Improving the clinical meaning of surrogate endpoints: An empirical assessment of clinical progression in phase III oncology trials.

Disease progression in clinical trials is commonly defined by radiologic measures. However, clinical progression may be more meaningful to patients, may occur even when radiologic criteria for progression are not met, and often requires a change in therapy in clinical practice. The objective of this study was to determine the utilization of clinical progression criteria within progression-based trial endpoints among phase III trials testing systemic therapies for metastatic solid tumors. The primary manuscripts and protocols of phase III trials were reviewed for whether clinical events, such as refractory pain, tumor bleeding, or neurologic compromise, could constitute a progression event. Univariable logistic regression computed odds ratios (OR) and 95% CI for associations between trial-level covariates and clinical progression. A total of 216 trials enrolling 148,190 patients were included, with publication dates from 2006 through 2020. A major change in clinical status was included in the progression criteria of 13% of trials (n = 27), most commonly as a secondary endpoint (n = 22). Only 59% of trials (n = 16) reported distinct clinical progression outcomes that constituted the composite surrogate endpoint. Compared with other disease sites, genitourinary trials were more likely to include clinical progression definitions (16/33 [48%] vs. 11/183 [6%]; OR, 14.72; 95% CI, 5.99 to 37.84; p < .0001). While major tumor-related clinical events were seldom considered as disease progression events, increased attention to clinical progression may improve the meaningfulness and clinical applicability of surrogate endpoints for patients with metastatic solid tumors.

Lost in the plot: missing visual elements in Kaplan-Meier plots of phase III oncology trials.

Missing visual elements (MVE) in Kaplan-Meier (KM) curves can misrepresent data, preclude curve reconstruction, and hamper transparency. This study evaluated KM plots of phase III oncology trials. MVE were defined as an incomplete y-axis range or missing number at risk table in a KM curve. Surrogate endpoint KM curves were additionally evaluated for complete interpretability, defined by (1) reporting the number of censored patients and (2) correspondence of the disease assessment interval with the number at risk interval. Among 641 trials enrolling 518 235 patients, 116 trials (18%) had MVE in KM curves. Industry sponsorship, larger trials, and more recently published trials were correlated with lower odds of MVE. Only 3% of trials (15 of 574) published surrogate endpoint KM plots with complete interpretability. Improvements in the quality of KM curves of phase III oncology trials, particularly for surrogate endpoints, are needed for greater interpretability, reproducibility, and transparency in oncology research.

Prevalence, trends, and characteristics of trials investigating local therapy in contemporary phase 3 clinical cancer research.

BACKGROUND: Although most patients with cancer are treated with local therapy (LT), the proportion of late-phase clinical trials investigating local therapeutic interventions is unknown. The purpose of this study was to determine the proportion, characteristics, and trends of phase 3 cancer clinical trials assessing the therapeutic value of LT over time. METHODS: This was a cross-sectional analysis of interventional randomized controlled trials in oncology published from 2002 through 2020 and registered on ClinicalTrials.gov. Trends and characteristics of LT trials were compared to all other trials. RESULTS: Of 1877 trials screened, 794 trials enrolling 584,347 patients met inclusion criteria. A total of 27 trials (3%) included a primary randomization assessing LT compared with 767 trials (97%) investigating systemic therapy or supportive care. Annual increase in the number of LT trials (slope [m] = 0.28; 95% confidence interval [CI], 0.15-0.39; p < .001) was outpaced by the increase of trials testing systemic therapy or supportive care (m = 7.57; 95% CI, 6.03-9.11; p < .001). LT trials were more often sponsored by cooperative groups (22 of 27 [81%] vs. 211 of 767 [28%]; p < .001) and less often sponsored by industry (5 of 27 [19%] vs. 609 of 767 [79%]; p < .001). LT trials were more likely to use overall survival as primary end point compared to other trials (13 of 27 [48%] vs. 199 of 767 [26%]; p = .01). CONCLUSIONS: In contemporary late-phase oncology research, LT trials are increasingly under-represented, under-funded, and evaluate more challenging end points compared to other modalities. These findings strongly argue for greater resource allocation and funding mechanisms for LT clinical trials. PLAIN LANGUAGE SUMMARY: Most people who have cancer receive treatments directed at the site of their cancer, such as surgery or radiation. We do not know, however, how many trials test surgery or radiation compared to drug treatments (that go all over the body). We reviewed trials testing the most researched strategies (phase 3) completed between 2002 and 2020. Only 27 trials tested local treatments like surgery or radiation compared to 767 trials testing other treatments. Our study has important implications for funding research and understanding cancer research priorities.

Health-related quality of life in adolescents and young adults with cancer who received radiation therapy: a scoping review.

PURPOSE: Radiation therapy (RT) is a critical component of treatment for adolescents and young adults (AYAs, age 15-39 years old) diagnosed with cancer. Limited prior studies have focused on AYAs receiving RT despite the potentially burdensome effects of RT. We reviewed the literature to assess health-related quality of life (HRQOL) in AYAs with cancer who received RT. METHODS: The MEDLINE, EMBASE, and Web of Science databases were searched in January 2022 to identify studies that analyzed HRQOL measured by patient-reported outcomes in AYAs who received RT. After title (n = 286) and abstract (n = 58) screening and full-text review (n = 19), articles that met eligibility criteria were analyzed. RESULTS: Six studies were analyzed. Two studies included AYAs actively receiving treatment and all included patients in survivorship; time between diagnosis and HRQOL data collection ranged from 3 to > 20 years. Physical and mental health were commonly assessed (6/6 studies) with social health assessed in three studies. AYA-relevant HRQOL needs were rarely assessed: fertility (1/6 studies), financial hardship (1/6), body image (0/6), spirituality (0/6), and sexual health (0/6). No study compared HRQOL between patients actively receiving RT and those post-treatment. None of the studies collected HRQOL data longitudinally. CONCLUSION: HRQOL data in AYAs receiving RT is limited. Future studies examining longitudinal, clinician- vs. patient-reported, and AYA-relevant HRQOL are needed to better understand the unique needs in this population.

Food and Drug Administration approvals in phase 3 Cancer clinical trials.

BACKGROUND: Phase 3 oncologic randomized clinical trials (RCTs) can lead to Food and Drug Administration (FDA) approvals. In this study, we aim to identify trial-related factors associated with trials leading to subsequent FDA drug approvals. METHODS: We performed a database query through the ClinicalTrials.gov registry to search for oncologic phase 3 RCTs on February 2020. We screened all trials for therapeutic, cancer-specific, phase 3, randomized, multi-arm trials. We then identified whether a trial was used for subsequent FDA drug approval through screening of FDA approval announcements. RESULTS: In total, 790 trials were included in our study, with 225 trials (28.4%) generating data that were subsequently used for FDA approvals. Of the 225 FDA approvals identified, 65 (28.9%) were based on trials assessing overall survival (OS) as a primary endpoint (PEP), two (0.9%) were based on trials with a quality of life (QoL) PEP, and 158 approvals (70.2%) were based on trials with other PEP (P = 0.01). FDA approvals were more common among industry funded-trials (219, 97.3%; P < 0.001), and less common among trials sponsored by national cooperative groups (21, 9.3%; P < 0.001). Finally, increased pre-hoc power and meeting patients' accrual target were associated with FDA approvals (P < 0.001). CONCLUSIONS: The majority of FDA approvals are based on data generated from trials analyzing surrogate primary endpoints and trials receiving industry funding. Additional studies are required to understand the complexity of FDA approvals.

Professional Medical Writer Assistance in Oncology Clinical Trials.

BACKGROUND: The use of professional medical writers (PMWs) has been historically low, but contemporary data regarding PMW usage are scarce. In this study, we sought to quantify PMW use in oncologic phase III randomized controlled trials (RCTs). METHODS: We performed a database query through ClinicalTrials.gov to identify cancer-specific phase III RCTs; we then identified whether a PMW was involved in writing the associated trial manuscript reporting primary endpoint results. RESULTS: Two-hundred sixty trials of 600 (43.3%) used a PMW. Industry-funded trials used PMWs more often than nonindustry trials (54.9% vs. 3.0%, p < .001). Increased PMW usage was further noted among trials meeting their primary endpoint (53.4% vs. 32.9%, p < .001) and trials that led to subsequent Food and Drug Administration approval (63.1% vs. 36.3%, p < .001). By treatment interventions, PMW use was highest among systemic therapy trials (50.2%). Lastly, the use of PMWs increased significantly over time (odds ratio: 1.11/year, p = .001). CONCLUSION: PMW use rates are high among industry-funded trials. We urge continued and increased transparency in reporting the funding and use of PMWs.

Performance Status Restriction in Phase III Cancer Clinical Trials.

BACKGROUND: Patients with good performance status (PS) tend to be favored in randomized clinical trials (RCTs), possibly limiting the generalizability of trial findings. We aimed to characterize trial-related factors associated with the use of PS eligibility criteria and analyze patient accrual breakdown by PS. METHODS: Adult, therapeutic, multiarm phase III cancer-specific RCTs were identified through ClinicalTrials.gov. PS data were extracted from articles. Trials with a PS restriction ECOG score ≤1 were identified. Factors associated with PS restriction were determined, and the use of PS restrictions was analyzed over time. RESULTS: In total, 600 trials were included and 238,213 patients had PS data. Of those trials, 527 studies (87.8%) specified a PS restriction cutoff, with 237 (39.5%) having a strict inclusion criterion (ECOG PS ≤1). Enrollment criteria restrictions based on PS (ECOG PS ≤1) were more common among industry-supported trials (P<.001) and lung cancer trials (P<.001). Nearly half of trials that led to FDA approval included strict PS restrictions. Most patients enrolled across all trials had an ECOG PS of 0 to 1 (96.3%). Even among trials that allowed patients with ECOG PS ≥2, only 8.1% of those enrolled had a poor PS. Trials of lung, breast, gastrointestinal, and genitourinary cancers all included <5% of patients with poor PS. Finally, only 4.7% of patients enrolled in trials that led to subsequent FDA approval had poor PS. CONCLUSIONS: Use of PS restrictions in oncologic RCTs is pervasive, and exceedingly few patients with poor PS are enrolled. The selective accrual of healthier patients has the potential to severely limit and bias trial results. Future trials should consider a wider cancer population with close toxicity monitoring to ensure the generalizability of results while maintaining patient safety.

Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.

PURPOSE: The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on P value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics. METHODS: We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database. RESULTS: The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199). CONCLUSION: By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.

Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.

PURPOSE: A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses. METHODS: PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions. RESULTS: Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] v 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; P = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; P = .01). CONCLUSION: Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.

Differential Treatment Effects of Subgroup Analyses in Phase 3 Oncology Trials From 2004 to 2020.

Importance: Subgroup analyses are often performed in oncology to investigate differential treatment effects and may even constitute the basis for regulatory approvals. Current understanding of the features, results, and quality of subgroup analyses is limited. Objective: To evaluate forest plot interpretability and credibility of differential treatment effect claims among oncology trials. Design, Setting, and Participants: This cross-sectional study included randomized phase 3 clinical oncology trials published prior to 2021. Trials were screened from ClinicalTrials.gov. Main Outcomes and Measures: Missing visual elements in forest plots were defined as a missing point estimate or use of a linear x-axis scale for hazard and odds ratios. Multiplicity of testing control was recorded. Differential treatment effect claims were rated using the Instrument for Assessing the Credibility of Effect Modification Analyses. Linear and logistic regressions evaluated associations with outcomes. Results: Among 785 trials, 379 studies (48%) enrolling 331 653 patients reported a subgroup analysis. The forest plots of 43% of trials (156 of 363) were missing visual elements impeding interpretability. While 4148 subgroup effects were evaluated, only 1 trial (0.3%) controlled for multiple testing. On average, trials that did not meet the primary end point conducted 2 more subgroup effect tests compared with trials meeting the primary end point (95% CI, 0.59-3.43 tests; P = .006). A total of 101 differential treatment effects were claimed across 15% of trials (55 of 379). Interaction testing was missing in 53% of trials (29 of 55) claiming differential treatment effects. Trials not meeting the primary end point were associated with greater odds of no interaction testing (odds ratio, 4.47; 95% CI, 1.42-15.55, P = .01). The credibility of differential treatment effect claims was rated as low or very low in 93% of cases (94 of 101). Conclusions and Relevance: In this cross-sectional study of phase 3 oncology trials, nearly half of trials presented a subgroup analysis in their primary publication. However, forest plots of these subgroup analyses largely lacked essential features for interpretation, and most differential treatment effect claims were not supported. Oncology subgroup analyses should be interpreted with caution, and improvements to the quality of subgroup analyses are needed.

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials.

IMPORTANCE: Improving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations. OBJECTIVE: We hypothesized that in silico Bayesian early stopping rules improve the efficiency of phase III trials compared with the original frequentist analysis without compromising overall interpretation. DESIGN: Cross-sectional analysis. SETTING: 230 randomized phase III oncology trials enrolling 184,752 participants. PARTICIPANTS: Individual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves. INTERVENTIONS: Trial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied. MAIN OUTCOMES AND MEASURES: Early stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75. RESULTS: A total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91; P < 0.0001). Trial-level early closure was recommended for 82 trials (36%), including 62 trials (76%) which had performed frequentist interim analysis. Bayesian early stopping rules were 96% sensitive (95% CI, 91% to 98%) for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (Bayesian Cohen's κ, 0.95; 95% CrI, 0.92 to 0.99). However, Bayesian interim analysis was associated with >99.99% posterior probability of reducing patient enrollment requirements ( P < 0.0001), with an estimated cumulative enrollment reduction of 20,543 patients (11%; 89 patients averaged equally over all studied trials) and an estimated cumulative cost savings of 851 million USD (3.7 million USD averaged equally over all studied trials). CONCLUSIONS AND RELEVANCE: Bayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies. KEY POINTS: Question: What are the effects of Bayesian early stopping rules on the efficiency of phase III randomized oncology trials?Findings: Individual-patient level outcomes were reconstructed for 184,752 patients from 230 trials. Compared with the original interim analysis strategy, in silico Bayesian interim analysis reduced patient enrollment requirements and preserved the original trial interpretation. Meaning: Bayesian interim analysis may improve the efficiency of conducting randomized trials, leading to reduced costs, reduced exposure of patients to disadvantageous treatments, and accelerated approval of efficacious therapies.

Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials.

Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.

Off-Protocol Radiation Therapy in Phase 3 Metastatic Solid Tumor Trials.

PURPOSE: Increasing data suggest that radiation therapy, particularly ablative radiation therapy, alters the natural history of metastatic disease. For patients with metastatic disease enrolled in prospective trials testing systemic therapy, the use of off-protocol radiation therapy to improve clinical symptoms or extend the duration of study systemic therapy may influence study endpoints. We sought to evaluate how often off-protocol radiation therapy was permitted among systemic therapy phase 3 trials, how often off-protocol radiation therapy is used, and whether off-protocol radiation therapy correlated with study outcomes. METHODS AND MATERIALS: Two-arm, superiority-design, phase 3 randomized trials testing systemic therapy were screened from ClinicalTrials.gov. Protocol availability was required to assess the trial approach to off-protocol radiation therapy if not described in the manuscript. Adjusted odds ratios with 95% CI were calculated by logistic regression. RESULTS: A total of 112 trials enrolling 80,134 patients were included, with publication dates between 2010 and 2019. Of these, off-protocol radiation therapy was allowed, not discussed, or prohibited during study systemic therapy in 52% (N =58), 25% (N = 28), and 23% (N = 26) of trials, respectively. However, only 2% (2 of 112) of trials reported off-protocol radiation therapy utilization rates, although no data were reported on the use of ablative off-protocol radiation therapy. No trials evaluated or adjusted for the potential influence of off-protocol radiation therapy on study endpoints. Among the subset of open-label studies, trials permissive toward off-protocol radiation therapy were more likely to meet their primary endpoint (adjusted odds ratio, 4.50; 95% CI, 1.23-20.23; P = .04). CONCLUSIONS: Although most trials allowed off-protocol radiation therapy during the receipt of the study systemic therapy, the influence of off-protocol radiation therapy, especially ablative radiation therapy, on study outcomes is underevaluated among phase 3 systemic therapy trials.

Proportional Hazards Violations in Phase 3 Cancer Clinical Trials: A Potential Source of Trial Misinterpretation.

BACKGROUND: Survival analyses of novel agents with long-term responders often exhibit differential hazard rates over time. Such proportional hazards violations (PHVs) may reduce the power of the log-rank test and lead to misinterpretation of trial results. We aimed to characterize the incidence and study attributes associated with PHVs in phase 3 oncology trials and assess the utility of restricted mean survival time (RMST) and MaxCombo as additional analyses. METHODS: Clinicaltrials.gov and PubMed were searched to identify 2-arm, randomized, phase 3 superiority-design cancer trials with time-to-event primary endpoints and published results through 2020. Patient-level data were reconstructed from published Kaplan-Meier curves. PHVs were assessed using Schoenfeld residuals. RESULTS: Three hundred fifty-seven Kaplan-Meier comparisons across 341 trials were analyzed, encompassing 292,831 enrolled patients. PHVs were identified in 85/357 (23.8%; 95%CI 19.7%, 28.5%) comparisons. In multivariable analysis, non-OS endpoints (odds ratio [OR] 2.16 [95%CI 1.21, 3.87]; P=.009) were associated with higher odds of PHVs, and immunotherapy comparisons (OR 1.94 [95%CI 0.98, 3.86]; P=.058) were weakly suggestive of higher odds of PHVs. Few trials with PHVs (25/85, 29.4%) pre-specified a statistical plan to account for PHVs. Fourteen trials with PHVs exhibited discordant statistical signals with RMST or MaxCombo, of which ten (71%) reported negative results. CONCLUSION: PHVs are common across therapy types, and attempts to account for PHVs in statistical design are lacking despite the potential for results exhibiting non-proportional hazards to be misinterpreted.

Secondary Endpoint Utilization and Publication Rate among Phase III Oncology Trials.

Secondary endpoints (SEP) provide crucial information in the interpretation of clinical trials, but their features are not yet well understood. Thus, we sought to empirically characterize the scope and publication rate of SEPs among late-phase oncology trials. We assessed SEPs for each randomized, published phase III oncology trial across all publications and ClinicalTrials.gov, performing logistic regressions to evaluate associations between trial characteristics and SEP publication rates. After screening, a total of 280 trials enrolling 244,576 patients and containing 2,562 SEPs met the inclusion criteria. Only 22% of trials (62/280) listed all SEPs consistently between ClinicalTrials.gov and the trial protocol. The absolute number of SEPs per trial increased over time, and trials sponsored by industry had a greater number of SEPs (median 9 vs. 5 SEPs per trial; P < 0.0001). In total, 69% of SEPs (1,770/2,562) were published. The publication rate significantly varied by SEP category [X2 (5, N = 2,562) = 245.86; P < 0.001]. SEPs that place the most burden on patients, such as patient-reported outcomes and translational correlatives, were published at 63% (246/393) and 44% (39/88), respectively. Trials with more SEPs were associated with lower overall SEP publication rates. Overall, our findings are that SEP publication rates in late-phase oncology trials are highly variable based on the type of SEP. To avoid undue burden on patients and promote transparency of findings, trialists should weigh the biological and clinical relevance of each SEP together with its feasibility at the time of trial design. SIGNIFICANCE: In this investigation, we characterized the utilization and publication rates of SEPs among late-phase oncology trials. Our results draw attention to the proliferation of SEPs in recent years. Although overall publication rates were high, underpublication was detected among endpoints that may increase patient burden (such as translational correlatives and patient-reported outcomes).

Implementation and Efficacy of a Large-Scale Radiation Oncology Case-Based Peer-Review Quality Program across a Multinational Cancer Network.

PURPOSE: With expansion of academic cancer center networks across geographically-dispersed sites, ensuring high-quality delivery of care across all network affiliates is essential. We report on the characteristics and efficacy of a radiation oncology peer-review quality assurance (QA) system implemented across a large-scale multinational cancer network. METHODS AND MATERIALS: Since 2014, weekly case-based peer-review QA meetings have been standard for network radiation oncologists with radiation oncology faculty at a major academic center. This radiotherapy (RT) QA program involves pre-treatment peer-review of cases by disease site, with disease-site subspecialized main campus faculty members. This virtual QA platform involves direct review of the proposed RT plan as well as supporting data, including relevant pathology and imaging studies for each patient. Network RT plans were scored as being concordant or nonconcordant based on national guidelines, institutional recommendations, and/or expert judgment when considering individual patient-specific factors for a given case. Data from January 1, 2014, through December 31, 2019, were aggregated for analysis. RESULTS: Between 2014 and 2019, across 8 network centers, a total of 16,601 RT plans underwent peer-review. The network-based peer-review case volume increased over the study period, from 958 cases in 2014 to 4,487 in 2019. A combined global nonconcordance rate of 4.5% was noted, with the highest nonconcordance rates among head-and-neck cases (11.0%). For centers that joined the network during the study period, we observed a significant decrease in the nonconcordance rate over time (3.1% average annual decrease in nonconcordance, P = 0.01); among centers that joined the network prior to the study period, nonconcordance rates remained stable over time. CONCLUSIONS: Through a standardized QA platform, network-based multinational peer-review of RT plans can be achieved. Improved concordance rates among newly added network affiliates over time are noted, suggesting a positive impact of network membership on the quality of delivered cancer care.

Single-fraction radiation retreatment for bone metastases: role of a rapid access clinic.

OBJECTIVES: This study investigates retreatment rates in single-fraction radiation therapy (SFRT) for painful bone metastasis in patients with limited life expectancy. We compared retreatment-free survival (RFS) in patients from a rapid access bone metastases clinic (RABC) and non-RABC patients, identifying factors associated with retreatment. METHODS: In this observational study, we analysed RABC patients who received SFRT between April 2018 and November 2019, using non-RABC SFRT patients as a comparison group. Patients with prior or perioperative radiation therapy (RT) were excluded. The primary endpoint was same-site and any-site retreatment with RT or surgery. Patient characteristics were compared using χ2 and Student's t-tests, with RFS estimates based on a multistate model considering death as a competing risk using Aalen-Johansen estimates. RESULTS: We identified 151 patients (79 RABC, 72 non-RABC) with 225 treatments (102 RABC, 123 non-RABC) meeting eligibility criteria. Of the 22 (10.8%) same-site retreatments, 5 (22.7%) received surgery, 14 (63.6%) received RT and 3 (13.6%) received both RT and surgery. We found no significant differences in any-site RFS (p=0.97) or same-site RFS (p=0.11). CONCLUSIONS: RFS is high and similar comparable in the RABC and non-RABC cohorts. Retreatment rates are low, even in patients with low Eastern Cooperative Oncology Group scores.

Association of differential censoring with survival and suboptimal control arms among oncology clinical trials.

Differential censoring, which refers to censoring imbalance between treatment arms, may bias the interpretation of survival outcomes in clinical trials. In 146 phase III oncology trials with statistically significant time-to-event surrogate primary endpoints, we evaluated the association between differential censoring in the surrogate primary endpoints, control arm adequacy, and the subsequent statistical significance of overall survival results. Twenty-four (16%) trials exhibited differential censoring that favored the control arm, whereas 15 (10%) exhibited differential censoring that favored the experimental arm. Positive overall survival was more common in control arm differential censoring trials (63%) than in trials without differential censoring (37%) or with experimental arm differential censoring (47%; odds ratio = 2.64, 95% confidence interval = 1.10 to 7.20; P = .04). Control arm differential censoring trials more frequently used suboptimal control arms at 46% compared with 20% without differential censoring and 13% with experimental arm differential censoring (odds ratio = 3.60, 95% confidence interval = 1.29 to 10.0; P = .007). The presence of control arm differential censoring in trials with surrogate primary endpoints, especially in those with overall survival conversion, may indicate an inadequate control arm and should be examined and explained.