Mapping Strategies for Reaching Socioeconomically Disadvantaged Populations in Clinical Trials.

Importance: Socioeconomically disadvantaged patients, such as persons with low income and those with low educational attainment, are less likely to participate in clinical trials than those with higher earnings and higher educational attainment, despite the former being more likely to have chronic medical conditions. Ways to improve the representation of socioeconomically disadvantaged patients in clinical trials deserve attention. Objective: To examine whether current recruitment and enrollment strategies used by US clinical research sites appropriately include patients from socioeconomically disadvantaged backgrounds. Design, Setting, and Participants: This survey study was conducted between April and July 2023. An online survey was distributed among US clinical research sites to explore their use of these strategies and the types of patient sociodemographic and socioeconomic data they collect. The survey was distributed by 13 pharmaceutical companies and 1 clinical research organization. Eight targeted strategies known to increase the recruitment and retention of socioeconomically disadvantaged participants as well as 6 general strategies to recruit and retain clinical trial participants were identified. Data analysis was performed between August and September 2023. Main Outcomes and Measures: Proportions of for-profit vs nonprofit or governmental sites that use recruitment and retention strategies, proportions that have partnerships with community organizations that target socioeconomically disadvantaged groups, and the distribution of sociodemographic and socioeconomic data collected by sites about their patients. A χ2 test of independence was performed to assess the association between research site ownership type and levels of adoption of strategies. Results: A total of 492 responses were collected from 381 clinical research sites in the US (219 for-profit sites [57.5%] and 162 nonprofit or governmental sites [42.5%]). Overall, compared with nonprofit or governmental sites, for-profit sites reported higher use of strategies shown to increase the recruitment and retention of socioeconomically disadvantaged populations, including always or often providing after-hours visits (84 of 173 for-profit sites [48.6%]; 22 of 123 nonprofit or governmental sites [17.9%]) and offering financial compensation (135 of 162 for-profit sites [83.3%]; 60 of 123 nonprofit or governmental sites [48.8%]). Additionally, there was an association between research site ownership type and levels of adoption of these strategies; for example, for-profit sites were more likely to provide after-hours visits (χ2 = 30.33; P < .001) and offer financial compensation (χ2 = 49.35; P < .001). Only 7.2% of for-profit sites (12 of 167) and 13.0% of nonprofit or governmental sites (16 of 123) collected information on the patient's annual income. Conclusions and Relevance: In this survey study, we found an association between a clinical research site's ownership type (for-profit vs nonprofit or governmental) and how often it used strategies to engage socioeconomically diverse populations in clinical research. Regardless of ownership type, most clinical research sites did not collect socioeconomic information from patients. Adoption of strategies to engage socioeconomically diverse populations, particularly by nonprofit or governmental sites, may help minimize barriers to participation for socioeconomically disadvantaged patients.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Enrollment on upfront high-risk neuroblastoma trials by race, ethnicity, and poverty status: A report from the Children's Oncology Group.

It is not clear whether trial access disparities exist in the Children's Oncology Group (COG). Here, we leverage a cohort of children with high-risk neuroblastoma (HR-NBL) enrolled on the COG ANBL00B1 neuroblastoma biology study to examine subsequent enrollment to upfront COG therapeutic trials by race, ethnicity, and proxied poverty status. Among 1917 children with HR-NBL enrolled on ANBL00B1, 696 (36.3%) subsequently enrolled on an upfront therapeutic trial with no difference by race, ethnicity, or proxied poverty status. In neuroblastoma, trial access disparities are not comparable to adult oncology, and efforts to advance equity should prioritize other mechanisms of survival disparities.

Publication rates of registered strabismus trials from ClinicalTrials.gov.

Presently, little is known regarding the characteristics and publication rates of registered strabismus trials from ClinicalTrials.gov. We queried registered strabismus trials that were completed prior to January 1, 2021, from ClinicalTrials.gov. Publication of trials in peer-reviewed journals was confirmed using PubMed.gov, ClinicalTrials.gov, and Google Scholar. Of the 117 trials found, only 69 (59%) were published with a publication delay of nearly 2.5 years. Interventional trials were associated with publication status compared with observational trials. The low publication rates and significant publication delay indicate potential bias in information dissemination of completed strabismus trials.

Isotonic design for single-arm biomarker stratified trials.

In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.

Toward Equitable Precision Oncology: Monitoring Racial and Ethnic Inclusion in Genomics and Clinical Trials.

PURPOSE: Ethnic diversity in cancer research is crucial as race/ethnicity influences cancer incidence, survival, drug response, molecular pathways, and epigenetic phenomena. In 2018, we began a project to examine racial/ethnic diversity in cancer research, with a commitment to review these disparities every 4 years. This report is our second assessment, detailing the present state of racial/ethnic diversity in cancer genomics and clinical trials. METHODS: To study racial/ethnic inclusion in cancer genomics, we extracted ethnic records from all data sets available at cBioPortal (n = 125,128 patients) and cancer-related genome-wide association studies (n = 28,011,282 patients) between 2018 and 2022. Concerning clinical trials, we selected studies related to breast cancer (n = 125,518 patients, 181 studies), lung cancer (n = 34,329 patients, 119 studies), and colorectal cancer (n = 40,808 patients, 105 studies). RESULTS: In cancer genomics (N = 28,136,410), 3% of individuals lack racial/ethnic registries; tumor samples were collected predominantly from White patients (89.14%), followed by Asian (7%), African American (0.55%), and Hispanic (0.21%) patients and other populations (0.1%). In clinical trials (N = 200,655), data on race/ethnicity are missing for 60.14% of the participants; for individuals whose race/ethnicity was recorded, most were characterized as White (28.33%), followed by Asian (7.64%), African (1.79), other ethnicities (1.37), and Hispanic (0.73). Racial/ethnic representation significantly deviates from global ethnic proportions (P ≤ .001) across all data sets, with White patients outnumbering other ethnic groups by a factor of approximately 4-6. CONCLUSION: Our second update on racial/ethnic representation in cancer research highlights the persistent overrepresentation of White populations in cancer genomics and a notable absence of racial/ethnic information across clinical trials. To ensure more equitable and effective precision oncology, future efforts should address the reasons behind the insufficient representation of ethnically diverse populations in cancer research.

Comparison of revision rates and epidemiological data of a single total knee arthroplasty system of different designs (cruciate retaining, posterior stabilized, mobile bearing, and fixed bearing): a meta-analysis and systematic review of clinical trials and national arthroplasty registries.

BACKGROUND: This study aimed to meta-analyze epidemiological data, revision rates, and incidences of different designs of a single Total Knee Arthroplasty System and compare these factors across different countries. METHODS: A systematic review was conducted on clinical studies and arthroplasty registries of ATTUNE TKA from 1999 to 2020. The main endpoints analyzed were revision rates and epidemiological data. RESULTS: The average age of patients was 67.8 years, with a gender distribution of 60% female and 40% male. The pooled average BMI was 29.4 kg/m2. Eight clinical studies showed a pooled revision rate per 100 observed CY of 0.5 (n = 1343 cases). Cumulative revision rates after 1, 3, and 5 years varied among registries, with the Swiss registry having the highest revision data (after 5 years: 6.3%) and the American registry having the lowest revision data (after 5 years: 1.7%). A comparison of the revision rates of mobile bearing and fixed bearing (41,200 cases) as well as cruciate retaining and posterior stabilized (n = 123,361 cases) showed no significant advantage in the first 5 years after implantation. CONCLUSION: In conclusion, pooled data from 41,200 cases of TKA with a single Total Knee Arthroplasty System in two arthroplasty registries revealed that there was no significant difference in revision rates between the mobile bearing and fixed bearing design within the first 5 years after implantation. In addition, a comparison of the revision rates in n = 123,361 cases showed no significant advantage for cruciate retaining or posterior stabilized in the first 5 years after implantation.

Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Reporting of interventional clinical trial results in an academic center: a survey of completed studies.

BACKGROUND: The dissemination of clinical trial results is an important scientific and ethical endeavour. This survey of completed interventional studies in a French academic center describes their reporting status. METHODS: We explored all interventional studies sponsored by Rennes University Hospital identified on the French Open Science Monitor which tracks trials registered on EUCTR or clinicaltrials.gov, and provides an automatic assessment of the reporting of results. For each study, we ascertained the actual reporting of results using systematic searches on the hospital internal database, bibliographic databases (Google Scholar, PubMed), and by contacting all principal investigators (PIs). We describe several features (including total budget and numbers of trial participants) of the studies that did not report any results. RESULTS: The French Open Science Monitor identified 93 interventional studies, among which 10 (11%) reported results. In contrast, our survey identified 36 studies (39%) reporting primary analysis results and an additional 18 (19%) reporting results for secondary analyses (without results for their primary analysis). The overall budget for studies that did not report any results was estimated to be €5,051,253 for a total of 6,735 trial participants. The most frequent reasons for the absence of results reported by PIs were lack of time for 18 (42%), and logistic difficulties (e.g. delay in obtaining results or another blocking factor) for 12 (28%). An association was found between non-publication and negative results (adjusted Odds Ratio = 4.70, 95% Confidence Interval [1.67;14.11]). CONCLUSIONS: Even allowing for the fact that automatic searches underestimate the number of studies with published results, the level of reporting was disappointingly low. This amounts to a waste of trial participants' implication and money. Corrective actions are needed. TRIAL REGISTRATION: https://osf.io/q5hcs.

Distributional imputation for the analysis of censored recurrent events.

Longitudinal clinical trials for which recurrent events endpoints are of interest are commonly subject to missing event data. Primary analyses in such trials are often performed assuming events are missing at random, and sensitivity analyses are necessary to assess robustness of primary analysis conclusions to missing data assumptions. Control-based imputation is an attractive approach in superiority trials for imposing conservative assumptions on how data may be missing not at random. A popular approach to implementing control-based assumptions for recurrent events is multiple imputation (MI), but Rubin's variance estimator is often biased for the true sampling variability of the point estimator in the control-based setting. We propose distributional imputation (DI) with corresponding wild bootstrap variance estimation procedure for control-based sensitivity analyses of recurrent events. We apply control-based DI to a type I diabetes trial. In the application and simulation studies, DI produced more reasonable standard error estimates than MI with Rubin's combining rules in control-based sensitivity analyses of recurrent events.

Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Diversity of US participants in AstraZeneca-sponsored clinical trials.

BACKGROUND: To develop medicines that are safe and efficacious to all patients, clinical trials must enroll appropriate target populations, but imbalances related to race, ethnicity and sex have been reported. A comprehensive analysis and improvement in understanding representativeness of patient enrollment in industry-sponsored trials are key public health needs. METHODS: We assessed race/ethnicity and sex representation in AstraZeneca (AZ)-sponsored clinical trials in the United States (US) from 2010 to 2022, compared with the 2019 US Census. RESULTS: In total, 246 trials representing 95,372 patients with complete race/ethnicity and sex records were analyzed. The proportions of different race/ethnicity subgroups in AZ-sponsored clinical trials and the US Census were similar (White: 69.5% vs 60.1%, Black or African American: 13.3% vs 12.5%, Asian: 1.8% vs 5.8%, Hispanic: 14.4% vs 18.5%). We also observed parity in the proportions of males and females between AZ clinical trials and US Census (males: 52.4% vs 49.2%, females: 47.6% vs 50.8%). Comparisons of four distinct therapy areas within AZ (Respiratory and Immunology [R&I]; Cardiovascular, Renal, and Metabolism [CVRM]; Solid Tumors; and Hematological Malignancies), including by trial phases, revealed greater variability, with proportions observed above and below US Census levels. CONCLUSION: This analysis provides the first detailed insights into the representativeness of AZ trials. Overall, the proportions of different race/ethnicity and sex subgroups in AZ-sponsored clinical trials were broadly aligned with the US Census. We outline some of AZ's planned health equity initiatives that are intended to continue to improve equitable patient enrollment.

Analysis and Optimization of Equitable US Cancer Clinical Trial Center Access by Travel Time.

Importance: Racially minoritized and socioeconomically disadvantaged populations are currently underrepresented in clinical trials. Data-driven, quantitative analyses and strategies are required to help address this inequity. Objective: To systematically analyze the geographical distribution of self-identified racial and socioeconomic demographics within commuting distance to cancer clinical trial centers and other hospitals in the US. Design, Setting, and Participants: This longitudinal quantitative study used data from the US Census 2020 Decennial and American community survey (which collects data from all US residents), OpenStreetMap, National Cancer Institute-designated Cancer Centers list, Nature Index of Cancer Research Health Institutions, National Trial registry, and National Homeland Infrastructure Foundation-Level Data. Statistical analyses were performed on data collected between 2006 and 2020. Main Outcomes and Measures: Population distributions of socioeconomic deprivation indices and self-identified race within 30-, 60-, and 120-minute 1-way driving commute times from US cancer trial sites. Map overlay of high deprivation index and high diversity areas with existing hospitals, existing major cancer trial centers, and commuting distance to the closest cancer trial center. Results: The 78 major US cancer trial centers that are involved in 94% of all US cancer trials and included in this study were found to be located in areas with socioeconomically more affluent populations with higher proportions of self-identified White individuals (+10.1% unpaired mean difference; 95% CI, +6.8% to +13.7%) compared with the national average. The top 10th percentile of all US hospitals has catchment populations with a range of absolute sum difference from 2.4% to 35% from one-third each of Asian/multiracial/other (Asian alone, American Indian or Alaska Native alone, Native Hawaiian or Other Pacific Islander alone, some other race alone, population of 2 or more races), Black or African American, and White populations. Currently available data are sufficient to identify diverse census tracks within preset commuting times (30, 60, or 120 minutes) from all hospitals in the US (N = 7623). Maps are presented for each US city above 500 000 inhabitants, which display all prospective hospitals and major cancer trial sites within commutable distance to racially diverse and socioeconomically disadvantaged populations. Conclusion and Relevance: This study identified biases in the sociodemographics of populations living within commuting distance to US-based cancer trial sites and enables the determination of more equitably commutable prospective satellite hospital sites that could be mobilized for enhanced racial and socioeconomic representation in clinical trials. The maps generated in this work may inform the design of future clinical trials or investigations in enrollment and retention strategies for clinical trials; however, other recruitment barriers still need to be addressed to ensure racial and socioeconomic demographics within the geographical vicinity of a clinical site can translate to equitable trial participant representation.

Are orthopedic clinical trials representative? An analysis of race and ethnicity reported in clinical trials between 2007 and 2022 : Running title: representation of clinical trials in orthopedic surgery.

INTRODUCTION: Prior studies investigating the racial and ethnic representation of orthopedic trial participants have found low rates of reporting, but these studies are dated due to the passing of the National Institutes of Health Final Rule in 2017 requiring the reporting of racial and ethnic data among clinical trials. Therefore, we evaluated the representativeness of orthopedic clinical trials before and after the Final Rule. METHODS: A cross-sectional survey of orthopaedic clinical trials registered at ClinicalTrials.gov between October 1, 2007 and May 20, 2023 was conducted. After identifying and screening 23,752 clinical trials, 1564 trials were included in the analysis. Trials started before the implementation of the Final Rule on January 18, 2017 were grouped and compared to trials that began after. Odds ratios (OR) were utilized to identify trial characteristics associated with reporting race/ethnicity data. One-proportion z tests compared the representation of each racial and ethnic category to the 2020 United States Census. RESULTS: In total, 34% (544 of 1564) of orthopedic clinical trials evaluated reported the race of participants, while 28% (438 of 1564) reported ethnicity. Trials registered after the Final Rule were more likely to report racial (OR: 5.15, 95%CI: 3.72-7.13, p < 0.001) and ethnic (OR: 3.23, 95%CI: 2.41-4.33, p < 0.001) representation of participants. Compared with the distribution of race and ethnicity reported by the United States 2020 Census, orthopedic trials had 16.6% more White participants (95% CI 16.4%, 16.8%; p < 0.001), 3.2% fewer Black participants (95%CI 3.1%, 3.3%; p < 0.001), and 5.7% fewer Hispanic/Latino participants (95%CI 5.2%, 6.2%; p < 0.001). Trials with enrollment sizes over 100 participants were also more likely to report race and ethnicity, with odds increasing with increased sample size. CONCLUSIONS: The Final Rule marginally improved the reporting of race and ethnicity in orthopedic clinical trials, and underrepresentation of Black or African American, Multiracial, and Hispanic populations persists. LEVEL OF EVIDENCE: III.

Delayed publication of clinical trials in gynecologic oncology.

OBJECTIVES: Delays in clinical trial publication can hinder timely implementation of evidence-based practices. We sought to determine publication rates and time to publication for clinical trials addressing gynecologic malignancies. METHODS: All clinical trials addressing gynecologic cancers in the ClinicalTrials.gov registry with a primary completion date between 1/1/2018 and 1/1/2020 were identified. The primary outcome was publication rate. All included studies had been completed for at least 3 years. Secondary outcomes were time to publication and associations between publication rate and sponsor, cancer type, and the number and location of primary study sites. RESULTS: Of the 290 trials included, 161 (55.5%) had a peer-reviewed publication for the primary outcome within at least 3 years after completion. Of these, 123 had positive results (76.4%) and 38 were negative (23.6%). The average duration from primary completion to manuscript publication was 23.6 months (SD 13.9; median 21.4, IQR 15.1-32.4). Only 73 had results posted on the ClinicalTrials.gov registry (25.2%). Studies with positive findings had a significantly faster time to publication than those with negative results (22.0 mo vs 29.0 mo, p = 0.009). There was no significant difference between publication rate and funding source, cancer type, or location and number of primary sites. CONCLUSIONS: Timely publication of clinical trials addressing gynecologic cancers remains an issue. Studies with positive findings were published faster than those with negative results, but the average publication time was still almost 2 years from trial completion. Further efforts should be made to identify and address barriers to clinical trial publication.

An evolutionary algorithm for the direct optimization of covariate balance between nonrandomized populations.

Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in estimating the average treatment effect on the treated. In practice, misspecification of the PS model leads to deviations from theory and matching quality is ultimately judged by the observed post-matching balance in baseline covariates. Since covariate balance is the ultimate arbiter of successful matching, we argue for an approach to matching in which the success criterion is explicitly specified and describe an evolutionary algorithm to directly optimize an arbitrary metric of covariate balance. We demonstrate the performance of the proposed method using a simulated dataset of 275,000 patients and 10 matching covariates. We further apply the method to match 250 patients from a recently completed clinical trial to a pool of more than 160,000 patients identified from electronic health records on 101 covariates. In all cases, we find that the proposed method outperforms PS matching as measured by the specified balance criterion. We additionally find that the evolutionary approach can perform comparably to another popular direct optimization technique based on linear integer programming, while having the additional advantage of supporting arbitrary balance metrics. We demonstrate how the chosen balance metric impacts the statistical properties of the resulting matched populations, emphasizing the potential impact of using nonlinear balance functions in constructing an external control arm. We release our implementation of the considered algorithms in Python.

Cautionary note on regional consistency evaluation in multiregional clinical trials with binary outcomes.

Multiregional clinical trials (MRCTs) have become increasingly common during the development of new drugs to obtain simultaneous drug approvals worldwide. When planning MRCTs, a major statistical challenge is determination of the regional sample size. In general, the regional sample size must be determined as the sample size such that the regional consistency probability, defined as the probability of meeting the regional consistency criterion, is greater than a prespecified value. The Japanese Ministry of Health, Labour and Welfare proposed two criteria for regional consistency. Moreover, many researchers have proposed corresponding closed-form formulas for calculating regional consistency probabilities when the primary outcome is continuous. Although some researchers have argued that those formulas are also applicable to cases with binary outcomes, it remains questionable whether such an argument can be true. Based on simulation results, we demonstrate that the existing formulas are inappropriate for binary cases, even when the regional sample size is sufficiently large. To address this issue, we develop alternative formulas and use simulation to show that they provide accurate regional consistency probabilities. Furthermore, we present an application of our proposed formulas for an MRCT of advanced or metastatic clear-cell renal cell carcinoma.

Comparison of nonparametric estimators of the expected number of recurrent events.

We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing risk, or gaps between at risk periods complicate the estimation. We use survival multistate models to represent different complex recurrent event situations, profiting from recent advances in nonparametric estimation for non-Markov multistate models, and explain several estimators by using multistate intensity processes, including the common Nelson-Aalen-type estimators with and without competing mortality. In addition to building on estimation of state occupation probabilities in non-Markov models, we consider a simple extension of the Nelson-Aalen estimator by allowing for dependence on the number of prior recurrent events. We pay particular attention to the assumptions required for the censoring mechanism, one issue being that some settings require the censoring process to be entirely unrelated while others allow for state-dependent or event-driven censoring. We conducted extensive simulation studies to compare the estimators in various complex situations with recurrent events. Our practical example deals with recurrent chronic obstructive pulmonary disease exacerbations in a clinical study, which will also be used to illustrate two-sample-inference using resampling.