Treatment success in cancer: new cancer treatment successes identified in phase 3 randomized controlled trials conducted by the National Cancer Institute-sponsored cooperative oncology groups, 1955 to 2006

The Benefits and Risks of Receiving Investigational Solid Tumor Drugs in Randomized Trials : A Systematic Review and Meta-analysis

BACKGROUND

Many patients participate in cancer trials to access new therapies. The extent to which new treatments produce clinical benefit for trial participants is unclear.

PURPOSE

To estimate the progression-free survival (PFS) and overall survival (OS) advantage of assignment to experimental groups in randomized trials for 6 solid tumors.

DATA SOURCES

ClinicalTrials.gov was searched for trials of investigational drugs with results posted between 2017 and 2021.

STUDY SELECTION

Investigational drugs were defined as those not yet having full approval from the U.S. Food and Drug Administration for the study indication. Trials were included if they were randomized and tested drugs or biologics.

DATA EXTRACTION

Data extraction was completed by 2 independent reviewers. Data were pooled using a random-effects model.

DATA SYNTHESIS

The sample included 128 trials comprising 141 comparisons of a new drug and a comparator. These comparisons included 47 050 patients. The pooled hazard ratio for PFS was 0.80 (95% CI, 0.75 to 0.85), indicating statistically significant benefit for patients in experimental groups. This corresponded to a median PFS advantage of 1.25 months (CI, 0.80 to 1.68 months). The pooled hazard ratio for OS was 0.92 (CI, 0.88 to 0.95), corresponding to a survival gain of 1.18 months (CI, 0.72 to 1.71 months). The absolute risk for a serious adverse event for comparator group patients was 29.56% (CI, 26.64% to 32.65%), with an increase in risk of 7.40% (CI, 5.66% to 9.14%) for patients in experimental groups.

LIMITATIONS

Trials in this sample were heterogeneous. Comparator group interventions were assumed to reflect standard of care.

CONCLUSION

Assignment to experimental groups produces statistically significant survival gains. However, the absolute survival gain is small, and toxicity is statistically significantly greater. The findings of this review provide reassuring evidence that patients are not meaningfully disadvantaged by assignment to comparator groups.

PRIMARY FUNDING SOURCE

Canadian Institutes of Health Research.

Effectiveness of a mobile-based return to work program for decent return to work, fatigue, stress, and quality of working life among cancer survivors

PURPOSE

This study aimed to develop a smartphone mobile application-based supportive return to work (RTW) program for cancer survivors and evaluate its effects on their RTW, fatigue, stress, and quality of working life. This program was developed through a comprehensive process involving literature review, interviews with cancer survivors, and consultations with experts.

METHODS

A non-equivalent control group pre- and post-test design was used, with 41 participants assigned to the experimental (n = 18) and control (n = 23) groups based on recruitment timing. The experimental group received a 6-week smartphone mobile application-based supportive RTW program comprising "Counseling and Education" and "Self-Management." Participants completed assessments of decent RTW, fatigue, stress, and quality of working life at baseline and 6 months later. The experimental group completed an additional post-program completion survey.

RESULTS

During the 6-week program, no experimental group participants dropped out. The program's impact on decent RTW remains unclear. Fatigue (F = 2.52, p = 0.095) and quality of working life (F = 0.86, p = 0.434) did not show statistically significant differences. However, there was a significant reduction in stress (F = 4.59, p = 0.017).

CONCLUSION

The smartphone application-based RTW program, focusing on self-management and counseling, effectively reduced participants' stress levels. To further evaluate the effectiveness of the program, a more diverse range of interventions and ongoing programs should be implemented.

IMPLICATIONS FOR CANCER SURVIVORS

This study underscores the importance of tailored digital interventions to support the RTW of cancer survivors. The use of mobile smartphone applications allows temporal and spatial flexibility in program participation. Interventions involving various activities should be implemented to ensure ongoing participation.

Accuracy of Event Rate and Effect Size Estimation in Major Cardiovascular Trials: A Systematic Review

Importance

For the design of a randomized clinical trial (RCT), estimation of the expected event rate and effect size of an intervention is needed to calculate the sample size. Overestimation may lead to an underpowered trial.

Objective

To evaluate the accuracy of published estimates of event rate and effect size in contemporary cardiovascular RCTs.

Evidence Review

A systematic search was conducted in MEDLINE for multicenter cardiovascular RCTs associated with MeSH (Medical Subject Headings) terms for cardiovascular diseases published in the New England Journal of Medicine, JAMA, or the Lancet between January 1, 2010, and December 31, 2019. Identified trials underwent abstract review; eligible trials then underwent full review, and those with insufficiently reported data were excluded. Data were extracted from the original publication or the study protocol, and a random-effects model was used for data pooling. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. The primary outcome was the accuracy of event rate and effect size estimation. Accuracy was determined by comparing the observed event rate in the control group and the effect size with their hypothesized values. Linear regression was used to determine the association between estimation accuracy and trial characteristics.

Findings

Of the 873 RCTs identified, 374 underwent full review and 30 were subsequently excluded, resulting in 344 trials for analysis. The median observed event rate was 9.0% (IQR, 4.3% to 21.4%), which was significantly lower than the estimated event rate of 11.0% (IQR, 6.0% to 25.0%) with a median deviation of -12.3% (95% CI, -16.4% to -5.6%; P < .001). More than half of the trials (196 [61.1%]) overestimated the expected event rate. Accuracy of event rate estimation was associated with a higher likelihood of refuting the null hypothesis (0.13 [95% CI, 0.01 to 0.25]; P = .03). The median observed effect size in superiority trials was 0.91 (IQR, 0.74 to 0.99), which was significantly lower than the estimated effect size of 0.72 (IQR, 0.60 to 0.80), indicating a median overestimation of 23.1% (95% CI, 17.9% to 28.3%). A total of 216 trials (82.1%) overestimated the effect size.

Conclusions and Relevance

In this systematic review of contemporary cardiovascular RCTs, event rates of the primary end point and effect sizes of an intervention were frequently overestimated. This overestimation may have contributed to the inability to adequately test the trial hypothesis.

Design and synthesis of new spirooxindole candidates and their selenium nanoparticles as potential dual Topo I/II inhibitors, DNA intercalators, and apoptotic inducers

A new wave of dual Topo I/II inhibitors was designed and synthesised via the hybridisation of spirooxindoles and pyrimidines. In situ selenium nanoparticles (SeNPs) for some derivatives were synthesised. The targets and the SeNP derivatives were examined for their cytotoxicity towards five cancer cell lines. The inhibitory potencies of the best members against Topo I and Topo II were also assayed besides their DNA intercalation abilities. Compound 7d NPs exhibited the best inhibition against Topo I and Topo II enzymes with IC50 of 0.042 and 1.172 μM, respectively. The ability of compound 7d NPs to arrest the cell cycle and induce apoptosis was investigated. It arrested the cell cycle in the A549 cell at the S phase and prompted apoptosis by 41.02% vs. 23.81% in the control. In silico studies were then performed to study the possible binding interactions between the designed members and the target proteins.

Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis

BACKGROUND

Bayesian methods have potential for efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Furthermore, value of information (VOI) methods estimate the value of reducing decision uncertainty, aiding transparent research prioritization. These methods require a prior distribution describing current uncertainty in key parameters, such as relative treatment effect (RTE). However, at the time of designing and commissioning research, there may be no data to base the prior on. The aim of this article is to present methods to construct priors for RTEs based on a collection of previous RCTs.

METHODS

We developed 2 Bayesian hierarchical models that captured variability in RTE between studies within disease area accounting for study characteristics. We illustrate the methods using a data set of 743 published RCTs across 9 disease areas to obtain predictive distributions for RTEs for a range of disease areas. We illustrate how the priors from such an analysis can be used in a VOI analysis for an RCT in bladder cancer and compare the results with those using an uninformative prior.

RESULTS

For most disease areas, the predicted RTE favored new interventions over comparators. The predicted effects and uncertainty differed across the 9 disease areas. VOI analysis showed that the expected value of research is much lower with our empirically derived prior compared with an uninformative prior.

CONCLUSIONS

This study demonstrates a novel approach to generating informative priors that can be used to aid research prioritization and trial design. The methods can also be used to combine RCT evidence with expert opinion. Further work is needed to create a rich database of RCT evidence that can be used to form off-the-shelf priors.

HIGHLIGHTS

Bayesian methods have potential to aid the efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Value-of-information (VOI) methods can be used to aid research prioritization by calculating the value of current decision uncertainty.These methods require a distribution describing current uncertainty in key parameters, that is, "prior distributions."This article demonstrates a methodology to estimate prior distributions for relative treatment effects (odds and hazard ratios) estimated from a collection of previous RCTs.These results may be combined with expert elicitation to facilitate 1) value-of-information methods to prioritize research or 2) Bayesian methods for research design.

Qualitative study of Oncology Clinicians' Perceptions of Barriers to Offering Clinical Trials to Underserved Populations

INTRODUCTION

Cancer clinical trials represent the "gold standard" for advancing novel cancer therapies. Optimizing trial participation is critical to ensuring the generalizability of findings across patients, yet trial enrollment rates, particularly among minority and socioeconomically disadvantaged populations, remain suboptimal.

METHODS

We conducted in-depth interviews with oncologists at a large academic medical center to explore their (1) attitudes and perceived barriers to offering clinical trials to minority and socioeconomically disadvantaged patients, and (2) recommendations for improving the enrollment of minority and socioeconomically disadvantaged patients in cancer clinical trials.

RESULTS

Of 23 medical oncologists approached, 17 enrolled (74% response rate; mean age = 47; female = 42%; White = 67%). Content analysis revealed several barriers to enrollment: (1) ethical dilemmas; (2) ambivalence about trial risks and benefits; and (3) concern about patient well-being. Concerns about the legitimacy of informed consent, perceived lack of equipoise, and fear of personal bias influenced clinicians' decisions to recommend trials during treatment discussions. Concerns about creating an imbalance between trial risks and benefits among patients with high-level needs, including patients with literacy, psychiatric, and other socioeconomic vulnerabilities, impacted clinicians' enthusiasm to engage in trial discussions. Clinicians identified patient, provider, and system-level solutions to address challenges, including increasing patient and clinician support as well as involving external personnel to support trial enrollment.

CONCLUSION

Findings reveal multi-level barriers to offering cancer clinical trials to underrepresented patients. Targeted solutions, including system level changes to support clinicians, patient financial support, and implementation of clinical trial navigation programs were recommended to help reduce access barriers and increase enrollment of underrepresented patients into cancer clinical trials.

Uptake of the multi-arm multi-stage (MAMS) adaptive platform approach: a trial-registry review of late-phase randomised clinical trials

BACKGROUND

For medical conditions with numerous interventions worthy of investigation, there are many advantages of a multi-arm multi-stage (MAMS) platform trial approach. However, there is currently limited knowledge on uptake of the MAMS design, especially in the late-phase setting. We sought to examine uptake and characteristics of late-phase MAMS platform trials, to enable better planning for teams considering future use of this approach.

DESIGN

We examined uptake of registered, late-phase MAMS platforms in the EU clinical trials register, Australian New Zealand Clinical Trials Registry, International Standard Randomised Controlled Trial Number registry, Pan African Clinical Trials Registry, WHO International Clinical Trial Registry Platform and databases: PubMed, Medline, Cochrane Library, Global Health Library and EMBASE. Searching was performed and review data frozen on 1 April 2021. MAMS platforms were defined as requiring two or more comparison arms, with two or more trial stages, with an interim analysis allowing for stopping of recruitment to arms and typically the ability to add new intervention arms.

RESULTS

62 late-phase clinical trials using an MAMS approach were included. Overall, the number of late-phase trials using the MAMS design has been increasing since 2001 and been accelerated by COVID-19. The majority of current MAMS platforms were either targeting infectious diseases (52%) or cancers (29%) and all identified trials were for treatment interventions. 89% (55/62) of MAMS platforms were evaluating medications, with 45% (28/62) of the MAMS platforms having at least one or more repurposed medication as a comparison arm.

CONCLUSIONS

Historically, late-phase trials have adhered to long-established standard (two-arm) designs. However, the number of late-phase MAMS platform trials is increasing, across a range of different disease areas. This study highlights the potential scope of MAMS platform trials and may assist research teams considering use of this approach in the late-phase randomised clinical trial setting.

PROSPERO REGISTRATION NUMBER

CRD42019153910.

IDENTIFICATION OF THRESHOLD FOR LARGE (DRAMATIC) EFFECTS THAT WOULD OBVIATE RANDOMIZED TRIALS IS NOT POSSIBLE

OBJECTIVE

To analyze distribution of "dramatic", large treatment effects.

STUDY DESIGN & SETTING

Pareto distribution modeling of previously reported cohorts of 3,486 randomized trials (RCTs) that enrolled 1,532,459 patients and 730 non-randomized studies (NRS) enrolling 1,650,658 patients.

RESULTS

We calculated the Pareto α parameter, which determines the tail of the distribution for various starting points of distribution [odds ratio_min (OR_min)]. In default analysis using all data at OR_min ≥1, Pareto distribution fit well to the treatment effects of RCTs favoring the new treatments (P = 0.21, Kolmogorov-Smirnov test) with best α = 2.32. For NRS, Pareto fit for OR_min ≥2 with best α = 1.91. For RCTs, theoretical 99th percentile OR was 32.7. The actual 99th percentile OR was 25; which converted into relative risk (RR) = 7.1. The maximum observed effect size was OR = 121 (RR = 11.45). For NRS, theoretical 99th percentile was OR = 315. The actual 99th percentile OR was 294 (RR = 13). The maximum observed effect size was OR = 1473 (RR = 66).

CONCLUSIONS

The effects sizes observed in RCTs and NRS considerably overlap. Large effects are rare and there is no clear threshold for dramatic effects that would obviate future RCTs.

Effects of financial incentives on volunteering for clinical trials: A randomized vignette experiment

BACKGROUND

Financial incentives may aid recruitment to clinical trials, but evidence regarding risk/burden-driven variability in participant preferences for incentives is limited. We developed and tested a framework to support real-world decisions on recruitment budget.

METHODS

We included two phases: an Anchoring Survey, to ensure we could capture perceived unpleasantness on a range of life events, and a Vignette Experiment, to explore relationships between financial incentives and participants' perceived risk/burden and willingness to participate in high- and low-risk/burden versions of five vignettes drawn from common research activities. We compared vignette ratings to identify similarly rated life events from the Anchoring Survey to contextualize ratings of study risk.

RESULTS

In our Anchoring Survey (n = 643), mean ratings (scale 1 = lowest risk/burden to 5 = highest risk/burden) indicated that the questions made sense to participants, with highest risk assigned to losing house in a fire (4.72), and lowest risk assigned to having blood pressure taken (1.13). In the Vignette Experiment (n = 534), logistic regression indicated that amount of offered financial incentive and perceived risk/burden level were the top two drivers of willingness to participate in four of the five vignettes. Comparison of event ratings in the Anchoring Survey with the Vignette Experiment ratings suggested reasonable concordance on severity of risk/burden.

CONCLUSIONS

We demonstrated feasibility of a framework for assessing participant perceptions of risk for study activities and discerned directionality of relationship between financial incentives and willingness to participate. Future work will explore use of this framework as an evidence-gathering approach for gauging appropriate incentives in real-world study contexts.

Cancer Clinical Trial Participation at the 1-Year Anniversary of the Outbreak of the COVID-19 Pandemic

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.

An open-source, expert-designed decision tree application to support accurate diagnosis of myeloid malignancies

Accurate, reproducible diagnoses can be difficult to make in haemato-oncology due to multi-parameter clinical data, complex diagnostic criteria and time-pressured environments. We have designed a decision tree application (DTA) that reflects WHO diagnostic criteria to support accurate diagnoses of myeloid malignancies. The DTA returned the correct diagnoses in 94% of clinical cases tested. The DTA maintained a high level of accuracy in a second validation using artificially generated clinical cases. Optimisations have been made to the DTA based on the validations, and the revised version is now publicly available for use at http://bit.do/ADAtool.

Association of Patient Demographic Characteristics and Insurance Status With Survival in Cancer Randomized Clinical Trials With Positive Findings

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.

Systematic Review and Meta-Analysis of the Magnitude of Structural, Clinical, and Physician and Patient Barriers to Cancer Clinical Trial Participation

BACKGROUND

Barriers to cancer clinical trial participation have been the subject of frequent study, but the rate of trial participation has not changed substantially over time. Studies often emphasize patient-related barriers, but other types of barriers may have greater impact on trial participation. Our goal was to examine the magnitude of different domains of trial barriers by synthesizing prior research.

METHODS

We conducted a systematic review and meta-analysis of studies that examined the trial decision-making pathway using a uniform framework to characterize and quantify structural (trial availability), clinical (eligibility), and patient/physician barrier domains. The systematic review utilized the PubMed, Google Scholar, Web of Science, and Ovid Medline search engines. We used random effects to estimate rates of different domains across studies, adjusting for academic vs community care settings.

RESULTS

We identified 13 studies (nine in academic and four in community settings) with 8883 patients. A trial was unavailable for patients at their institution 55.6% of the time (95% confidence interval [CI] = 43.7% to 67.3%). Further, 21.5% (95% CI = 10.9% to 34.6%) of patients were ineligible for an available trial, 14.8% (95% CI = 9.0% to 21.7%) did not enroll, and 8.1% (95% CI = 6.3% to 10.0%) enrolled. Rates of trial enrollment in academic (15.9% [95% CI = 13.8% to 18.2%]) vs community (7.0% [95% CI = 5.1% to 9.1%]) settings differed, but not rates of trial unavailability, ineligibility, or non-enrollment.

CONCLUSIONS

These findings emphasize the enormous need to address structural and clinical barriers to trial participation, which combined make trial participation unachievable for more than three of four cancer patients.

Influence of Modeling Choices on Value of Information Analysis: An Empirical Analysis from a Real-World Experiment

BACKGROUND

Value of information (VOI) analysis often requires modeling to characterize and propagate uncertainty. In collaboration with a cancer clinical trial group, we integrated a VOI approach to assessing trial proposals.

OBJECTIVE

This paper aims to explore the impact of modeling choices on VOI results and to share lessons learned from the experience.

METHODS

After selecting two proposals (A: phase III, breast cancer; B: phase II, pancreatic cancer) for in-depth evaluations, we categorized key modeling choices relevant to trial decision makers (characterizing uncertainty of efficacy, evidence thresholds to change clinical practice, and sample size) and modelers (cycle length, survival distribution, simulation runs, and other choices). Using a $150,000 per quality-adjusted life-year (QALY) threshold, we calculated the patient-level expected value of sample information (EVSI) for each proposal and examined whether each modeling choice led to relative change of more than 10% from the averaged base-case estimate. We separately analyzed the impact of the effective time horizon.

RESULTS

The base-case EVSI was $118,300 for Proposal A and $22,200 for Proposal B per patient. Characterizing uncertainty of efficacy was the most important choice in both proposals (e.g. Proposal A: $118,300 using historical data vs. $348,300 using expert survey), followed by the sample size and the choice of survival distribution. The assumed effective time horizon also had a substantial impact on the population-level EVSI.

CONCLUSIONS

Modeling choices can have a substantial impact on VOI. Therefore, it is important for groups working to incorporate VOI into research prioritization to adhere to best practices, be clear in their reporting and justification for modeling choices, and to work closely with the relevant decision makers, with particular attention to modeling choices.

Clinical trial participation and aggressive care at the end of life in patients with ovarian cancer

OBJECTIVES

In non-gynecologic cancers, clinical trial participation has been associated with aggressive care at the end of life. The objective of this investigation was to examine how trial participation affects end of life outcomes in patients with ovarian cancer.

METHODS

In a retrospective review of women diagnosed with ovarian cancer at our institution between January 2010 and December 2015, we collected variables identified by the National Quality Forum as measures of aggressive end of life care including chemotherapy in the last 14 days of life, intensive care unit (ICU) admission in the last 30 days of life, or death in the acute care setting. Trials investigating medications but not surgical interventions were included. The primary outcome of this study was the association between trial participation and the National Quality Forum measures of aggressive end of life care in ovarian cancer decedents. Data were analyzed with univariable and multivariable parametric and non-parametric testing, and time to event outcomes were analyzed using the Kaplan-Meier method and Cox's proportional hazard models.

RESULTS

Among 391 women treated for ovarian cancer, 62 patients (16%) participated in a clinical trial. Patients enrolled in clinical trials were more likely to have chemotherapy administered within 14 days of death; however, no association was found with other metrics of aggressive care at the end of life including the initiation of a new chemotherapy regimen in the last 30 days of life, ICU admissions, and death in an acute care setting. Among patients with recurrent ovarian cancer, median overall survival for trial participants was 57 months compared with only 31 months in non-trial participants (p<0.001).

CONCLUSIONS

In patients with ovarian cancer, clinical trial enrollment is associated with chemotherapy administration within 14 days of death, but not other measures of aggressive care at the end of life. Given the importance of clinical trial participation in improving care for women with ovarian cancer, this study suggests that concerns regarding aggressive care prior to death should not limit clinical trial participation.

'Optimism bias' in contemporary national clinical trial network phase III trials: are we improving?

Background

Previous studies have found that overestimating treatment effects (i.e. 'optimism bias') leads to underpowered clinical trials. The prevalence of 'optimism bias' in contemporary National Clinical Trials Network (NCTN) cancer clinical trials is unknown.

Methods

We conducted a systematic review of NCTN phase III randomized trials published from January 2007 to January 2017. We compared the hypothesized versus observed treatment effects in each trial, and examined whether trial-related factors were correlated with the study results. We also reviewed the methods of each protocol to assess whether a rationale for the hypothesized effect size was provided.

Results

We identified 161 clinical trials, of which 130 were eligible for analysis. Original protocols could not be located for 8 trials (5.0%). Twenty-eight trials (21.5%) observed a statistically significant difference in the primary end point favoring the experimental treatment. The median ratio of observed-to-expected hazard ratios among trials that observed a statistically significant effect on the primary end point was 1.07 (range: 0.33-1.28) versus 1.32 (range: 0.86-2.02) for trials that did not, compared with 1.34 and 1.86, respectively, for National Cancer Institute (NCI) trials published between 1955 and 2006. An effect size at least as large as the one projected in the protocol trials was observed in 9.8% of trials, compared with 17% of NCI trials published from 1955 to 2006. Most trials (64.6%) provided no rationale to support the magnitude of the proposed treatment effect in the protocol.

Conclusions

Despite a reduction in 'optimism bias' compared with previous eras, most contemporary NCTN phase III trials failed to establish statistically significant benefits of new cancer therapies. Better rationalization of proposed effect sizes in research protocols is needed.

Intervention development and treatment success in UK health technology assessment funded trials of physical rehabilitation: a mixed methods analysis

OBJECTIVES

Physical rehabilitation is a complex process, and trials of rehabilitation interventions are increasing in number but often report null results. This study aimed to establish treatment success rates in physical rehabilitation trials funded by the National Institute of Health Research Health Technology Assessment (NIHR HTA) programme and examine any relationship between treatment success and the quality of intervention development work undertaken.

DESIGN

This is a mixed methods study.

SETTING

This study was conducted in the UK.

METHODS

The NIHR HTA portfolio was searched for all completed definitive randomised controlled trials of physical rehabilitation interventions from inception to July 2016. Treatment success was categorised according to criteria developed by Djulbegovic and colleagues. Detailed textual data regarding any intervention development work were extracted from trial reports and supporting publications and informed the development of quality ratings. Mixed methods integrative analysis was undertaken to explore the relationship between quantitative and qualitative data using joint displays.

RESULTS

Fifteen trials were included in the review. Five reported a definitive finding, four of which were in favour of the 'new' intervention. Eight trials reported a true negative (no difference) outcome. Integrative analysis indicated those with lower quality intervention development work were less likely to report treatment success.

CONCLUSIONS

Despite much effort and funding, most physical rehabilitation trials report equivocal findings. Greater focus on high quality intervention development may reduce the likelihood of a null result in the definitive trial, alongside high quality trial methods and conduct.

Identification of repurposed protein kinase B binders from FDA-approved drug library: a hybrid-structure activity relationship and systems modeling based approach

Food and Drug Administration (FDA)-approved drugs may be repurposed against those diseases, for which their therapeutic action has not been described. The present study deals with repurposing FDA-approved drugs for selective targeting of protein kinase B (PKB/Akt) for anti-cancer activity, through a two-tier (Cell and Target) model hybridization protocol implemented with support vector machine-based learning method. The hybridization was done as per rules of reaction kinetics. The hybridization process was facilitated as a standalone application for free access at https://github.com/undwivedi/Akt-Selective.git. The selectivity of the ligands for PKB/Akt binding was also evaluated on the basis of mitophagy system model for anti-apoptotic activity. Screening of the FDA-approved drug library, using the developed H- SAR model, led to identification of four compounds (Cas nos. 94749-08-3, 57808-66-9, 62-13-5, 76-43-7), bearing the selectivity for PKB/Akt. Since, the identified compounds have already crossed the barriers of absorption, distribution, metabolism, excretion, toxicity in clinical trials, therefore are safe to be considered for repurposing individually or in combination with other drugs.Communicated by Ramaswamy H. Sarma.

Methodological problems in the method used by IQWiG within early benefit assessment of new pharmaceuticals in Germany

BACKGROUND

The decision matrix applied by the Institute for Quality and Efficiency in Health Care (IQWiG) for the quantification of added benefit within the early benefit assessment of new pharmaceuticals in Germany with its nine fields is quite complex and could be simplified. Furthermore, the method used by IQWiG is subject to manifold criticism: (1) it is implicitly weighting endpoints differently in its assessments favoring overall survival and, thereby, drug interventions in fatal diseases, (2) it is assuming that two pivotal trials are available when assessing the dossiers submitted by the pharmaceutical manufacturers, leading to far-reaching implications with respect to the quantification of added benefit, and, (3) it is basing the evaluation primarily on dichotomous endpoints and consequently leading to an information loss of usable evidence.

OBJECTIVE

To investigate if criticism is justified and to propose methodological adaptations.

METHODS

Analysis of the available dossiers up to the end of 2016 using statistical tests and multinomial logistic regression and simulations.

RESULTS

It was shown that due to power losses, the method does not ensure that results are statistically valid and outcomes of the early benefit assessment may be compromised, though evidence on favoring overall survival remains unclear. Modifications, however, of the IQWiG method are possible to address the identified problems.

CONCLUSION

By converging with the approach of approval authorities for confirmatory endpoints, the decision matrix could be simplified and the analysis method could be improved, to put the results on a more valid statistical basis.

Design analysis indicates Potential overestimation of treatment effects in randomized controlled trials supporting Food and Drug Administration cancer drug approvals

OBJECTIVE

Statistical significance drives interpretation of randomized controlled trials (RCTs). We examined the type S error risk-claiming a new drug is falsely beneficial-and exaggeration ratio-how estimated effects differ from true effects-to re-emphasize direction and magnitude of treatment effects.

STUDY DESIGN AND SETTING

We systematically reviewed RCTs supporting Food and Drug Administration (FDA) approval of cancer drugs between 2007 and 2016. We extracted data for overall survival (OS), progression-free survival (PFS), and response outcomes from FDA reviews. We estimated type S error risks and exaggeration ratios by considering replicated RCTs of equal size and a range of true effects.

RESULTS

We analyzed 42 RCTs for 39 approved drugs. Across 38 RCTs reporting OS, the median type S error risk was 0.00% (Q1-Q3, 0.00-0.01%) and 3.56% (0.40-6.74%), for true hazard ratios of 0.7 and 0.9, respectively, indicating confidence in effect direction. The corresponding exaggeration ratios were 1.09 (1.01-1.11) and 1.30 (1.13-1.42), indicating median overestimations of 9% and 30%. Similar results held for PFS and response outcomes.

CONCLUSIONS

The type S error risk and exaggeration ratio provide additional insights into the replicability of RCTs. Our analyses also quantify the winner's curse, in which pivotal RCTs tend toward overoptimism.

Efficacy, Safety, and Regulatory Approval of Food and Drug Administration–Designated Breakthrough and Nonbreakthrough Cancer Medicines

Purpose

The breakthrough therapy program was established in 2012 to expedite the development and review of new medicines. We evaluated the times to approval, efficacy, and safety of breakthrough-designated versus non–breakthrough-designated cancer drugs approved by the US Food and Drug Administration (FDA).

Methods

We studied all new cancer drugs approved by the FDA between January 2012 and December 2017. Regulatory and therapeutic characteristics (time to FDA approval, pivotal trial efficacy end point, novelty of mechanism of action) were compared between breakthrough-designated and non–breakthrough-designated cancer drugs. Random-effects meta-regression was used to assess the association between breakthrough therapy designation and hazard ratios for progression-free survival (PFS), response rates (RRs) for solid tumors, serious adverse events, and deaths not attributed to disease progression.

Results

Between 2012 and 2017, the FDA approved 58 new cancer drugs, 25 (43%) of which received breakthrough therapy designation. The median time to first FDA approval was 5.2 years for breakthrough-designated drugs versus 7.1 years for non–breakthrough-designated drugs (difference, 1.9 years; P = .01). There were no statistically significant differences between breakthrough-designated and non–breakthrough-designated drugs in median PFS gains (8.6 v 4.0 months; P = .11), hazard ratios for PFS (0.43 v 0.51; P = .28), or RRs for solid tumors (37% v 39%; P = .74). Breakthrough therapy–designated drugs were not more likely to act via a novel mechanism of action (36% v 39%; P = 1.00). Rates of deaths (6% v 4%; P = .99) and serious adverse events (38% v 36%; P = 0.93) were also similar in breakthrough-designated and non–breakthrough-designated drugs.

Conclusion

Breakthrough-designated cancer drugs were associated with faster times to approval, but there was no evidence that these drugs provide improvements in safety or novelty; nor was there a statistically significant efficacy advantage when compared with non–breakthrough-designated drugs.

Better to be in The Placebo Arm for Trials of Neurological Therapies?

Patients with progressive neurodegenerative diseases often pursue trial entry seeking to access cutting edge therapies. However, cutting edge therapies for neurodegenerative diseases tend to have higher adverse event rates and underperform placebo. This essay argues that patients seeking trial entry are probably better off, medically, by being assigned to the placebo arm. Because trials involve extra clinic visits and research procedures, patients may be still better off medically by skipping trial participation altogether. I close by arguing that the Neurology research community might better honor the contributions of research subjects by pressing sponsors to promptly publish the results of non-positive trials, minimizing the use of uneven randomization ratios that favor assignment to the investigational treatment, and by fostering systematic collection of data on the risk/benefit balance of trial participation.

Clinical Equipoise for Trials of Novel Biologic Therapies, Therapeutic Success Rates, and Predictors of Success: A Meta-Analysis

Purpose

The demand for more rapid access to novel biologic therapies than randomized controlled trials can deliver is a topic of ongoing study and debate. We aimed to inform this debate by estimating therapeutic success from phase III trials comparing novel biologic therapies with standard of care and identifying predictors of success.

Methods

This was a meta-analysis of phase III trials evaluating novel biologic therapies in advanced breast, colorectal, lung, and prostate cancers. Therapeutic success was defined as statistically significant results for the primary end point favoring novel biologic therapies.

Results

Of 119 included phase III trials (76,726 patients), therapeutic success was 41%, with a statistically significant relative reduction in disease progression and death for novel biologic therapies over standard of care of 20% and 8%. Therapeutic success did not improve over time (pre-2010, 33%; 2010 to 2014, 44%; P = .2). Predictors of success were a biomarker-selected population (odds ratio, 4.74; 95% CI, 2.05 to 10.95) and progression-free survival end point compared with overall survival (odds ratio, 5.22; 95% CI, 2.41 to 11.39). Phase III trials with a biomarker-selected population showed a larger 28% progression-free survival benefit than phase III trials overall (hazard ratio, 0.72; 95% CI, 0.70 to 0.75) but similar 8% overall survival benefit (hazard ratio, 0.92; 95% CI, 0.90 to 0.94). Therapeutic success of phase III trials with and without a preceding phase II trial were 43% and 30%, respectively

Conclusion

Therapeutic success of novel biologic therapies in phase III trials, including therapies with a matching predictive biomarker, was modest and has not significantly improved over time. Equipoise remains and supports the ongoing ethical and scientific requirement for phase III randomized controlled trials to estimate treatment efficacy and assess the value of potential biomarkers.

Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology

Importance

Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?

Objective

To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.

Data Sources

We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.

Study Selection

The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).

Data Extraction and Synthesis

The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.

Main Outcomes and Measures

Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.

Results

We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.

Conclusions and Relevance

Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.

The Scientific Impact of Positive and Negative Phase 3 Cancer Clinical Trials

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.

A Model of Cancer Clinical Trial Decision-making Informed by African-American Cancer Patients

BACKGROUND

Clinical trials are critical to advancing cancer treatment. Minority populations are underrepresented among trial participants, and there is limited understanding of their decision-making process and key determinants of decision outcomes regarding trial participation.

METHODS

To understand research decision-making among clinical trial-eligible African-American cancer patients at Johns Hopkins, we conducted seven focus groups (n=32) with trial-offered patients ≥ 18 years diagnosed with lung, breast, prostate, or colorectal cancer ≤ 5 years. Three "acceptor" and four "decliner" focus groups were conducted. Questions addressed: attitudes towards clinical trials, reasons for accepting or declining participation, and recommendations to improve minority recruitment and enrollment. Data were transcribed and analyzed using traditional approaches to content and thematic analysis in NVivo 9.0. Data coding resulted in themes that supported model construction.

RESULTS

Participant experiences revealed the following themes when describing the decision-making process: Information gathering, Intrapersonal perspectives, and Interpersonal influences. Decision outcomes included the presence or absence of decision regret and satisfaction. From these themes, we generated a Model of Cancer Clinical Trial Decision-making.

CONCLUSION

Our model should be tested in hypothesis-driven research to elucidate factors and processes influencing decision balance and outcomes of trial-related decision-making. The model should also be tested in other disparities populations and for diagnoses other than cancer.

Development and Evaluation of an Approach to Using Value of Information Analyses for Real-Time Prioritization Decisions Within SWOG, a Large Cancer Clinical Trials Cooperative Group

OBJECTIVE

Value of information (VOI) analyses can align research with areas with the greatest potential impact on patient outcome, but questions remain concerning the feasibility and acceptability of these approaches to inform prioritization decisions. Our objective was to develop a process for calculating VOI in "real time" to inform trial funding decisions within SWOG, a large cancer clinical trials group.

METHODS

We developed an efficient and scalable VOI modeling approach using a selected sample of 9 randomized phase II/III trial proposals from the Breast, Gastrointestinal, and Genitourinary Disease Committees reviewed by SWOG's leadership between 2008 and 2013. There was bidirectional communication between SWOG investigators and the research team throughout the modeling development. Partial expected value of sample information for the treatment effect evaluated by the proposed trial's primary endpoint was calculated using Monte Carlo simulation.

RESULTS

We derived prior uncertainty in the treatment effect estimate from the sample size calculations. Our process was feasible for 8 of 9 trial proposals and efficient: the time required of 1 researcher was <1 week per proposal. We accommodated stakeholder input primarily by deconstructing VOI metrics into expected health benefits and incremental healthcare costs and assuming treatment decisions within our simulations were based on health benefits. Following customization, feedback from over 200 SWOG members was positive regarding the overall VOI framework, specific retrospective results, and potential for VOI analyses to inform future trial proposal evaluations.

CONCLUSIONS

We developed an efficient and customized process to calculate the expected VOI of cancer clinical trials that is feasible for use in decision making and acceptable to investigators. Prospective use and evaluation of this approach is currently underway within SWOG.

A Comprehensive Program for the Enhancement of Accrual to Clinical Trials

BACKGROUND

The Ohio State University Comprehensive Cancer Center (OSUCCC) embarked on a single institution campaign over 2 years to enhance the enrollment of cancer patients into therapeutic clinical trials. The goal of this campaign was to achieve a 40 % increase in accrual over a 2-year period.

METHODS

The entire process of accruing patients to clinical trials at the OSUCCC was carefully evaluated and broken down into several interlocking components. The four key areas of emphasis were as follows: (i) tasking of OSUCCC leadership with increased oversight of the entire process; (ii) education of all stakeholders [patients, their families, nurses and staff, physicians (both internal and external), Disease-Specific Committees (DSCs), and the OSUCCC leadership] as to the purpose, advantages, and availability of clinical trials, with an emphasis on accrual to cancer clinical trials (CCTs) being a critical function of all OSUCCC employees; (iii) increased oversight of the portfolio of clinical trials by DSCs; and (iv) optimization of accrual operations and infrastructure center-wide.

RESULTS

The accrual goal was achieved a full 4 months ahead of schedule. In total, 2327 patients were accrued to therapeutic clinical trials over the course of this 2-year campaign. Prior to implementation of the accrual program, the accrual rate was consistently below 15 %. From 2009 onwards, the therapeutic accrual rate was always greater than 25 %.

CONCLUSIONS

A campaign to educate key stakeholders in the clinical trials accrual process was successful in its goal of increasing accrual to therapeutic trials.

Clinical trial metadata: defining and extracting metadata on the design, conduct, results and costs of 125 randomised clinical trials funded by the National Institute for Health Research Health Technology Assessment programme

BACKGROUND

By 2011, the Health Technology Assessment (HTA) programme had published the results of over 100 trials with another 220 in progress. The aim of the project was to develop and pilot 'metadata' on clinical trials funded by the HTA programme.

OBJECTIVES

The aim of the project was to develop and pilot questions describing clinical trials funded by the HTA programme in terms of it meeting the needs of the NHS with scientifically robust studies. The objectives were to develop relevant classification systems and definitions for use in answering relevant questions and to assess their utility.

DATA SOURCES

Published monographs and internal HTA documents.

REVIEW METHODS

A database was developed, 'populated' using retrospective data and used to answer questions under six prespecified themes. Questions were screened for feasibility in terms of data availability and/or ease of extraction. Answers were assessed by the authors in terms of completeness, success of the classification system used and resources required. Each question was scored to be retained, amended or dropped.

RESULTS

One hundred and twenty-five randomised trials were included in the database from 109 monographs. Neither the International Standard Randomised Controlled Trial Number nor the term 'randomised trial' in the title proved a reliable way of identifying randomised trials. Only limited data were available on how the trials aimed to meet the needs of the NHS. Most trials were shown to follow their protocols but updates were often necessary as hardly any trials recruited as planned. Details were often lacking on planned statistical analyses, but we did not have access to the relevant statistical plans. Almost all the trials reported on cost-effectiveness, often in terms of both the primary outcome and quality-adjusted life-years. The cost of trials was shown to depend on the number of centres and the duration of the trial. Of the 78 questions explored, 61 were well answered, 33 fully with 28 requiring amendment were the analysis updated. The other 17 could not be answered with readily available data.

LIMITATIONS

The study was limited by being confined to 125 randomised trials by one funder.

CONCLUSIONS

Metadata on randomised controlled trials can be expanded to include aspects of design, performance, results and costs. The HTA programme should continue and extend the work reported here.

FUNDING

The National Institute for Health Research HTA programme.

Patient Support Groups Identifying Clinical Equipoise in UK Gynaecological Oncology Surgeons as the Basis for Trials in Ultraradical Surgery for Advanced Ovarian Cancer

"Clinical equipoise" is defined as the genuine uncertainty by the expert medical community of the most beneficial treatment. A survey performed in 2013 by a patient support group, Ovacome, of gynaecological oncologists in the UK on ultra-radical surgery in advanced ovarian cancer has shown that there is a wide variation in surgical practice across the country. In addition, there were mixed views on the quality of published evidence justifying it's performance, signifying a state of clinical equipoise. The survey also identified widespread insufficient infra-structural resources and lack of surgical training and skills. The majority of respondents would be prepared to undertake additional training to acquire the surgical skills and/or refer to other centres/surgeons already performing the surgery and/or recruit to surgical trials investigating ultra-radical surgery in advanced ovarian cancer.

Control treatments in biologics trials of rheumatoid arthritis were often not deemed acceptable in the context of care

OBJECTIVES

Control treatments in randomized controlled trials (RCTs) should not deliberately disadvantage patients. The objectives of the study were to compare (1) willingness to include vs. (2) willingness to prescribe control treatment among physicians randomized to assess, respectively, either (1) enrollment in a trial or (2) appropriateness of control treatment in a care context for the same fictional patient.

STUDY DESIGN AND SETTING

Physicians were authors of articles about rheumatoid arthritis (RA), involved in RA patient care, and used to enrolling patients in trials. The outcomes were willingness to give control treatment: trial enrollment or control-treatment appropriateness in care context. We derived three case vignettes of fictional standard eligible patients for each of 30 RCTs assessing biologics in RA. Physicians were randomly allocated to the "trial" or "care" arm. For each of the 90 fictional patients, physicians assigned to the trial arm were asked if they would enroll the patient in the RCT the patient was derived from. For the same 90 fictional patients, physicians assigned to the care arm were asked if the control treatment of the RCT was appropriate in a context of usual care.

RESULTS

Of the 1,779 physicians invited to participate, 151 were randomized. Half of the fictional patients {41/90; 45% [95% confidence interval (CI): 37%, 53%]} would be enrolled in the RCT although the control-arm treatment of the RCT was not considered appropriate for them in the context of care. This rate differed by type of comparator [55% for non-head-to-head RCTs vs. 6% for head-to-head RCTs; adjusted odds ratio (aOR), 23.9 (95% CI: 5.5, 92.7)] and duration of trial control treatment [56% for ≤24 weeks and 15% for >24 weeks; aOR, 10.7 (95% CI: 2.8, 63.9)] but not patient RA activity [aOR, 2.5 (95% CI: 1.0, 6.6)]. The limitation of this study was that physicians gave their opinion on fictional patients with only RA.

CONCLUSIONS

Control treatments in RCTs of biologics in RA are often deemed not acceptable in the context of usual care, especially those for non-head-to-head RCTs. These findings raise ethical concerns and challenge the choice of the comparator in RCTs.

Implementation of AMNOG: An industry perspective

In 2010, the Federal Parliament (Bundestag) of Germany passed a new law (Arzneimittelmarktneuordnungsgesetz, AMNOG) on the regulation of medicinal products that applies to all pharmaceutical products with active ingredients that are launched beginning January 1, 2011. The law describes the process to determine the price at which an approved new product will be reimbursed by the statutory health insurance system. The process consists of two phases. The first phase assesses the additional benefit of the new product versus an appropriate comparator (zweckmäßige Vergleichstherapie, zVT). The second phase involves price negotiation. Focusing on the first phase, this paper investigates requirements of benefit assessment of a new product under this law with special attention on the methods applied by the German authorities on issues such as the choice of the comparator, patient relevant endpoints, subgroup analyses, extent of benefit, determination of net benefit, primary and secondary endpoints, and uncertainty of the additional benefit. We propose alternative approaches to address the requirements in some cases and invite other researchers to help develop solutions in other cases.

A framework to assess the value of application of formal criteria to check clinical relevance in RCTs as part of a benefit assessment strategy

Recently, the topic of assessing clinical relevance on top of statistical significance in the analysis of randomized control trials (RCTs) has got increasing attention, in particular as part of benefit assessments. Several formal criteria to serve this purpose have been published. In this paper, we present a framework to assess the value of the application of such criteria. We propose to quantify the need for the assessment of clinical relevance by the actual risk of having accepted a benefit for a treatment with an irrelevant effect in a successful RCT. We then study how this risk can be controlled by two popular criteria based on comparing the effect estimate or the lower bound of the confidence interval with a given threshold. We further propose to quantify the impact of using formal criteria by considering the expected costs when specifying error-specific costs for each of the three possible types of errors: A benefit may be accepted for a treatment, which is actually inferior, or which is not inferior, but only implies an irrelevant improvement, or a benefit may be rejected for a treatment implying a relevant improvement. This way we can demonstrate that the impact depends on parameters which are typically not explicitly defined in the frame of benefit assessments. Depending on the values of these parameters, formal checks of clinical relevance may imply better decisions on average, but they may also imply more harm than good on average.

Methodological approach to determine minor, considerable, and major treatment effects in the early benefit assessment of new drugs

At the beginning of 2011, the early benefit assessment of new drugs was introduced in Germany with the Act on the Reform of the Market for Medicinal Products (AMNOG). The Federal Joint Committee (G‐BA) generally commissions the Institute for Quality and Efficiency in Health Care (IQWiG) with this type of assessment, which examines whether a new drug shows an added benefit (a positive patient‐relevant treatment effect) over the current standard therapy. IQWiG is required to assess the extent of added benefit on the basis of a dossier submitted by the pharmaceutical company responsible. In this context, IQWiG was faced with the task of developing a transparent and plausible approach for operationalizing how to determine the extent of added benefit. In the case of an added benefit, the law specifies three main extent categories (minor, considerable, major). To restrict value judgements to a minimum in the first stage of the assessment process, an explicit and abstract operationalization was needed. The present paper is limited to the situation of binary data (analysis of 2 × 2 tables), using the relative risk as an effect measure. For the treatment effect to be classified as a minor, considerable, or major added benefit, the methodological approach stipulates that the (two‐sided) 95% confidence interval of the effect must exceed a specified distance to the zero effect. In summary, we assume that our approach provides a robust, transparent, and thus predictable foundation to determine minor, considerable, and major treatment effects on binary outcomes in the early benefit assessment of new drugs in Germany. After a decision on the added benefit of a new drug by G‐BA, the classification of added benefit is used to inform pricing negotiations between the umbrella organization of statutory health insurance and the pharmaceutical companies.

A framework to assess the value of subgroup analyses when the overall treatment effect is significant

Although subgroup analysis has been developed and widely used for many years, it is still not clear whether we should perform and how to perform such subgroup analyses when the overall treatment effect is significant. In this paper, we develop a framework to assess and compute the long-term impact of different strategies to perform subgroup analysis. We propose two performance measures: the average gain for patients in the future (E) and the probability of recommending a change to a worse treatment at individual patient level (P). Five families of decision rules are applied under different assumptions for the individual treatment effect (TE) variation. Three distributions reflecting optimistic, moderate, and pessimistic scenarios are assumed for true treatment effects across studies. This framework allows us to compare subgroup analyses decision rules, and we demonstrate through simulation studies that there are decision rules for subgroup analysis which can decrease P and increase E simultaneously compared to the situation of no subgroup analysis. These rules are much more liberal than the usual superiority testing. The latter typically implies a dramatic decrease in E.

The ethical problem of randomization

The Fondazione Umberto Veronesi ethics committee recently published a statement concerning the inherent ethical issues of randomized clinical trials (RCTs), mainly focusing on randomization, raising many questions, and suggesting possible solutions. The main concern is that the patients enrolled in a RCT are used to improve medical knowledge, but they cannot be the beneficiaries of the results of the trials in which they are participating. Possible solutions come from a wider use of clinical and administrative databases, and an early termination of trials. We discuss this statement, emphasizing that the scientific and ethical reason for embarking on a clinical trial is uncertainty. The uncertainty regarding the comparative benefits and harms of each compared treatment (clinical equipoise) warrants equity in allocation. Randomization allows one to obtain unbiased evidence that we cannot know in advance. The expected probability of a new treatment to be successful describes the limits within which a study can be acceptable both from an ethical as well as a scientific point of view. Most people accept enrollment in a RCT if the probability of success of the experimental treatment is between 50 and 70%. The assumption and concern that there is a conflict between "scientific" and "ethical" aspects of a clinical trial due to randomization should at least be mitigated, considering that only scientifically sounded studies can be considered ethical. Randomization remains the appropriate approach to ensure the study's internal validity. Different aspects seem to be more important, from the ethical point of view, considering RCT and their publication.

The move from accuracy studies to randomized trials in PET: current status and future directions

Since the influential study by van Tinteren et al. published in The Lancet in 2002, there have been an increasing number of diagnostic randomized controlled trials (RCTs) investigating the benefit of PET. If they provide valid and useful information on the benefit, these studies can play an important role in informing guideline developers and policy makers. Our aim was to investigate how far the nuclear medicine community has come on its way from accuracy studies to RCTs and which issues we have to take into account in planning future studies.

METHODS

We conducted a systematic review of diagnostic randomized trials, in which PET was applied in only one arm. We covered published studies as well as registered unpublished and planned studies. We considered 3 quality indicators related to the usefulness of a trial to generate evidence for a clinical benefit: use of patient-important outcome, sufficient sample size, and current standard as comparator.

RESULTS

Fourteen published and 15 planned studies were identified. Five of the published studies and 12 of the planned studies did not use a patient-important outcome. Sample sizes were often so small that a significant result could be expected only under the assumption of a substantial reduction in the event rate. Comparators typically reflected the current standard.

CONCLUSION

If we consider the traditional areas of primary diagnosis, staging, and follow-up, then the number and quality of RCTs on PET is currently not sufficient to provide a major source for evidence-based decisions on the clinical benefit of PET. There will also be a future need in these traditional areas to deduce the clinical benefit of PET from the results of accuracy studies. The situation may be more favorable for the areas of treatment planning and response evaluation. Choice of patient-important outcomes and sufficient sample sizes are crucial issues in planning RCTs to demonstrate the clinical benefit of using PET.

The misguided ethics of crossover trials

Crossover is increasingly favored in trials of cancer therapies; even those that seek to establish the basic efficacy of novel drugs. Crossover is done in part for trial recruitment, but also out of a sense of doing the right thing-offering the investigational agent to more patients. In this paper, we argue that this ethical inclination-that crossover is a preferred trial choice-is misguided. In seeking to sate the desires of participants, we might undermine a trial's ability to answer a meaningful clinical question. When a trial is incapable of answering a question, it becomes unethical. Using a crossover strategy in oncology clinical trials can make trials less ethical, not more.

Description of the protocols for randomized controlled trials on cancer drugs conducted in Spain (1999-2003)

OBJECTIVE

To describe the characteristics of randomized controlled clinical trials (RCT) on cancer drugs conducted in Spain between 1999 and 2003 based on their protocols.

METHODS

We conducted an observational retrospective cohort study to identify the protocols of RCTs on cancer drugs authorized by the Agencia Española del Medicamento y Productos Sanitarios (AEMPS) (Spanish Agency for Medicines and Medical Devices) during 1999-2003. A descriptive analysis was completed and the association between variables based on the study setting and sponsorship were assessed.

RESULTS

We identified a total of 303 protocols, which included 176,835 potentially eligible patients. Three-quarter of the studies were internationally-based, 61.7% were phase III, and 76.2% were sponsored by pharmaceutical companies. The most frequently assessed outcomes were response rate (24.7%), overall survival (20.7%), and progression-free survival (14.5%). Of all protocols, 10.6% intended to include more than 1000 patients (mean: 2442, SD: 2724). Compared with their national counterparts, internationally-based studies were significantly larger (p<0.001) and were more likely to implement centralized randomization (p<0.001), blinding of the intervention (p<0.001), and survival as primary outcome (p<0.001). Additionally, most internationally-based studies were sponsored by pharmaceutical companies (p<0.01). In a high percentage of protocols, the available information was not explicit enough to assess the validity of each trial. Compared to other European countries, the proportion of Spanish cancer drugs protocols registered at www.clinicaltrials.gov (7%) was lower.

CONCLUSION

RCTs on cancer drugs conducted in Spain between 1999 and 2003 were more likely to be promoted by pharmaceutical companies rather than by non-profit national groups. The former were more often part of international studies, which generally had better methodological quality than national ones. There are some worldwide on-going initiatives that aim to increase the transparency and quality of future research.

Treatment success in cancer: industry compared to publicly sponsored randomized controlled trials

OBJECTIVE

To assess if commercially sponsored trials are associated with higher success rates than publicly-sponsored trials.

STUDY DESIGN AND SETTINGS

We undertook a systematic review of all consecutive, published and unpublished phase III cancer randomized controlled trials (RCTs) conducted by GlaxoSmithKline (GSK) and the NCIC Clinical Trials Group (CTG). We included all phase III cancer RCTs assessing treatment superiority from 1980 to 2010. Three metrics were assessed to determine treatment successes: (1) the proportion of statistically significant trials favouring the experimental treatment, (2) the proportion of the trials in which new treatments were considered superior according to the investigators, and (3) quantitative synthesis of data for primary outcomes as defined in each trial.

RESULTS

GSK conducted 40 cancer RCTs accruing 19,889 patients and CTG conducted 77 trials enrolling 33,260 patients. 42% (99%CI 24 to 60) of the results were statistically significant favouring experimental treatments in GSK compared to 25% (99%CI 13 to 37) in the CTG cohort (RR = 1.68; p = 0.04). Investigators concluded that new treatments were superior to standard treatments in 80% of GSK compared to 44% of CTG trials (RR = 1.81; p<0.001). Meta-analysis of the primary outcome indicated larger effects in GSK trials (odds ratio = 0.61 [99%CI 0.47-0.78] compared to 0.86 [0.74-1.00]; p = 0.003). However, testing for the effect of treatment over time indicated that treatment success has become comparable in the last decade.

CONCLUSIONS

While overall industry sponsorship is associated with higher success rates than publicly-sponsored trials, the difference seems to have disappeared over time.