential differences in Medicaid use associated with economic conditions and national administrative policies, respectively. Data analysis was conducted between June 22, 2021, and August 5, 2022., Exposure: Implementation of Medicaid expansion on January 1, 2014, was the independent exposure variable., Main Outcomes and Measures: The number and proportion of patients by insurance type enrolled in cancer clinical trials over time were analyzed., Results: Overall, data for 51 751 patients were analyzed. Mean (SD) age was 50.6 (9.8) years, 67.3% of patients were female, 41.1% were younger than 50 years, and 9.1% used Medicaid. A 19% annual increase (odds ratio [OR], 1.19; 95% CI, 1.11-1.28; P < .001) was identified in the odds of patients using Medicaid after the ACA Medicaid expansion, resulting in a 52% increase (OR, 1.52; 95% CI, 1.29-1.78; P < .001) compared with what was expected in the number of Medicaid patients enrolled over time. The association was greater in states that adopted Medicaid expansion in 2014 to 2015 (OR, 1.26; 95% CI, 1.15-1.38; P < .001) compared with other states (OR, 1.08; 95% CI, 0.96-1.21; P = .20; P = .04 for interaction). By February 2020, the proportion of patients with Medicaid insurance was 17.8% (95% CI, 15.0%-20.8%; P < .001), whereas the expected proportion had ACA Medicaid expansion not occurred was 6.9% (95% CI, 4.4%-10.3%; P < .001)., Conclusions and Relevance: Findings suggest that implementation of ACA Medicaid expansion was associated with increased participation of patients using Medicaid in cancer clinical trials. Improved participation in clinical trials for Medicaid-insured patients is critical for socioeconomically vulnerable patients seeking access to the newest treatments available in trials and for improving confidence that trial findings apply to patients of all backgrounds.

Background: Screening for viral infection in cancer patients is inconsistent. A mechanism to readily identify cancer patients at increased risk of existing or prior viral infection could enhance screening efforts while reducing costs., Methods: We identified factors associated with increased risk of past or chronic hepatitis virus B, hepatitis virus C, or HIV infection before initiation of systemic cancer therapy. Data were from a multicenter prospective cohort study of 3051 patients with newly diagnosed cancer (SWOG-S1204) enrolled between 2013 and 2017. Patients completed a survey with questions pertaining to personal history and behavioral, socioeconomic, and demographic risk factors for viral hepatitis or HIV. We derived a risk model to predict the presence of viral infection in a random set of 60% of participants using best subset selection. The derived model was validated in the remaining 40% of participants. Logistic regression was used., Results: A model with 7 risk factors was identified, and a risk score with 4 levels was constructed. In the validation cohort, each increase in risk level was associated with a nearly threefold increased risk of viral positivity (odds ratio = 2.85, 95% confidence interval = 2.26 to 3.60, P < .001). Consistent findings were observed for individual viruses. Participants in the highest risk group (with >3 risk factors), comprised of 13.4% of participants, were 18 times more likely to be viral positive compared with participants with no risk factors (odds ratio = 18.18, 95% confidence interval = 8.00 to 41.3, P < .001)., Conclusions: A risk-stratified screening approach using a limited set of questions could serve as an effective strategy to streamline screening for individuals at increased risk of viral infection., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: In the United States, the National Cancer Institute National Cancer Clinical Trials Network (NCTN) groups have conducted publicly funded oncology research for 50 years. The combined impact of all adult network group trials has never been systematically examined., Methods: We identified randomized, phase III trials from the adult NCTN groups, reported from 1980 onward, with statistically significant findings for ≥ 1 clinical, time-dependent outcomes. In the subset of trials in which the experimental arm improved overall survival, gains in population life-years were estimated by deriving trial-specific hazard functions and hazard ratios to estimate the experimental treatment benefit and then mapping this trial-level benefit onto the US cancer population using registry and life-table data. Scientific impact was based on citation data from Google Scholar. Federal investment costs per life-year gained were estimated. The results were derived through December 31, 2020., Results: One hundred sixty-two trials comprised of 108,334 patients were analyzed, representing 29.8% (162/544) of trials conducted. The most common cancers included breast (34), gynecologic (28), and lung (14). The trials were cited 165,336 times (mean, 62.2 citations/trial/year); 87.7% of trials were cited in cancer care guidelines in favor of the recommended treatment. These studies were estimated to have generated 14.2 million (95% CI, 11.5 to 16.5 million) additional life-years to patients with cancer, with projected gains of 24.1 million (95% CI, 19.7 to 28.2 million) life-years by 2030. The federal investment cost per life-year gained through 2020 was $326 in US dollars., Conclusion: NCTN randomized trials have been widely cited and are routinely included in clinical guidelines. Moreover, their conduct has predicted substantial improvements in overall survival in the United States for patients with oncologic disease, suggesting they have contributed meaningfully to this nation's health. These findings demonstrate the critical role of government-sponsored research in extending the lives of patients with cancer.

Purpose: Women have more adverse events (AEs) from chemotherapy than men, but few studies have investigated sex differences in immune or targeted therapies. We examined AEs by sex across different treatment domains., Methods: We analyzed treatment-related AEs by sex in SWOG phase II and III clinical trials conducted between 1980 and 2019, excluding sex-specific cancers. AE codes and grade were categorized using the Common Terminology Criteria for Adverse Events. Symptomatic AEs were defined as those aligned with the National Cancer Institute's Patient-Reported Outcome-Common Terminology Criteria for Adverse Events; laboratory-based or observable/measurable AEs were designated as objective (hematologic v nonhematologic). Multivariable logistic regression was used, adjusting for age, race, and disease prognosis. Thirteen symptomatic and 14 objective AE categories were examined., Results: In total, N = 23,296 patients (women, 8,838 [37.9%]; men, 14,458 [62.1%]) from 202 trials experiencing 274,688 AEs were analyzed; 17,417 received chemotherapy, 2,319 received immunotherapy, and 3,560 received targeted therapy. Overall, 64.6% (n = 15,051) experienced one or more severe (grade ≥ 3) AEs. Women had a 34% increased risk of severe AEs compared with men (odds ratio [OR] = 1.34; 95% CI, 1.27 to 1.42; P < .001), including a 49% increased risk among those receiving immunotherapy (OR = 1.49; 95% CI, 1.24 to 1.78; P < .001). Women experienced an increased risk of severe symptomatic AEs among all treatments, especially immunotherapy (OR = 1.66; 95% CI, 1.37 to 2.01; P < .001). Women receiving chemotherapy or immunotherapy experienced increased severe hematologic AE. No statistically significant sex differences in risk of nonhematologic AEs were found., Conclusion: The greater severity of both symptomatic AEs and hematologic AEs in women across multiple treatment modalities indicates that broad-based sex differences exist. This could be due to differences in AE reported, pharmacogenomics of drug metabolism/disposition, total dose received, and/or adherence to therapy. Particularly large sex differences were observed for patients receiving immunotherapy, suggesting that studying AEs from these agents is a priority., Competing Interests: Kathy S. AlbainHonoraria: Encore Medical EducationResearch Funding: Seattle Genetics (Inst), Quantum Leap Healthcare Collaborative (Inst)Other Relationship: Seattle Genetics Michael LeBlancConsulting or Advisory Role: Agios Carolyn C. GotayStock and Other Ownership Interests: Johnson & Johnson/Janssen N. Lynn HenryResearch Funding: Blue Note Therapeutics (Inst)Open Payments Link: https://openpaymentsdata.cms.gov/physician/27894/summary Michael J. FischEmployment: AIM Specialty HealthStock and Other Ownership Interests: AnthemPatents, Royalties, Other Intellectual Property: Healthcore, Inc, A subsidiary of Anthem, IncOpen Payments Link: https://openpaymentsdata.cms.gov/physician/767578 Shing M. LeeConsulting or Advisory Role: PTC TherapeuticsResearch Funding: Merck, Genentech/Roche Dawn L. HershmanConsulting or Advisory Role: AIM Specialty HealthNo other potential conflicts of interest were reported.

Importance: During the initial outbreak of the COVID-19 pandemic, cancer clinical trial participation decreased precipitously. Given the continued pandemic-especially the severe wave of new cases and deaths in winter 2020 to 2021-a vital question is whether trial enrollments have remained low or even worsened., Objective: To examine the experience of cancer clinical trial enrollment 1 year after the COVID-19 outbreak., Design, Setting, and Participants: This cohort study examines initial enrollments to treatment trials and cancer control and prevention (CCP) trials conducted by the SWOG Cancer Research Network between January 1, 2016, and February 28, 2021. Participants include patients enrolled in the trials., Exposures: Landmark time points reflecting the onset and the apex, respectively, of the initial COVID-19 wave (March 1 to April 25, 2020) and the winter 2020 to 2021 wave (October 4, 2020, to January 23, 2021)., Main Outcomes and Measures: This study used interrupted time-series analysis to examine enrollments over time related to the COVID-19-derived exposure variables using negative-binomial regression. Relative risk (RR) estimates representing weekly enrollment changes compared with expected rates (had the pandemic not occurred) were derived. The numbers of enrollments lost during the pandemic were estimated., Results: Overall, 29 398 patients (mean [SD] age, 60.3 [13.2] years) were enrolled (24 034 before the pandemic and 5364 during the pandemic), with 9198 patients (31.3%) aged 65 years or older, 17 199 female patients (58.6%), 3039 Black patients (10.8%), and 2260 Hispanic patients (7.9%). Most enrollments (19 451 [66.2%]) were to treatment trials. During the initial COVID-19 wave, there was a 9.0% model-estimated weekly reduction in enrollments (RR, 0.91; 95% CI, 0.89-0.93; P < .001), with effects compounding each week. Enrollment recovered thereafter, but decreased again during the winter 2020 to 2021 wave, although by only 2.0% each week (RR, 0.98; 95% CI, 0.97-0.99; P < .001). Overall, during the pandemic, actual enrollments were 77.3% of expected enrollments (5364 of 6913 enrollments; 95% CI, 70.5%-85.0%; P < .001). Actual enrollments were 54.0% of expected enrollments for CCP trials (1421 of 2641 enrollments; 95% CI, 43.0%-67.0%; P < .001) and 91.0% of expected enrollments for treatment trials (3922 of 4304 enrollments; 95% CI, 81.0%-102.0%; P = .12)., Conclusions and Relevance: In this cohort study, clinical trial enrollments decreased during the full year of the COVID-19 pandemic. Enrollment reductions were primarily to CCP trials, whereas, remarkably, there was not strong evidence of enrollment reductions to treatment trials. This finding suggests that clinical research rapidly adapted to the circumstances of enrolling and treating patients on protocols during the COVID-19 pandemic.

Importance: Few new treatments tested in phase 3 cancer randomized clinical trials show an overall survival benefit. Although understanding whether the benefits are consistent among all patient groups is critical for informing guideline care, individual trials are designed to assess the benefits of experimental treatments among all patients and are too small to reliably determine whether treatment benefits apply to demographic or insurance subgroups., Objective: To systematically examine whether positive treatment effects in cancer randomized clinical trials apply to specific demographic or insurance subgroups., Design, Setting, and Participants: Cohort study of pooled patient-level data from 10 804 patients in SWOG Cancer Research Network clinical treatment trials reported from 1985 onward with superior overall survival for those receiving experimental treatment. Patients were enrolled from 1984 to 2012. Maximum follow-up was 5 years., Main Outcomes and Measures: Interaction tests were used to assess whether hazard ratios (HRs) for death comparing standard group vs experimental group treatments were associated with age (≥65 vs <65 years), race/ethnicity (minority vs nonminority populations), sex, or insurance status among patients younger than 65 years (Medicaid or no insurance vs private insurance) in multivariable Cox regression frailty models. Progression- or relapse-free survival was also examined. Data analyses were conducted from August 2019 to February 2020., Results: In total, 19 trials including 10 804 patients were identified that reported superior overall survival for patients randomized to experimental treatment. Patients were predominantly younger than 65 years (67.3%) and female (66.3%); 11.4% were black patients, and 5.7% were Hispanic patients. There was evidence of added survival benefits associated with receipt of experimental therapy for all groups except for patients with Medicaid or no insurance (HR, 1.23; 95% CI, 0.97-1.56; P = .09) compared with those with private insurance (HR, 1.66; 95% CI, 1.44-1.92; P < .001; P = .03 for interaction). Receipt of experimental treatment was associated with reduced added overall survival benefits in patients 65 years or older (HR, 1.21; 95% CI, 1.11-1.32; P < .001) compared with patients younger than 65 years (HR, 1.41; 95% CI, 1.30-1.53; P < .001; P = .01 for interaction), although both older and younger patients appeared to strongly benefit from receipt of experimental treatment. The progression- or relapse-free survival HRs did not differ by age, sex, or race/ethnicity but differed between patients with Medicaid or no insurance (HR, 1.32; 95% CI, 1.06-1.64; P = .01) vs private insurance (HR, 1.74; 95% CI, 1.54-1.97; P < .001; P = .03 for interaction)., Conclusions and Relevance: Patients with Medicaid or no insurance may have smaller added benefits from experimental therapies compared with standard treatments in clinical trials. A better understanding of the quality of survivorship care that patients with suboptimal insurance receive, including supportive care and posttreatment care, could help establish how external factors may affect outcomes for these patients.

The hazard ratio is widely used to measure or to summarize the magnitude of treatment effects, but it is justifiably difficult to interpret in a meaningful way to patients and perhaps for clinicians as well. In addition, it is most meaningful when the hazard functions are approximately proportional over time. We propose a new measure, termed personalized chance of longer survival. The measure, which quantifies the probability of living longer with one treatment over the another, accounts for individualized characteristics to directly address personalized treatment effects. Hence, the measure is patient focused, which can be used to evaluate subgroups easily. We believe it is intuitive to understand and clinically interpretable in the presence of nonproportionality. Furthermore, because it estimates the probability of living longer by some fixed amount of time, it encodes the probabilistic part of treatment effect estimation. We provide nonparametric estimation and inference procedures that can accommodate censored survival outcomes. We conduct extensive simulation studies, which characterize performance of the proposed method, and data from a large randomized Phase III clinical trial (SWOG S0819) are analyzed using the proposed method., (© 2019 John Wiley & Sons, Ltd.)

Importance: Recently, tremendous prominence has been given to the investigation of the effect of different research processes as part of the Cancer Moonshot. More than half a century ago, the National Cancer Institute (NCI) established a network of publicly funded cancer cooperative research groups to systematically evaluate new treatments for efficacy and safety., Objective: To examine the extent to which positive NCI-sponsored cancer treatment trials have benefited patients with cancer in the US population., Design, Setting, and Participants: This investigation used study data from SWOG, an NCI-sponsored network cooperative research group. All treatment trials during SWOG's 60-year history (1956-2016) were identified for which the new, experimental therapy provided a statistically significant improvement in overall survival. It was assumed that the new, proven treatments from these trials established new standards for cancer care in the treatment community. Twenty-three positive SWOG treatment trials were identified from a variety of different disease settings., Main Outcomes and Measures: This study estimated population life-years gained from the 23 treatment trials through 2015 by mapping the effect of the new treatments onto the US cancer population using an area under the Kaplan-Meier survival curve approach that combined trial-specific hazard function and hazard ratio results, along with Surveillance, Epidemiology, and End Results program and life table data. Calculations were age adjusted. The US dollar return on investment was estimated as the ratio of the total investment by the NCI in the treatment trial program divided by the estimate of life-years gained., Results: In total, 12 361 patients were enrolled to the 23 positive trials from 1965 to 2012. The study estimated that 3.34 million (95% confidence limit, 2.39-4.15 million) life-years were gained from these 23 trials through 2015. Estimates were greater than 2 million life-years gained under most model simulations. The US dollar return on investment was $125 per life-year gained., Conclusions and Relevance: SWOG treatment trials have had a substantial impact on population survival for patients with cancer over 60 years. The NCI's investment in its cancer cooperative group research program has provided exceptional value and benefit to the American public through its research programs generating positive cancer treatment trials., Trial Registration: clinicaltrials.gov Identifiers: NCT00004001, NCT00075764, and NCT00644228.

Importance: Positive phase 3 cancer clinical trials are widely hailed, while trials with negative results are often interpreted as scientific failures. We hypothesized that these interpretations would be reflected in the scientific literature., Objective: To compare the scientific impact of positive vs negative phase 3 cancer clinical treatment trials., Design, Setting, and Participants: We examined the phase 3 trial history of SWOG, a national cancer clinical trials consortium, over a 30-year period (1985-2014). Scientific impact was assessed according to multiple publication and citation outcomes. Citation data were obtained using Google Scholar. Citation counts were compared using generalized estimating equations for Poisson regression. Any trial that was formally evaluated for the randomized treatment comparison was included for analysis of publication and citation outcomes. Trials were categorized as positive if they achieved a statistically significant result in favor of the new experimental treatment for the protocol-specified primary end point. Trials were categorized as negative if they achieved a statistically significant result in favor of standard therapy or a null result with no statistically significant benefit for either the experimental or standard therapy., Main Outcomes and Measures: Impact factors for the journals publishing the primary trial results, and the number of citations for the primary trial articles and all secondary articles associated with the trials., Results: Ninety-four studies enrolling n = 46 424 patients were analyzed. Twenty-eight percent of trials were positive (26 of 94). The primary publications from positive trials were published in journals with higher mean (SD) 2-year impact factors (28 [19] vs 18 [13]; P = .007) and were cited twice as often as negative trials (mean per year, 43 vs 21; relative risk, 2.0; 95% CI, 1.1-3.9; P = .03). However, the number of citations from all primary and secondary articles did not significantly differ between positive and negative trials (mean per year, 55 vs 45; relative risk, 1.2; 95% CI, 0.7-2.3; P = .53)., Conclusions and Relevance: The scientific impact of the primary articles from positive phase 3 randomized cancer clinical trials was twice as great as for negative trials. But when all of the articles associated with the trials were considered, the scientific impact between positive and negative trials was similar. Positive trials indicate clinical advances, but negative trials also have a sizeable scientific impact by generating important scientific observations and new hypotheses and by showing what new treatments should not be used.

Anticancer drugs are combined in an effort to treat a heterogeneous tumor or to maximize the pharmacodynamic effect. The development of combination regimens, while desirable, poses unique challenges. These include the selection of agents for combination therapy that may lead to improved efficacy while maintaining acceptable toxicity, the design of clinical trials that provide informative results for individual agents and combinations, and logistic and regulatory challenges. The phase I trial is often the initial step in the clinical evaluation of a combination regimen. In view of the importance of combination regimens and the challenges associated with developing them, the Clinical Trial Design (CTD) Task Force of the National Cancer Institute Investigational Drug Steering Committee developed a set of recommendations for the phase I development of a combination regimen. The first two recommendations focus on the scientific rationale and development plans for the combination regimen; subsequent recommendations encompass clinical design aspects. The CTD Task Force recommends that selection of the proposed regimens be based on a biologic or pharmacologic rationale supported by clinical and/or robust and validated preclinical evidence, and accompanied by a plan for subsequent development of the combination. The design of the phase I clinical trial should take into consideration the potential pharmacokinetic and pharmacodynamic interactions as well as overlapping toxicity. Depending on the specific hypothesized interaction, the primary endpoint may be dose optimization, pharmacokinetics, and/or pharmacodynamics (i.e., biomarker)., (©2014 American Association for Cancer Research.)

Background: Clinical trials test the efficacy of a treatment in a select patient population. We examined whether cancer clinical trial patients were similar to nontrial, "real-world" patients with respect to presenting characteristics and survival., Methods: We reviewed the SWOG national clinical trials consortium database to identify candidate trials. Demographic factors, stage, and overall survival for patients in the standard arms were compared with nontrial control subjects selected from the Surveillance, Epidemiology, and End Results program. Multivariable survival analyses using Cox regression were conducted. The survival functions from aggregate data across all studies were compared separately by prognosis (≥50% vs <50% average 2-year survival). All statistical tests were two-sided., Results: We analyzed 21 SWOG studies (11 good prognosis and 10 poor prognosis) comprising 5190 patients enrolled from 1987 to 2007. Trial patients were younger than nontrial patients (P < .001). In multivariable analysis, trial participation was not associated with improved overall survival for all 11 good-prognosis studies but was associated with better survival for nine of 10 poor-prognosis studies (P < .001). The impact of trial participation on overall survival endured for only 1 year., Conclusions: Trial participation was associated with better survival in the first year after diagnosis, likely because of eligibility criteria that excluded higher comorbidity patients from trials. Similar survival patterns between trial and nontrial patients after the first year suggest that trial standard arm outcomes are generalizable over the long term and may improve confidence that trial treatment effects will translate to the real-world setting. Reducing eligibility criteria would improve access to clinical trials.

The primary goal of seamless care is improved patient outcomes and improved standards of care for patients with cancer. The pharmacy service of the Newfoundland Cancer Treatment and Research Foundation conducted a randomized control study that measured clinical and humanistic outcomes of a pharmacist-directed seamless care program in an ambulatory oncology clinic. This article focuses on the intervention group, particularly the identification of drug-related problems (DRPs) and utilization of health care services as well the satisfaction of 3 types of health professionals with the services provided by the pharmacist-directed seamless care program. Overall, the seamless care pharmacist (SCP) identified an average of 3.7 DRPs per intervention patient; the most common DRP reported was a patient not receiving or taking a drug therapy for which there is an indication. The SCP identified more DRPs in patients receiving adjuvant treatment compared to those receiving palliative treatment. On average, family physicians, oncology nurses, and hospital pharmacists were satisfied with the SCP intervention indicating that they agreed the information collected and distributed by the SCP was useful to them. Pharmacist-directed seamless care services in an ambulatory oncology clinic have a significant impact on clinical outcomes and processes of patient care. The presence of a SCP can help identify and resolve DRPs experienced by patients in an outpatient oncology clinic, ensuring that patients are receiving the highest standard of care.

The standard phase II trial design has changed dramatically over the past decade. Randomized phase II studies have essentially become the standard phase II design in oncology for a variety of reasons. The use of these designs is motivated by concerns about the use of historical data to determine if a new agent or regimen shows promise of activity. However, randomized phase II designs come with the cost of increased study duration and patient resources. Progression-free survival (PFS) is an important endpoint used in many phase II designs. In many clinical settings, changes in PFS with the introduction of a new treatment may represent true benefit in terms of the gold standard outcome, overall survival (OS). The phase II/III design has been proposed as an approach to shorten the time of discovery of an active regimen. In this article, design considerations for a phase II/III trial are discussed and presented in terms of a model defining the relationship between OS and PFS. The design is also evaluated using 15 phase III trials completed in the Southwest Oncology Group (SWOG) between 1990 and 2005. The model provides a framework to evaluate the validity and properties of using a phase II/III design. In the evaluation of SWOG trials, three of four positive studies would have also proceeded to the final analysis and 10 of 11 negative studies would have stopped at the phase II analysis if a phase II/III design had been used. Through careful consideration and thorough evaluation of design properties, substantial gains could occur using this approach., (©2013 AACR)

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors.

Purpose: Testing agents in cancers with multiple disease subtypes, in which the activity of a new treatment may vary between subtypes, presents statistical and logistical challenges. We propose a flexible phase II strategy which includes both analyses for each histology or stratum and a combined analysis which borrows information from all the patients in the study. Sequential futility analyses are conducted once each subgroup or the overall group reaches a specified minimum accrual., Experimental Design: Examples based on a soft tissue sarcoma phase II trial, which includes multiple histologies and simulation studies, are used to assess the statistical properties of the proposed strategy., Results: The combined analyses in one phase II trial lead to smaller expected sample sizes when the drug is broadly inactive, and to greater statistical power if there is modest activity across multiple strata as compared with conducting several smaller phase II studies. In addition, by retaining the stratum-specific tests, the design allows the identification of subgroups for which the agents are most active., Conclusion: To consider phase II testing with multiple biological subtypes, a strategy which combines both the individual subgroup tests and overall combined tests has promising statistical properties. Our results support the appropriate use of statistical borrowing of information in phase II studies in this setting. More broadly, this work fits the paradigm that phase II studies should include as broad a group of patients as scientifically reasonable, but incorporate design considerations for subsets of patients with potentially differing responses to therapy.

As the use of molecularly targeted agents, which are anticipated to increase overall survival (OS)and progression-free survival (PFS) but not necessarily tumor response, has increased in oncology, there has been a corresponding increase in the recommendation and use of randomized phase II designs. Such designs reduce the potential for bias, existent in comparisons with historical controls, but also substantially increase the sample size requirements. We review the principal statistical designs for historically controlled and randomized phase II trials, along with their advantages, disadvantages, and statistical design considerations. We review the arguments for and against the use of randomization in phase II studies, the situations in which the use of historical controls is preferred, and the situations in which the use of randomized designs is preferred. We review methods used to calculate predicted OS or PFS values from historical controls, adjusted so as to be appropriate for an experimental sample with particular prognostic characteristics. We show how adjustment of the type I and type II error bounds for randomized studies can facilitate the detection of appropriate target increases in median PFS or OS with sample sizes appropriate for phase II studies. Although there continue to be differences among investigators concerning the use of randomization versus historical controls in phase II trials, there is agreement that each approach will continue to be appropriate, and the optimal approach will depend upon the circumstances of the individual trial.

Background: Interim analysis of Phase III trials typically includes testing for both efficacy and futility. Futility testing is commonly performed on the primary outcome at very low levels (e.g., one-sided alpha=0.0025) at one or two times before final analysis. When overall survival is the primary outcome and events accrue slowly, and if a suitable intermediate endpoint is available, then using this endpoint for interim futility testing may yield a higher probability of stopping early for futility in the absence of any treatment effect., Purpose: The purpose of this study is to explore the possibility of incorporating an intermediate endpoint into interim futility testing of Phase III trials., Methods: Using a simple two-stage exponential survival model based on recent Southwest Oncology Group Phase III studies in several disease settings, we perform a series of simulation studies. Survival data are simulated under both the null and alternative hypotheses, and analyzed using overall survival, progression-free survival, and a composite endpoint for futility testing., Results: In all disease settings examined here, when survival data were simulated under the null hypothesis, the probability of stopping a trial early for futility was substantially increased by incorporating PFS into interim futility analyses. When testing for futility with the composite endpoint, average patient accrual was reduced by 6-11% in a wide variety of disease settings. In the study scenario with the longest survival, the savings in study duration was more dramatic than in patient resources. When data were simulated under the alternative hypothesis, this procedure resulted in a negligible loss of power., Limitations: The properties of this procedure outside of the context of cancer clinical trials and/or using a different intermediate endpoint are not examined. These results also do not address its performance in the context of less conservative stopping rules., Conclusions: Interim futility monitoring of Phase III trials using a suitable intermediate endpoint may substantially increase the probability of stopping early for futility when there is no treatment effect. These simulation studies suggest that this would lead to meaningful reductions in study duration and patient resources in many disease settings, with no substantial loss of power for the primary test of efficacy. Future work is needed to explore in detail whether modifications to the stopping rules used in this procedure may yield greater savings without compromising power.

Purpose: The 31-year "war on cancer" has focused largely on therapeutic (as opposed to preventative) cancer research, which, in both the public and private sector, has received the majority of funding. Meanwhile the prevention of cancer has received less attention., Patients and Methods: We analyzed eight positive phase III therapeutic trials of the Southwest Oncology Group, and estimated how the observed improvements in survival from the new therapies would impact mortality at the population level (utilizing Surveillance, Epidemiology, and End Results-data). We compared these results with the impact of the Prostate Cancer Prevention Trial. The measure of impact was person-years saved in the first 5 years., Results: Estimates of person-years saved in the first 5 years included 28,534 from improved treatment of localized bladder cancer and 26,241 from improved treatment of advanced lung cancer, representing, respectively, 31.4% and 2.8% of the person-years which could have been saved for these diseases (the "relative impact of new treatment on survival"). The new therapies from all eight positive phase III trials would have saved 114,641 person-years over the first 5 years. The estimate from the Prostate Cancer Prevention Trial was 99,441 person-years over the first 5 years., Conclusion: New cancer therapies have a proven and quantifiable impact on population mortality. Successful cancer prevention has a similarly large impact. However, federal funding for cancer prevention is less than half that of cancer treatment. As a result of its enormous potential for extending life, cancer prevention warrants increased funding and support from federal funding agencies.