MétaCan
Menu
Back to cohort
Record W3092547724 · doi:10.1093/jnci/djaa156

Revisiting Barriers to Clinical Trials Accrual

2020· letter· en· W3092547724 on OpenAlexaff
Michael Brundage

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2020
Typeletter
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccrualMedicineClinical trialIntensive care medicineInternal medicineBusinessAccounting

Abstract

fetched live from OpenAlex

Properly conducted clinical trials provide high-quality evidence for a spectrum of outcomes relevant to patients, care providers, policy makers, and funders of healthcare services. Although such trials often have high internal validity, their external validity, or generalizability, depends on how representative the patients enrolled in a given trial are for the general target population. Only about 5%-8% of cancer patients participate in clinical trials, and studies conducted in the United States indicate that patients who are enrolled in trials are, on average, younger, better educated, less racially diverse, and with fewer comorbid conditions compared with typical patients with the condition of interest. These findings raise concern as to the validity of the trial-estimated treatment benefits and risks when applied in the “real world.” Improving access to trial enrollment and expanding the diversity of patients participating have long been recognized as ways to address threatened generalizability. Improved access could have other benefits such as shortened accrual times or fewer trials closing early. Forty years ago, Penchansky and Thomas (1) provided a framework describing dimensions of access: availability, accessibility, accommodation, affordability, and acceptability. Thirty years ago, Gotay (2) reviewed the available literature on barriers to trial accrual and found that nonparticipation was influenced by trial, physician, and patient-related variables. In 2010, the US National Cancer Institute and American Society for Clinical Oncology cosponsored a symposium to examine the state of science related to barriers to accrual and promoted the development of new interventions to facilitate clinical trial enrollment (3). More recently, Unger et al. (4) reviewed studies of the trial decision-making pathway and estimated that structural and clinical barriers combine to make trial participation unachievable for more than 75% of cancer patients. These reviews, among others, consistently identify both patient decision-making factors (ie, those influencing a patient accepting trial participation) and system or structural or clinician factors (those influencing if a patient has the opportunity to choose) as barriers to accrual. To what extent does each of these domains limit trial participation, and where are the greatest opportunities to overcome barriers? In this issue of the Journal, Unger and colleagues (5) address these issues by posing the question, “What is the rate of trial participation among patients who are actually offered an opportunity to participate?” To seek the answer, they reviewed 35 research studies that individually asked this same question in a variety of clinical contexts, all in the United States. The authors then applied meta-analytical techniques to pool the findings of these individual studies and calculated the best estimate of the proportion of cancer patients who would accept enrollment on a randomized clinical trial if given the opportunity. They found that more than half of the patients in these pooled studies were willing to participate (55.0% among the 30 studies examining participation in clinical trials of cancer treatment and 55.3% in the 5 cancer control trial studies). Willingness to participate was slightly higher overall in patients receiving care in academic centers compared with those in community centers but did not differ statistically significantly between Black, Hispanic, or Asian vs White patient groups in the studies examining these factors. This estimated proportion of more than half of patients willing to participate is in stark contrast to the estimated 5% of patients actually participating in trials. Did the meta-analysis itself have internal validity? The study used appropriate methods that adhered to PRISMA (6) methodologic recommendations, including a comprehensive search strategy for candidate studies, clear inclusion and exclusion criteria, and independent abstractions of study findings. The statistical analyses were appropriately selected and explicitly tested if factors such as a requirement for patient consent (on the enrollment study), community vs academic setting, or patient race and ethnicity group were associated with the estimated proportion of patients willing to participate. Sensitivity analyses were conducted to test the robustness of the pooled findings, and the potential for publication bias was addressed. In sum, the research would appear to be of very high quality, and the authors should be commended on their efforts. Are the study findings generalizable? Unlike most meta-analyses in oncology that provide the best estimate of therapeutic efficacy in a narrow patient population, this analysis estimated an overall patient clinical trial acceptance rate across a broad range of cancer types and clinical trial interventions. The included studies had considerable heterogeneity (hence, a random-effects approach was used). Moreover, although study quality could not be formally assessed, many were retrospectively conducted. It is remarkable, however, that despite these limitations, the variation in patient acceptance rates across studies was fairly narrow (95% confidence interval ranged from 50% to 60%), and the sensitivity analyses showed the estimates to be robust. These findings suggest that across a range of clinical trial contexts, the proportion of patients willing to accept a trial, if offered, is more than half. Could it be higher still? Several reviews have focused on patients’ willingness to participate in trials. The current literature synthesis (5) found that about 1 patient in 4 cited treatment choice–related reasons (eg, a strong treatment preference) and a similar proportion expressed lack of interest, whereas fewer than 1 patient in 12 cited each of treatment toxicity, financial concerns, travel distance, or participation in an experiment to be a reason for declining. Collectively, these reasons account for about 7 out of 10 patients’ preferences, but the heterogeneous nature of patients’ concerns underscores the complexity of the problem and the lack of simple solutions. A focus on modifiable factors, improved understanding of patients’ perspectives, and greater engagement of patient stakeholders in codesigning trials to include appealing elements and outcomes offers a promising pathway to both greater accessibility and higher rates of patient participation once offered (7,8). Importantly, the finding of a 50% acceptance estimate, in the context of an overall trial enrollment rate of 5%, implies that only about 10% of cancer patients are given a trial enrollment opportunity. Even if patient acceptance rates climb higher, participation rates on trials would remain less than 10%. Thus, the system-related and physician-related barriers described in systematic reviews dominate the problem of low participation by limiting access opportunities for patients. These barriers are clearly relevant to the majority of cancer patients. Further, the finding that patient acceptance rates varied little among racial and ethnic groups implies that observed disparities in clinical trial participation arise predominantly from system and/or clinical barriers rather than from patient decision-making. Progress is being made in addressing these barriers such as the US Food and Drug Administration draft guidance documents on broadening trial inclusion criteria, new applications of social media (9), development of clinical trial research networks (10), and greater engagement of patient stakeholders to design trials that matter most to both clinicians and patients (7), among others (3). Changes in practice (such as virtual patient management) owing to COVID-19 considerations may also provide new opportunities to improve access. Novel approaches to improved accessibility continue to be required and should be designed to test hypotheses based on modifiable mechanisms by which common barriers prevent patient access to trials. None. Disclosures: The author has no conflicts of interest to disclose. Not applicable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.319
metaresearch head score (Gemma)0.654
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.681
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3190.654
Meta-epidemiology (narrow)0.0010.004
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.004
Science and technology studies0.0140.030
Scholarly communication0.0290.022
Open science0.0110.017
Research integrity0.1600.137
Insufficient payload (model declined to judge)0.0120.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.824
GPT teacher head0.689
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractno

Explore more

Same venueJNCI Journal of the National Cancer InstituteSame topicEthics in Clinical ResearchFrench-language works237,207