Surgery for nonlocalizing hyperparathyroidism in high volume center.

BACKGROUND: Patients with nonlocalizing hyperparathyroidism pose a significant challenge to surgeons when undergoing neck exploration for parathyroidectomy. METHODS: We evaluated 536 patients that had parathyroidectomy for primary hyperparathyroidism (PHPT) from 2005 to 2018 at a single tertiary academic center, and 155 (29%) had standard nonlocalizing preoperative imaging (negative ultrasound and sestamibi scans). RESULTS: There were a total of 102 (66%) non-ectopic single adenomas in the nonlocalizing group and 325 (85%) single adenomas in the localizing group. There was no significant difference (p = 0.09) in adenoma quadrant between localizing and nonlocalizing single adenomas, but the most common location in both groups was right inferior. Patients with nonlocalizing scans were more likely to have double adenomas (21% vs. 9%, p < 0.001), ectopic glands (10% vs. 5%, p = 0.052), and multi-gland disease (13% vs. 8%, p = 0.002). CONCLUSION: Nonlocalizing PHPT patients experienced similar cure and complication rates as localizing PHPT, but required more bilateral explorations and increased operative time.

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.

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.

Progression-free survival is a suboptimal predictor for overall survival among metastatic solid tumour clinical trials.

BACKGROUND: The use of overall survival (OS) as the gold standard primary end-point (PEP) in metastatic oncologic randomised controlled trials (RCTs) has declined in favour of progression-free survival (PFS) without a complete understanding of the degree to which PFS reliably predicts for OS. METHODS: Using ClinicalTrials.gov, we identified 1239 phase III oncologic RCTs, 260 of which were metastatic solid tumour trials with a superiority-design investigating a therapeutic intervention by using either a PFS or OS PEP. Each individual trial was reviewed to quantify RCT design factors and disease-related outcomes. RESULTS: A total of 172,133 patients were enrolled from the year 1999 to 2015 in RCTs that used PFS (56.2%, 146/260) or OS (43.8%, 114/260) as the PEP. PFS trials were more likely to restrict patient eligibility by using molecular criteria (15.1% versus 4.4%, p = 0.005) use targeted therapy (80.1% versus 67.5%, p = 0.048), accrue fewer patients (median 495 versus 619, p = 0.03), and successfully meet the trial PEP (66.9% versus 33.3%, p < 0.0001). On multiple binary logistic regression analysis, factors that predicted for PFS or OS PEP trial success included choice of PFS PEP (p < 0.0001), molecular profile restriction (p = 0.02) and single agent therapy (p = 0.02). Notably, there was only a 38% (31/82) conversion rate of positive PFS-to-OS benefit; lack of industry sponsorship predicted for PFS-to-OS signal conversion (80.0% without industry sponsorship versus 35.1% with industry sponsorship, p = 0.045). CONCLUSIONS: A PFS PEP has suboptimal positive predictive value for OS among phase III metastatic solid tumour RCTs. Regulatory agency decisions should be judicious in using PFS results as the primary basis for approval.

Exclusion of Men from Randomized Phase III Breast Cancer Clinical Trials.

Male breast cancer treatment regimens are often extrapolated from female-based studies because of a paucity of literature analyzing male breast cancer. Using ClinicalTrials.gov, we analyzed breast cancer randomized clinical trials (RCTs) to determine which factors were associated with male-gender inclusion. Of 131 breast cancer RCTs identified, male patients represented 0.087% of the total study population, which is significantly less than the proportion of male patients with breast cancer in the U.S. (0.95%; p < .001). Twenty-seven trials included male patients (20.6%). Lower rates of male inclusion were seen in trials that randomized or mandated hormone therapy as part of the trial protocol compared with trials that did not randomize or mandate endocrine therapy (2.5% vs. 28.6% male inclusion; p < .001). It is imperative for breast cancer clinical trials to include men when allowable in order to improve generalizability and treatment decisions in male patients with breast cancer.

Non-English language validation of patient-reported outcome measures in cancer clinical trials.

Patient-reported outcome measures (PROMs) are increasingly incorporated as endpoints in oncology clinical trials but are often only validated in English. ClinicalTrials.gov was queried for cancer-specific randomized control trials (RCTs) addressing a therapeutic intervention and enrolling primarily in the USA. Peer-reviewed validation of Spanish and Chinese versions of each PROM was assessed. Of 103 eligible trials, a PROM was used as a primary endpoint in 25 RCTs (24.3%) and as a secondary endpoint in 78 RCTs (75.7%). A total of 61 of the 103 eligible trials (59.2%) and 17 of the 25 trials with a PROM primary endpoint (68.0%) used a PROM with either no Spanish or Chinese validation. The absence of validated PROM translations may diminish the voices of non-English language speaking trial participants. With an increasingly diverse US population, validation of non-English PROM translations may decrease disparities in trial participation and improve generalizability of study results.

Sex-Based Disparities Among Cancer Clinical Trial Participants.

Landmark investigation two decades ago demonstrated sex-based disparities among participants in cancer cooperative group trials. Although federal efforts have aimed to improve representation of female patients in government-sponsored research, less is known about sex disparities in the broader landscape of modern oncologic randomized controlled trials. Using ClinicalTrials.gov, we identified randomized controlled trials related to colorectal or lung cancer (the two most common non-sex-specific disease sites). Among the 147 included trials, the proportion of female patients enrolled on trial was on average 6.8% (95% confidence interval = -8.8% to -4.9%) less than the proportion of female patients in the population by disease site (P < .001). Whereas no statistically significant underrepresentation of women was noted within the 26 cooperative group trials, sex disparities were markedly heightened for the 121 noncooperative-group-sponsored trials. Furthermore, underrepresentation of women did not improve with time. Future efforts should therefore focus on addressing these pervasive sex-based enrollment disparities beyond cooperative group trials alone.

Decreasing incidence of upper age restriction enrollment criteria among cancer clinical trials.

BACKGROUND: Age disparities among cancer clinical trial participants are pervasive and worsening over time. Identification of factors associated with age disparities is critical to improve enrollment of older patients on trials. The incidence and impact of trial eligibility criteria that exclude patients on the basis of age remains opaque. METHODS: ClinicalTrials.gov was queried for completed oncologic randomized controlled trials (RCTs). Phase 3 RCTs assessing a therapeutic intervention among adult cancer patients were included. Trial eligibility criteria were assessed using the ClinicalTrials.gov website as well as trial publications and protocol documentation. RESULTS: Seven hundred and forty-two trials met inclusion criteria, with a total combined enrollment of 449,720 patients. Upper age restriction enrollment criteria were identified for 10.1% of RCTs; the median age cutoff for restricted trials was 72 years (interquartile range 70-80 years). Linear regression modeling revealed decreasing incidence of age restriction criteria over time, at a rate of -1.1% annually (p = .03); trials initiating enrollment in 2002-2005, for example, had a 16.1% rate of age-restrictive eligibility criteria, compared with 7.6% for trials initiating enrollment in 2010-2014. CONCLUSION: Use of eligibility criteria that explicitly exclude patients on the basis of age appears to be decreasing with time. Future efforts should aim to better characterize the relationship between eligibility criteria (such as those that exclude patients on the basis of specific organ function) and their association with age disparities among enrolled patients.

The Trials (and Tribulations) of Complementary and Alternative Medicine in Oncology.

Two decades following the creation of the Office of Cancer Complementary and Alternative Medicine at the National Cancer Institute, the status of complementary and alternative medicine (CAM) research within oncology remains opaque. To better understand the landscape of CAM studies in oncology, we identified CAM-related phase III randomized controlled trials (RCTs) through ClinicalTrials.gov and compared these CAM trials to all non-CAM oncologic RCTs. Pearson χ2 testing was used to compare proportions across groups; all tests were two-sided. Comparing the 25 identified CAM RCTs with 739 non-CAM RCTs, CAM studies were more likely to be sponsored by a cooperative group (64.0% vs 28.6%, P < .001) and less likely to be industry funded (8.0% vs 76.5%, P < .001). CAM trials disproportionately excluded disease-related outcomes as endpoints (8.0% vs 84.6%, P < .001), were unsupported by prior early-phase data (55.0% vs 96.1%, P < .001), and did not meet the primary endpoint (8.7% vs 53.0%, P < .001). Given the observed relationship between encouraging pilot data and subsequent phase III trial success, we contend that future CAM RCTs may yield more promising findings if better supported by appropriately designed and well-characterized early-phase signals.

Factors Associated With Age Disparities Among Cancer Clinical Trial Participants.

Importance: Seminal investigation 2 decades ago alerted the oncology community to age disparities in participation in cooperative group trials; less is known about whether these disparities persist in industry-funded research. Objective: To characterize the age disparities among trial enrollees on randomized clinical trials (RCTs) of common cancers in clinical oncology and identify factors associated with wider age imbalances. Data Sources: Phase 3 clinical oncology RCTs were identified through ClinicalTrials.gov. Study Selection: Multiarm RCTs assessing a therapeutic intervention for patients with breast, prostate, colorectal, or lung cancer (the 4 most common cancer disease sites) were included. Data Extraction and Synthesis: Trial data were extracted from ClinicalTrials.gov. Trial screening and parameter identification were independently performed by 2 individuals. Data were analyzed in 2018. Main Outcomes and Measures: The difference in median age (DMA) between the trial participant median age and the population-based disease-site-specific median age was determined for each trial. Results: Three hundred two trials met inclusion criteria. The trials collectively enrolled 262 354 participants; 249 trials (82.5%) were industry-funded. For all trials, the trial median age of trial participants was a mean of 6.49 years younger than the population median age (95% CI, -7.17 to -5.81 years; P < .001). Age disparities were heightened among industry-funded trials compared with non-industry-funded trials (mean DMA, -6.84 vs -4.72 years; P = .002). Enrollment criteria restrictions based on performance status or age cutoffs were associated with age disparities; however, industry-funded trials were not more likely to use these enrollment restrictions than non-industry-funded trials. Age disparities were also larger among trials that evaluated a targeted systemic therapy and among lung cancer trials. Linear regression modeling revealed a widening gap between trial and population median ages over time at a rate of -0.19 years annually (95% CI, -0.37 to -0.01 years; P = .04). Conclusions and Relevance: Age disparities between trial participants and the incident disease population are pervasive across trials and appear to be increasing over time. Industry sponsorship of trials is associated with heightened age imbalances among trial participants. With an increasing role of industry funding among cancer trials, efforts to understand and address age disparities are necessary to ensure generalizability of trial results as well as equity in trial access.