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.

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

Differential censoring (DC), referring to censoring imbalance between treatment arms, may bias the interpretation of survival outcomes in clinical trials. In 146 phase 3 oncology trials with statistically significant time-to-event surrogate primary endpoints (PEPs), we evaluated the association between DC in the surrogate PEP, control arm adequacy, and the subsequent statistical significance of OS results. Twenty-four (16%) trials exhibited DC favoring the control arm (ConDC), while 15 (10%) exhibited experimental arm DC (ExpDC). Positive OS was more common in ConDC trials (63%) than trials without DC (37%) or with ExpDC (47%; odds ratio [OR] 2.64, 95% CI 1.10-7.20; P=.04). ConDC trials more frequently used suboptimal control arms (46%) compared to 20% without DC and 13% with ExpDC (OR 3.60, 95% CI 1.29-10.0; P=.007). The presence of ConDC in trials with surrogate PEPs, especially in those with OS conversion, may indicate an inadequate control arm and should be examined and explained.

Prevalence and implications of significance testing for baseline covariate imbalance in randomised cancer clinical trials: The Table 1 Fallacy.

BACKGROUND: The 'Table 1 Fallacy' refers to the unsound use of significance testing for comparing the distributions of baseline variables between randomised groups to draw erroneous conclusions about balance or imbalance. We performed a cross-sectional study of the Table 1 Fallacy in phase III oncology trials. METHODS: From ClinicalTrials.gov, 1877 randomised trials were screened. Multivariable logistic regressions evaluated predictors of the Table 1 Fallacy. RESULTS: A total of 765 randomised controlled trials involving 553,405 patients were analysed. The Table 1 Fallacy was observed in 25% of trials (188 of 765), with 3% of comparisons deemed significant (59 of 2353), approximating the typical 5% type I error assertion probability. Application of trial-level multiplicity corrections reduced the rate of significant findings to 0.3% (six of 2345 tests). Factors associated with lower odds of the Table 1 Fallacy included industry sponsorship (adjusted odds ratio [aOR] 0.29, 95% confidence interval [CI] 0.18-0.47; multiplicity-corrected P < 0.0001), larger trial size (≥795 versus <280 patients; aOR 0.32, 95% CI 0.19-0.53; multiplicity-corrected P = 0.0008), and publication in a European versus American journal (aOR 0.06, 95% CI 0.03-0.13; multiplicity-corrected P < 0.0001). CONCLUSIONS: This study highlights the persistence of the Table 1 Fallacy in contemporary oncology randomised controlled trials, with one of every four trials testing for baseline differences after randomisation. Significance testing is a suboptimal method for identifying unsound randomisation procedures and may encourage misleading inferences. Journal-level enforcement is a possible strategy to help mitigate this fallacy.

Challenges, Complexities, and Considerations in the Design and Interpretation of Late-Phase Oncology Trials.

Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.

Effectiveness of the nutrition referral system in a multidisciplinary pancreatic cancer clinic.

PURPOSE: Proactive nutrition screening and intervention is associated with improved outcomes for patients with pancreatic adenocarcinoma (PDAC). To better optimize nutrition amongst our PDAC population, we implemented systematic malnutrition screening in the Johns Hopkins pancreas multidisciplinary clinic (PMDC) and assessed the effectiveness of our nutrition referral system. METHODS: This was a single institution prospective study of patients seen in the PMDC, screened for malnutrition using the Malnutrition Screening Tool (MST) (score range=0 to 5, score > 2 indicates risk of malnutrition), and offered referrals to the oncology dietitian. Patients that requested a referral but did not attend a nutrition appointment were contacted by phone to assess barriers to seeing the dietitian. Univariate (UVA) and multivariable (MVA) analyses were carried out to identify predictors of referral status and appointment completion status. RESULTS: A total of 97 patients were included in the study, of which 72 (74.2%) requested a referral and 25 (25.8%) declined. Of the 72 patients who requested a referral, 31 (43.1%) attended an appointment with the oncology dietitian. Data on information session attendance was available for 35 patients, of which 8 (22.9%) attended a pre-clinic information session in which the importance of optimal nutrition was highlighted. On MVA, information session attendance was significantly associated with requesting a referral (OR: 11.1, 95% CI 1.12-1.0E3, p=0.037) and successfully meeting with the oncology dietitian (OR: 5.88, 95% CI 1.00-33.3, p=0.049). CONCLUSION: PMDC teams should institute educational initiatives on the importance of optimal nutrition in order to increase patient engagement with nutrition services.

Incidence of Primary End Point Changes Among Active Cancer Phase 3 Randomized Clinical Trials.

Importance: Primary end point (PEP) changes to an active clinical trial raise questions regarding trial quality and the risk of outcome reporting bias. It is unknown how the frequency and transparency of the reported changes depend on reporting method and whether the PEP changes are associated with trial positivity (ie, the trial met the prespecified statistical threshold for PEP positivity). Objectives: To assess the frequency of reported PEP changes in oncology randomized clinical trials (RCTs) and whether these changes are associated with trial positivity. Design, Setting, and Participants: This cross-sectional study used publicly available data for complete oncology phase 3 RCTs registered in ClinicalTrials.gov from inception through February 2020. Main Outcomes and Measures: The main outcome was change between the initial PEP and the final reported PEP, assessed using 3 methods: (1) history of tracked changes on ClinicalTrials.gov, (2) self-reported changes noted in the article, and (3) changes reported within the protocol, including all available protocol documents. Logistic regression analyses were performed to evaluate whether PEP changes were associated with US Food and Drug Administration approval or trial positivity. Results: Of 755 included trials, 145 (19.2%) had PEP changes found by at least 1 of the 3 detection methods. Of the 145 trials with PEP changes, 102 (70.3%) did not have PEP changes disclosed within the manuscript. There was significant variability in rates of PEP detection by each method (χ2 = 72.1; P < .001). Across all methods, PEP changes were detected at higher rates when multiple versions of the protocol (47 of 148 [31.8%]) were available compared with 1 version (22 of 134 [16.4%]) or no protocol (76 of 473 [16.1%]) (χ2 = 18.7; P < .001). Multivariable analysis demonstrated that PEP changes were associated with trial positivity (odds ratio, 1.86; 95% CI, 1.25-2.82; P = .003). Conclusions and Relevance: This cross-sectional study revealed substantial rates of PEP changes among active RCTs; PEP changes were markedly underreported in published articles and mostly occurred after reported study completion dates. Significant discrepancies in the rate of detected PEP changes call into question the role of increased protocol transparency and completeness in identifying key changes occurring in active trials.

Assessment of Median and Mean Survival Time in Cancer Clinical Trials.

This cohort study assesses the relative stability of median and mean survival time estimates reported in cancer clinical trials.

Patterns and kinetics of hepatocellular carcinoma relapse post-liver transplantation: oligorecurrence and role of local therapies.

Background: Amongst patients with recurrent hepatocellular carcinoma (HCC) post-liver transplantation, systemic therapy options may be limited by immunosuppression or poor performance status. Thus, we aimed to assess the impact of metastasis-directed therapy to all sites of disease (MDT-All) in HCC patients with limited disease recurrence [i.e., oligorecurrence (oligoM1)] post-transplantation and characterize pre-transplant characteristics associated with oligoM1. Methods: In this retrospective cohort study, patients at a single institution with recurrent HCC post-liver transplantation were identified. OligoM1 disease was defined as ≤3 lesions at recurrence, while polyrecurrent (polyM1) disease was defined as >3 lesions. Outcomes were compared in patients with oligoM1 disease by receipt of MDT-All. Regression analyses were used to identify predictors of polyM1 disease and characteristics associated with post-recurrence outcomes. Results: Forty-three patients with recurrent HCC post-liver transplantation from 2005-2022 were identified. Twenty-seven (63%) patients had oligoM1. Microvascular invasion was independently associated with polyM1 [odds ratio (OR): 14.64; 95% confidence interval (CI): 1.48-144.77; P=0.022]. Elevated alpha-fetoprotein (AFP) ≥400 ng/mL [hazard ratio (HR): 2.44; 95% CI: 1.08, 5.52; P=0.033] at recurrence was independently associated with inferior overall survival (OS), while oligoM1 (HR: 0.42; 95% CI: 0.21, 0.87; P=0.018) was independently associated with favorable OS. Amongst patients with oligoM1 who received MDT-All (n=15) median OS was 38.4 vs. 16.1 months for those who did not receive MDT-All (log-rank P=0.021). There was a non-significant improvement in polyprogression-free survival (polyPFS) (median 14.0 vs. 10.7 months, P=0.1) amongst oligoM1 patients who received MDT-All compared to those who did not. Conclusions: Receipt of MDT-All was associated with improved OS amongst patients with limited HCC disease recurrence following liver transplantation.

Association between Prior Malignancy Exclusion Criteria and Age Disparities in Cancer Clinical Trials.

Prior malignancy exclusion criteria (PMEC) are often utilized in cancer clinical trials; however, the incidence of PMEC and the association of PMEC with trial participant age disparities remain poorly understood. This study aimed to identify age disparities in oncologic randomized clinical trials as a result of PMEC. Using a comprehensive collection of modern phase III cancer clinical trials obtained via ClinicalTrials.gov, we assessed the incidence and covariates associated with trials excluding patients with prior cancers within 5+ years from registration (PMEC-5). Using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database, we further sought to determine the correlation between PMEC-5 and age disparities. PMEC-5 were used in 41% of all trials, with higher PMEC-5 utilization among industry-supported trials as well as trials evaluating a targeted therapy. Comparing trial patient median ages with population-matched median ages by disease site and time-period, we assessed the association between PMEC-5 and age disparities among trial participants. PMEC-5 were independently associated with heightened age disparities, which further worsened with longer exclusionary timeframes. Together, PMEC likely contribute to age disparities, suggesting that eligibility criteria modernization through narrower PMEC timeframes may work toward reducing such disparities in cancer clinical trial enrollment.

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.

Detecting the Dark Matter of Unpublished Clinical Cancer Studies: An Analysis of Phase 3 Randomized Controlled Trials.

Unpublished randomized controlled trial (RCT) frequency, correlates, and financial impact are not well understood. We sought to characterize the nonpublication of peer-reviewed manuscripts among interventional, therapeutic, multi-arm, phase 3 oncology RCTs. Trials were identified by searching ClinicalTrials.gov, while publications and abstracts were identified through PubMed and Google Scholar. Trial data were extracted from ClinicalTrials.gov and individual publications. Publication was defined as a peer-reviewed manuscript addressing the primary endpoint. Patient accrual cost was extrapolated from experimental data; investigators/sponsors were contacted to determine nonpublication reasons. Six hundred eighty-four completed RCTs met inclusion criteria, which accrued 434,610 patients from 1994 to 2015; 638 were published (93.3%) and 46 were unpublished (6.7%). Among the unpublished trials, the time difference from primary endpoint maturity to data abstraction was a median of 6 years (interquartile range, 4 to 8 years). On multiple binary logistic regression analysis, factors associated with unpublished trials included lack of cooperative group sponsorship (odds ratio, 5.91, 95% CI, 1.35 to 25.97; P=.019) and supportive care investigation (odds ratio, 2.90; 95% CI, 1.13 to 7.41; P=.027). The estimated inflation-adjusted average cost of patient accrual for all unpublished trials was $113,937,849 (range, $41,136,883 to $320,201,063). Direct contact with sponsors/investigators led to a 50.0% response rate (n=23 of 46); manuscript in preparation and/or in submission (n=10 of 23) was the most commonly cited reason for nonpublication. In conclusion, approximately 1 in 15 clinical oncology RCTs are unpublished and this has a profound impact on the research enterprise. The cooperative group infrastructure may serve as a blueprint to reduce nonpublication.

Racial and Ethnic Disparities Among Participants in US-Based Phase 3 Randomized Cancer Clinical Trials.

Although improving representation of racial and ethnic groups in United States clinical trials has been a focus of federal initiatives for nearly 3 decades, the status of racial and ethnic minority enrollment on cancer trials is largely unknown. We used a broad collection of phase 3 cancer trials derived from ClinicalTrials.gov to evaluate racial and ethnic enrollment among US cancer trials. The difference in incidence by race and ethnicity was the median absolute difference between trial and corresponding Surveillance, Epidemiology, and End Results data. All statistical tests were 2-sided. Using a cohort of 168 eligible trials, median difference in incidence by race and ethnicity was +6.8% for Whites (interquartile range [IQR] = +1.8% to +10.1%; P < .001 by Wilcoxon signed-rank test comparing median difference in incidence by race and ethnicity to a value of 0), -2.6% for Blacks (IQR = -5.1% to +1.2%; P = .004), -4.7% for Hispanics (IQR = -7.5% to -0.3%; P < .001), and -4.7% for Asians (IQR = -5.7% to -3.3%; P < .001). These data demonstrate overrepresentation of Whites, with continued underrepresentation of racial and ethnic minority subgroups.

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.

Tobacco exposure as a major modifier of oncologic outcomes in human papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma.

BACKGROUND: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) in the US is rapidly increasing, driven largely by the epidemic of human papillomavirus (HPV)-mediated OPSCC. Although survival for patients with HPV mediated OPSCC (HPV+ OPSCC) is generally better than that of patients with non-virally mediated OPSCC, this effect is not uniform. We hypothesized that tobacco exposure remains a critical modifier of survival for HPV+ OPSCC patients. METHODS: We conducted a retrospective analysis of 611 OPSCC patients with concordant p16 and HPV testing treated at a single institute (2002-2013). Survival analysis was performed using Kaplan-Meier analysis and Cox regression. Recursive partitioning analysis (RPA) was used to define tobacco exposure associated with survival (p < 0.05). RESULTS: Tobacco exposure impacted overall survival (OS) for HPV+ patients on univariate and multivariate analysis (p = 0.002, p = 0.003 respectively). RPA identified 30 pack-years (PY) as a threshold at which survival became significantly worse in HPV+ patients. OS and disease-free survival (DFS) for HPV+ > 30 PY patients didn't differ significantly from HPV- patients (p = 0.72, p = 0.27, respectively). HPV+ > 30 PY patients had substantially lower 5-year OS when compared to their ≤30 PYs counterparts: 78.4% vs 91.6%; p = 0.03, 76% vs 88.3%; p = 0.07, and 52.3% vs 74%; p = 0.05, for stages I, II, and III (AJCC 8th Edition Manual), respectively. CONCLUSIONS: Tobacco exposure can eliminate the survival benefit associated with HPV+ status in OPSCC patients. Until this effect can be clearly quantified using prospective datasets, de-escalation of treatment for HPV + OPSCC smokers should be avoided.

Trial Sponsorship and Time to Reporting for Phase 3 Randomized Cancer Clinical Trials.

The pace of clinical trial data generation and publication is an area of interest within clinical oncology; however, little is known about the dynamics and covariates of time to reporting (TTR) of trial results. To assess these, ClinicalTrials.gov was queried for phase three clinical trials for patients with metastatic solid tumors, and the factors associated with TTR from enrollment completion to publication were analyzed. Based on the 319 included trials, cooperative-group-sponsored trials were reported at a slower rate than non-cooperative-group trials (median 37.5 vs. 31.0 months; p < 0.001), while industry-funded studies were reported at a faster rate than non-industry-supported trials (31.0 vs. 40.0 months; p = 0.005). Furthermore, successful trials (those meeting their primary endpoint) were reported at a faster rate than unsuccessful studies (27.5 vs. 36.0 months; p < 0.001). Multivariable analysis confirmed that industry funding was independently associated with a shorter TTR (p = 0.006), while cooperative group sponsorship was not associated with a statistically significant difference in TTR (p = 0.18). These data underscore an opportunity to improve cooperative group trial efficiency by reducing TTR.

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.

Relationship between treatment center case volume and survival for localized Ewing sarcoma: The role of radiotherapy timing.

In the treatment of localized Ewing sarcoma (EWS), delays in local therapy are known to adversely impact overall survival (OS). However, the role of treatment center volume in EWS outcomes, and the interaction between center volume and local therapy timing with definitive radiotherapy, remains unknown. Using the National Cancer Database, we demonstrate that treatment at the lowest EWS volume centers is associated with reduced OS, explained partly by higher rates of delayed local therapy. Treatment at the highest volume centers results in improved OS, but appears independent of radiotherapy timing. Future efforts to improve care for EWS patients across treatment centers are imperative.