Engagement in cancer clinical trials among a nationally representative cancer survivor sample: Motivators,barriers and opportunities for improvement

Abstract

Purpose:

Most patients with cancer do not participate in a clinical trial. Understanding clinical participation rates, and the barriers and motivators that influence participation may help identify opportunities for improvement in accrual.

Methods:

A cross-sectional analysis of cancer survivors was conducted using the Health Information National Trends Survey (HINTS) administered in 2020. Primary outcome was clinical trial participation amongst patients with cancer. Secondary outcomes were motivators and barriers to influence clinical trial participation. Logistic regression was employed to assess the association of clinical trials being discussed as a cancer treatment option with social determinants.

Results:

Six hundred and eighteen respondents (weighted population estimate 22,723,047) with a self-reported history of cancer were included. Overall, 15.7% reported an invitation to participate in a clinical trial, of which 37.8% participated. Clinical trials were discussed as a cancer treatment option amongst 13.5% of respondents. Knowledge of clinical trials was low (9.3%). Reported motivators included trying new care (72.3%), wanting to get better (88.9%), getting paid (40.1%), helping other people (73.0%), and encouragement from the doctor (73.7%) or family/friends (59.5%). Reported barriers included getting transportation, childcare or paid time off work (42.4%), and standard care not covered by insurance (69.6%). Race (Other, OR 3.84) and income (<$35k, OR 5.84) were associated with discussion of clinical trials as a cancer treatment option.

Conclusion:

Clinical trial treatment discussion, invitation, and participation are low among patients with a history of cancer. Although the study identified multiple motivators and barriers to participation, improvement in the rates of discussion and invitation to participate in a clinical trial are required. Nevertheless, addressing the identified barriers and motivators that influence clinical trial participation may be a strategy to optimize patient enrollment.

Keywords

oncology clinical trial engagement motivator barrier

Introduction

Clinical trials are a critical part of the research process, essential for assessing clinical innovations, many of which target patients with cancer.¹ These trials represent the gold standard for testing the efficacy and safety of an intervention. Despite multiple benefits derived from clinical trial participation, accrual is challenging.² Approximately one in four clinical trials fail to enroll the number of participants required, and 18% of trials close with fewer than half of the target accrual.³ Inability to recruit sufficient participants may be secondary to lack of patient awareness, lack of invitation to participate, or a desire not to participate.

In a 2000 poll commissioned by the American Society of Clinical Oncologists, 85% of nearly 6000 patients with cancer said they were either unaware or not sure that participation in a clinical trial was an option.⁴ Nevertheless, 32% of American adults indicated that they would be willing to participate in a cancer clinical trial if invited.⁵ For cancer patients that might be aware that clinical trials represent a therapeutic option, many are unable to participate due to lack of access. A systematic review reported that local clinical trials were not available for 56% of patients with cancer, exemplifying a structural barrier to recruitment amongst interested eligible patients.⁶ Despite this, interest in clinical trial participation remains similar amongst urban and rural patients with cancer.⁷ There are many other factors which may inhibit enrollment amongst patients with cancer including comorbid conditions,⁸ perceptions regarding clinical trials,⁹ age,^10,11 race, ^12-14 or socio-economic differences.¹⁵ As a result, clinical trial participation rates remain poor, with studies suggesting overall rates under 10%,^6,11,16 with Medicare billing data suggesting rates less than 2%.¹⁷

A paucity of data exists to report nationally representative clinical trial participation patterns among patients with cancer at the population level. Specifically, awareness of clinical trials amongst patients with cancer, rates of invitation and rates of subsequent participation are unknown. The primary objective of this study is to determine the frequency of clinical trial discussion as a treatment option, invitation to participate based on eligibility, and subsequent participation amongst patients with cancer. Secondarily, we seek to assess self-reported social motivators and barriers influencing clinical trial participation. Finally, we aim to further explore patient factors associated with higher likelihood of cancer trial discussion as a treatment option.

Methods

Data source and cohort selection

The Health Information National Trends Survey (HINTS) is an ongoing cross-sectional nationally representative survey administered by the National Cancer Institute (NCI). HINTS explores consumer health information and communication behavior topics, including perceptions of clinical trials and cancer history. The target population of HINTS consists of adults 18 years or older in the US civilian noninstitutionalized population. HINTS uses a two-stage sampling design selecting a stratified sample of households, ultimately identifying an adult from each sampled household. For this study, HINTS 5, Cycle 4 survey, conducted in 2020, was used.¹⁸ Our analytic cohort was comprised of adults who answered “yes” to the question “have you ever been diagnosed as having cancer?”

Clinical trials participation

Our primary outcomes included discussions of a clinical trial as a treatment option, invitation to participate in a clinical trial, and patient participation in a trial. This was assessed by the survey questions: “Has a doctor or other member of your medical team discussed clinical trials as a treatment option for your cancer?”, “Have you ever been invited to participate in a clinical trial?”, or “Did you participate in the clinical trial?” Responses included “Yes” or “No”.

Motivators to participating in a clinical trial

Assessment of motivators that may influence a respondent’s decision to participate in a clinical trial were assessed from the responses to the six questions: ‘Imagine that you had a health issue and you were invited to participate in a clinical trial for that issue. 1. “How much would ‘getting a chance to try a new kind of care’ influence your decision to participate in the clinical trial?” 2. “How much would ‘getting paid to participate’ influence your decision to participate in the clinical trial?” 3. “How much would ‘wanting to get better’ influence your decision to participate in the clinical trial?” 4. “How much would ‘helping other people by participating’ influence your decision to participate in the clinical trial?” 5. “How much would ‘your doctor encouraging you to participate’ influence your decision to participate in the clinical trial?” 6. “How much would ‘your family and friends encouraging you to participate’ influence your decision to participate in the clinical trial?” Responses included: ‘Not at all’, ‘A little’, ‘Somewhat’ and ‘A lot’. Due to limited sample sizes, responses were then consolidated into two groups including ‘Not at all’, and ‘A little’, vs. ‘Somewhat’ and ‘A lot.’

Barriers to participating in a clinical trial

Assessment of barriers that may influence a respondent’s decision to participate in a clinical trial were assessed from the responses to the three questions: 1. “How would you describe your level of knowledge about clinical trials?” Responses included “I don’t know anything about clinical trials”, “I know a little bit about clinical trials”, “I know a lot about clinical trials”. 2. “Imagine that you had a health issue and you were invited to participate in a clinical trial for that issue. How much would ‘getting support such as transportation, childcare, or paid time off from work’ influence your decision to participate in the clinical trial?” 3. “How much would ‘the standard care not being covered by your insurance’ influence your decision to participate in the clinical trial?” Responses included: ‘Not at all’, ‘A little’, ‘Somewhat’ and ‘A lot’. Due to limited sample sizes, responses were then consolidated into two groups including ‘Not at all’, and ‘A little’, vs. ‘Somewhat’ and ‘A lot.’

Measures

HINTS collects regular socio-economic and demographic variables, which were included as covariates: Age (continuous variable); Sex (male and female); Marital Status (married before, now, and single); Education (High School (HS) or less, some college, or college degree and higher); Race/ethnicity (White, Black, and Other); Income (<$35,00; $35,000-<$75,000; and ⩾$75,000); Income Feeling (living comfortably on present income, getting by on present income, finding it difficult on present income); Metro location (defined as area of residence for counties in metro areas using USDA Rural/Urban Designation (2013); E-device ownership (owning a tablet or smartphone); Medicaid (Medicaid, medical assistance, or any kind of government-assistance plan); Regular provider (response of “Yes” to the question “Is there a doctor/nurse/health provider that you see most often?”); Cardiovascular disease was based on answering “Yes” to the question “Has the doctor or other health professional ever told you that you had any of the following medical conditions: Diabetes or high blood sugar? High blood pressure or hypertension? A heart condition such as heart attack, angina, or congestive heart failure?”

Statistical analysis

We described the study cohort with and without survey weights. Following recommendations by HINTS, survey results were weighted to approximately adjust to the overall adult US population. The survey replicate weight and variance estimation methods were used per HINTS study recommendations to ensure valid inferences by accounting for the complex sampling design, including clustering of subjects, and nonresponse and noncoverage biases.

We present frequencies and percentages for primary outcomes, motivators, and barriers by social determinants. Chi-squared analysis was employed to determine the association between sociodemographic characteristics and discussion, invitation, or participation in a clinical trial, with significance threshold of p<0.05.

A single multivariable logistic regression model was fitted to assess the association between patient characteristics and the likelihood of clinical trial discussion as a treatment option. This outcome was selected because the decision to discuss a trial is made by the provider, and it serves as a prerequisite for the downstream outcomes of invitation and participation, which are influenced by patient choice. The goal of this model was to evaluate whether specific patient demographic or socio-economic factors were associated with a provider initiating a clinical trial discussion.

The covariates included in this model were selected based on theoretical relevance to disparities in clinical trial access and included: race/ethnicity, education, income, e-device ownership, and Medicaid enrollment status. While initial univariate analysis informed candidate variables, we ultimately retained a consistent set of covariates based on conceptual importance, recognizing limitations of p-value–based selection in small samples. Likert-scale responses for motivators and barriers were collapsed into binary categories to preserve interpretability and statistical power. While this approach may limit granularity, we judged it acceptable for exploratory purposes and have noted this as a limitation.

Survey weights were incorporated into the regression model using SAS 9.4 survey procedures (SAS Institute), ensuring nationally representative estimates and unbiased inference. Missing data were assessed and handled using complete case analysis for the regression model. Covariate missingness was minimal (<5%).

Results

Demographics

A total of 618 respondents were included, representing a population weighted estimate 22,723,047 people with a self-reported history of cancer. Mean age was 63 years and 43.5% were female. Respondent demographic data are shown (Table 1).

Table 1.

Sociodemographic characteristics of survey respondents (Raw frequency n=618, weighted frequency n=22,723,047).

Characteristic	Unweighted n (%)	Weighted %	p-value
Age (Mean, years)	63.3	62.1	—
Sex
Female	237 (42.0%)	43.5%	0.12
Male	327 (58.0%)	56.5%
Marital Status
Married Now	312 (51.8%)	50.7%	0.24
Married Before	218 (36.2%)	32.6%
Single	72 (12.0%)	16.7%
Education
High School or Less	173 (28.9%)	35.2%	0.03*
Some College	164 (27.4%)	37.4%
College or More	261 (43.6%)	27.4%
Race/Ethnicity
White	448 (74.4%)	75.0%	0.40
Black	76 (12.6%)	11.1%
Other	78 (13.0%)	13.9%
Household Income
<$35,000	181 (33.2%)	30.9%	0.65
$35,000–$74,999	180 (33.0%)	36.5%
⩾$75,000	184 (33.8%)	32.6%
Income Feeling
Comfortable	244 (41.6%)	35.1%	0.09
Getting by	237 (40.4%)	40.7%
Difficult	105 (17.9%)	24.2%
Metro Area
Yes	537 (86.9%)	81.3%	<0.01*
No	81 (13.1%)	18.7%
Owns Electronic Device
Yes	481 (77.8%)	79.9%	0.18
No	137 (22.2%)	20.1%
Medicaid Coverage
Yes	87 (14.5%)	10.6%	<0.01*
No	511 (85.5%)	89.4%
Regular Provider
Yes	515 (83.4%)	89.5%	<0.01*
No	86 (16.6%)	10.5%
Cardiopulmonary Disease
Yes	336 (54.1%)	47.3%	0.04*
No	282 (45.9%)	52.7%

Note: p-values represent chi-square comparisons between categories of each variable and a dichotomous outcome (e.g., survey response status or health condition, as applicable). *designates statistically significant.

Clinical trials invitation, participation, and discussion as a cancer treatment option

Overall, 13.5% of respondents reported that a doctor or member of the medical team discussed clinical trials as a treatment option for their cancer (Table 2). Patients with lower levels of education were more likely to report that a clinical trial had been discussed as an option for treatment, despite low levels of participation (P=0.011). Black or other race was associated with increased likelihood of discussion (P=0.0059). Additionally, lower income patients, and those who felt their economic situation to be ‘difficult’ were more likely to report that a clinical trial had been discussed (P=0.0003). E-device ownership (P=0.003), and Medicaid insurance holders (P=0.0036) were informed about clinical trials at a higher rate.

Table 2.

Clinical trial invitation, participation, and discussion as a cancer treatment option among survey respondents with a history of cancer.

Characteristic	Discussed Clinical Trials: Weighted % Responding ‘Yes’ (n=597)	p	Ever Invited to a Clinical Trial: Weighted % Responding ‘Yes’ (n=618)	p	Participated in a Clinical Trial: Weighted % Responding ‘Yes’ (n=108)	p
Overall	13.5	—	15.7	—	37.8	—
Sex		0.78		0.99		0.58
Female	13.5		16.0		42.6
Male	14.7		15.9		31.2
Marital Status		0.52		0.12		0.034*
Married Before	17.2		14.9		53.9
Married Now	12.2		13.8		42.6
Single	17.9		27.4		16.1
Education		0.011*		0.017*		0.097
High School or Less	22.0		8.7		41.5
Some College	9.0		18.7		23.2
College or More	11.3		21.7		53.3
Race/Ethnicity		0.0059*		0.36		0.36
White	11.4		15.7		14.8
Black	19.7		23.7		19.5
Other	30.3		12.3		29.5
Income		0.0003*		0.24		0.19
<$35k	26.4		19.5		27.8
$35–74k	11.9		11.0		56.3
⩾$75k	4.9		15.4		45.1
Income Feeling		0.0096*		0.98		0.13
Comfortable	5.9		16.3		53.9
Getting By	16.2		15.4		35.8
Difficult	24.1		14.9		22.8
Metro Area		0.82		0.013*		0.44
No	14.9		4.5		60.0
Yes	13.6		18.5		36.5
Owns Electronic Device		0.0030*		0.84		0.28
No	24.8		16.8		21.1
Yes	11.2		15.6		42.3
Medicaid Coverage		0.0036*		0.33		0.0058*
No	11.4		14.9		43.4
Yes	29.8		22.2		12.5
Regular Provider		0.33		0.23		0.18
No	9.4		10.3		16.5
Yes	14.4		17.1		38.9
Cardiopulmonary Disease		0.25		0.78		0.46
No	10.8		16.8		31.0
Yes	15.6		15.3		42.0

Note: Weighted percentages are shown. p-values refer to chi-square tests of association between each characteristic and the binary outcome (“Yes” vs. “No”) in each column. *designates statistically significant.

Similarly, 15.7% of patients with a cancer diagnosis reported an invitation to participate in a clinical trial based on eligibility (Table 2). Invitation was associated with higher levels of education (P=0.017) and residence in a metro area (P=0.013). Among those respondents endorsing an invitation, 37.8% acknowledged subsequent participation. Participants represent 7.4% of all survey respondents with a history of cancer. Amongst the patients who were invited to participate in a clinical trial, respondents who were either currently married or had been married prior were more likely to take part in a clinical trial (P=0.034). Patients with non-Medicaid insurance (P=0.0058) were more likely to participate. Race was not associated with frequency of participation in a clinical trial.

Motivators to clinical trial participation

Approximately three-quarters of respondents (72.3%) reported that ‘getting a chance to try a new care’ would influence their decision to participate in a clinical trial ‘somewhat’ or ‘a lot’ (Table 3). Wanting to get better served as the most significant factor prompting clinical trial participation (88.9%). Meanwhile, payment for participation was the factor least associated with participation with 40.1% of respondents reporting this as a motivating factor. Patients with higher levels of education and those who owned e-devices were most likely to report participation due to a ‘desire to try new care,’ or ‘wanting to get better.’ Low income respondents (p=0.024), those reporting a ‘difficult’ economic situation (p=0.026), and those living outside of a metro area (p=0.036) were more likely to report ‘getting paid’ for participation as a motivating factor.

Table 3.

Weighted percent responses of ‘A Lot’ and ‘Somewhat’ to motivators to clinical trial participation.

Characteristic	Desire to Try New Care: Weighted % Responding ‘A lot’ or ‘Somewhat’ (%)	p	Wanting to Get Better: Weighted % Responding ‘A lot’ or ‘Somewhat’ (%)	p	Getting Paid: Weighted % Responding ‘A lot’ or ‘Somewhat’ (%)	p	Helping Others	p	Doctor Encouragement: Weighted % Responding ‘A lot’ or ‘Somewhat’ (%)	p	Family/Friend Encouragement: Weighted % Responding ‘A lot’ or ‘Somewhat’ (%)	p
Overall	72.3	—	88.9	—	40.1	—	73.0	—	73.7	—	59.5	—
Sex		0.60		0.24		0.36		0.16		0.092		0.07
Female	73.3		87.2		43.3		76.7		77.0		64.5
Male	70.6		91.2		37.0		69.1		68.0		52.3
Marital Status		0.85		0.20		0.16		0.077		0.72		0.99
Married Before	69.7		84.7		38.0		65.8		77.0		60.0
Married Now	72.9		91.9		37.2		72.8		71.8		59.4
Single	75.2		84.4		55.9		84.8		73.8		58.5
Education		0.002*		0.002*		0.053		0.14		0.01*		0.63
High School or Less	59.9		81.1		35.1		66.1		65.4		55.4
Some College	83.1		93.3		49.4		78.1		79.1		61.0
College or More	74.3		93.2		34.6		75.2		75.3		62.4
Race/Ethnicity		0.12		0.073		0.09		0.36		0.83		0.08
White	74.8		90.2		39.6		74.4		73.9		61.3
Black	66.4		90.0		33.9		70.0		70.0		61.9
Other	55.3		77.7		56.9		63.0		69.0		37.0
Income		0.85		0.32		0.024 *		0.86		0.58		0.45
<$35k	70.8		86.9		56.0		72.9		69.5		52.9
$35k–74k	74.8		87.1		34.2		72.5		76.9		62.1
⩾$75k	71.6		93.6		35.3		76.6		71.9		62.1
Income Feeling		0.58		0.33		0.026*		0.42		0.34		0.87
Comfortable	69.7		90.5		30.7		78.4		76.1		61.1
Difficult	78.3		83.0		56.7		72.1		65.1		56.3
Getting By	71.7		90.3		42.0		70.8		74.2		59.6
Metro Area		0.52		0.53		0.036*		0.12		0.71		0.42
No	67.6		86.3		55.0		81.9		71.4		53.5
Yes	73.4		89.5		36.7		70.9		74.2		60.9
Owns Electronic Device		0.028*		0.028*		0.43		0.87		0.18		0.28
No	59.4		81.0		34.1		72.0		66.5		52.7
Yes	75.3		90.7		41.5				75.4		61.1
Medicaid Coverage		0.61		0.051		0.25		0.30		0.43
No	72.9		90.6		39.1		74.3		74.6
Yes	68.8		80.2		49.2		66.3		67.7
Regular Provider		0.59		0.61		0.78		0.93		0.34		0.82
No	68.9		87.0		37.6		72.3		66.9		61.4
Yes	72.9		89.4		40.3		74.2		69.3		62.6
Cardiopulmonary Disease		0.75		0.50		0.13		0.11		0.57		0.45
No	73.6		90.5		46.1		78.3		71.5		63.0
Yes	71.5		87.9		36.7		69.9		74.9		57.5

Note: Weighted percentages are shown. p-values refer to chi-square tests of association between each characteristic and the binary outcome (“A lot” + “Somewhat” vs. “A little” + “Not at all”) in each column. * designates statistically significant.

Overall, 73.0% of respondents reported that ‘helping other people by participating’ in a clinical trial would influence their decision to participate ‘somewhat’ or ‘a lot.’ Doctor encouragement or family/friend encouragement were reported to influence the decision to participate in a clinical trial ‘somewhat’ or ‘a lot’ amongst 73.7% and 59.5% of respondents, respectively. Individual social determinants were not associated with a decision to participate in a clinical trial based on helping other people, doctor, or family encouragement. Frequency, weighted frequency, and weighted percent responses of ‘A Little’ and ‘Not at All’ to motivators to clinical trial participation are reported (Online Supplemental Table 1).

Barriers to clinical trials participation

Regarding patient awareness, 90.7% reported little or no knowledge regarding clinical trials as an option (Table 4). Patients with higher educational attainment (P<0.0001), higher income (P<0.0001), residence in a metro area (P=0.0057), those who owned e-devices (P=0.0013), and those with a regular provider (P=0.0167) were more likely to report awareness of clinical trials.

Table 4.

Weighted percent responses to barriers to clinical trial participation among adults with a reported history of cancer.

Characteristic	Clinical Trial Knowledge: Weighted % Responding ‘Don’t Know’	Clinical Trial Knowledge: Weighted % Responding ‘A Little’	Clinical Trial Knowledge: Weighted % Responding ‘A Lot’	p	Getting Transportation, Childcare, or Paid Time Off Work: Weighted % Responding ‘A lot’ or ‘Somewhat’	p	Standard Care Not Being Covered By Your Insurance: Weighted % Responding ‘A lot’ or ‘Somewhat’	p
Overall	36.1	54.6	9.3		42.4		69.6
Sex				0.73		0.61		0.11
Female	36.6	53.1	10.2		41		66.3
Male	34.6	57.9	7.6		44.5		75.3
Marital Status				0.45		0.0039*		0.03*
Married Before	41.2	49.0	9.8		34.3		55.7
Married Now	35.5	54.6	6.2		38.6		72.5
Single	29.7	65.3	5.0		70.1		80.6
Education				<0.0001 *		0.96		0.013 *
High School or Less	55.4	41.5	3.1		43		58.8
Some College	33.0	58.6	8.3		43.3		74
College or More	14.1	68	17.9		41.3		79
Race				0.11		0.42		0.19
White	33	56.8	10.2		44.3		71.6
Black	54.3	41.5	4.2		32.9		67.5
Other Race	40.8	54.7	4.5		37.7		54.9
Income				<0.0001		0.094		0.12
< $35k	50.7	43	6.3		50.5		60
$35-74k	38.6	57.9	3.4		47.5		68.5
⩾ $75k	20.7	63.3	16		33.1		77.7
Income Feeling				0.085		0.032*		0.73
Comfortable	32.4	52.1	15.5		35.4		66.7
Difficult	44.6	47.3	8.1		61.3		70.7
Getting By	34.4	60.8	4.8		41		72.5
Metro Area				0.0057*		0.3		0.26
No	54.9	41.2	3.9		50.1		61
Yes	31.7	57.7	10.6		40.5		71.6
Owns Electronic Device				0.0013*		0.81		0.0025*
No	56.9	40.2	2.9		40.8		49.2
Yes	30.8	58.2	10.9		42.7		60.2
Medicaid Coverage				0.95		0.53		0.22
No	35.6	54.9	9.5		41.6		71
Yes	37.5	54.5	8		47.4		60.6
Regular Provider				0.0167*		0.32		0.8
No	55.1	40.5	4.4		51.2		68.5
Yes	31.1	58.5	10.4		40.5		70.4
Cardiopulmonary Disease				0.28		0.094		0.15
No	40.6	49	10.5		49.8		74.6
Yes	42.2	50.1	7.8		38		66.6

Regarding barriers to clinical trial participation, 42.4% of respondents reported that getting transportation, childcare, or paid time off work would increase their ability to participate in a clinical trial ‘somewhat’ or ‘a lot.’ A significant proportion of ‘single’ respondents reported that participation was constrained by their marital status (P=0.0039). Lack of insurance coverage for the standard care treatment option was reported to influence the patient decision to participate in a clinical trial ‘somewhat’ or ‘a lot’ amongst 69.6% of respondents. Respondents who were ‘single’ (P=0.030), those with higher levels of educational attainment (P=0.013), and those who reported e-device ownership (P=0.0025) were more likely to report standard care not being covered by insurance as a barrier to participation. Frequency, weighted frequency, and weighted percent responses of ‘A Little’ and ‘Not at All’ to barriers to clinical trial participation are reported (Online Supplemental Table 2).

Multivariable logistic regression of clinical trials being discussed as cancer treatment

Multivariable logistic regression analysis was performed to assess the association of clinical trials being discussed as a cancer treatment option with patient factors. (Table 5). Race (Other, OR 3.84, 95% Cl 1.23-11.92), and income (<$35k OR 5.84, 95% Cl 1.13-30.32) were associated with increased odds that a clinical trial was discussed as a cancer treatment option.

Table 5.

Multivariable logistic regression model assessing the effects of self-reported social determinants on discussing clinical trials as a treatment option for your cancer.

	OR	95% Cl
Education
	Ref
College+	1.59	0.60-4.20
Race/Ethnicity
White	Ref
Black	1.33	0.48-3.70
Other	3.84	1.23-11.92
Income
$75k+	Ref
<$35k	5.84	1.13-30.32
$35-75k	2.47	0.91-6.65
Owns Electronic Device
Yes	Ref
No	0.63	0.24-1.62
Medicaid Coverage
Yes	Ref
No	1.60	0.62-4.16

Discussion

Advances in cancer care rely on clinical trials.¹ However, cancer trial accrual remains poor, and few patients with cancer enroll in a clinical trial.^12,19 Identification of barriers and motivators to trial enrollment represent an opportunity to improve clinical trial participation. In this study, we assessed the rates of clinical trial discussion, invitation, and participation amongst patients with a history of cancer using a nationally representative survey. We prioritized discussion of clinical trials as a treatment option as our key outcome for multivariable modeling, given that this provider-initiated step is a prerequisite for downstream patient decisions regarding invitation and participation.

Overall, reported enrollment in clinical trials amongst respondents with cancer was 7.4%. This is similar to rates reported in the literature, which range from 5-10%.^6,11,16 In this study, 37.8% of respondents invited to enroll in a clinical trial reported subsequent participation. However, the participation rate in this study remained significantly lower as compared to a meta-analysis suggesting a 55% participation rate amongst respondents with a history of cancer who received an invitation to participate.²⁰ This difference may be due to biases presented by survey design or variability in respondent populations, such as age, race¹² and geographic differences have been demonstrated to impact participation rates.²¹ Given that this study includes a nationally representative survey sample, reported rates may represent a more generalizable perspective regarding clinical trial enrollment. This is supported by a prior national probability sample of American adults, which reported that 32% of respondents were very willing to enroll in a clinical trial if invited.⁵

Clinical trial participation is motivated by personal benefit, incorporating an interest in therapeutic options, financial compensation, access to care, curiosity and scientific interest. Our study findings provide insight regarding the motivating factors that influenced respondents to participate in a clinical trial. For example, ‘wanting to get better’ was most associated with participation. However, ‘getting paid’ was least likely to have an impact. This contradicts prior studies which suggest financial incentives may serve as a primary motivator.^22,23 It should be noted that concerns exist regarding the ethics of financial incentivization for trial participation.²⁴ Altruism, including advancing science and treatment of a patients with cancer’s disease, helping save or improve the lives of others, or improve their own disease or condition was cited as the top benefits of a clinical trial in a Global survey by the Center for Information and Study on Clinical Research Participation (CISCRP).²⁵ Other studies have observed conflicting results regarding altruism, suggesting a role as a secondary motivator.²² Our study supported this notion, demonstrating that 73% of respondents reported that ‘helping others’ would influence their decision to participate in a clinical trial. Lastly, prior studies have observed that physician decision or preference was the primary reason for non-participation of patients who were eligible for a clinical trial.²⁶ Results of this study underscore that doctor encouragement served to influence patient decisions. Given the centrality of provider initiation, our regression model focused on identifying patient demographic factors associated with discussion of clinical trials by the medical team. This provider behavior serves as a critical gatekeeper step, shaping whether patients even have the opportunity to consider participation, regardless of patient social determinants, while encouragement from family/friends appears less influential.

Although barriers to clinical trial participation have been the subject of recent study,²⁷ the rate of clinical trial participation has not changed substantially over time.²⁸ Factors associated with access to clinical trials include age, race, marital status,¹⁵ insurance,²⁹ or access.⁶ Our study directly addressed several of these patient concerns, identifying social factors that served as barriers to access due to issues such as transportation, childcare or paid time off of work. In general, these reflect less frequently reported barriers, suggesting that interventions targeting these obstacles may not be as effective in enhancing clinical trial participation. Similarly, rural-urban disparities regarding access to clinical trial sites have been suggested.³⁰ Our analysis, however, found that metro location was only associated with clinical trial knowledge, but not other barriers to participation. Nevertheless, there are certain respondents, including those who were single, and those characterizing their economic situation as ‘difficult’ who may be more likely to participate should access-related factors be addressed.

Nearly 70% of respondents reported that the standard of care not being covered by insurance would influence clinical trial participation. Out of pocket costs have been strongly linked to financial toxicity in the setting of cancer care,³¹ however we did not find an association between income or income feeling and the decision to pursue a clinical trial due to lack of ‘insurance coverage of the standard of care.’ This may be explained by the Congressional Clinical Treatment Act, which requires Medicaid to subsidize clinical care expenditures associated with clinical trial enrollment.³²

Recent analyses of cancer treatment trials report that only 4% to 6% of trial participants are Black and 3% to 6% are Hispanic, even though they represent 15% and 13% of all patients with cancer, respectively.³³ Race and ethnicity have been showed to be significantly associated with clinical trial awareness, independent of sociodemographic, attitudinal, and knowledge variables. Compared with White respondents, Black and Hispanic respondents have been observed to be significantly less likely to have heard of a clinical trial,³⁴ however a recent analysis has highlighted that Black and White patients participate in clinical trials at similar rates.²⁰ We found Black respondents had the highest rate of being invited to participate in a clinical trial (23.7%) and were more likely to have discussed a clinical trial as compared to White respondents. Race was not associated with clinical trial knowledge in our analysis.

Efforts to increase clinical trial awareness and participation are underway through a combination of regulatory policy, advocacy, outreach, global engagement, and research publications. The American Society of Clinical Oncology (ASCO) and Association of Community Cancer Centers (ACCC) have developed initiatives including a research site assessment tool and implicit bias training program.³⁵ The goal of these programs is to establish evidence-based practical strategies and solutions to help increase participation of people from historically underrepresented racial and ethnic communities in cancer treatment trials to address the barriers preventing enrollment of eligible patients. The April 2021 ASCO meeting offered an Education Session “Bridging the Divide: Novel Strategies to Bring Clinical Trials to Disparate Communities”. In addition, the FDA has several ongoing projects including Project Equity, which focuses on generating evidence for diverse populations in oncology by increasing the enrollment in clinical trials through developing policy and guidance.^36,37 The Coalition for Clinical Trials Awareness³⁸ and Center for Information and Study on Clinical Research Participation³⁹ continue to serve as advocates for both patient and health professionals, with the goal of improving awareness of the benefits of clinical trial enrollment. Lastly, ASCO and other bodies have suggested liberalizing clinical trial eligibility criteria, which may serve to both hasten and diversify accrual.⁴⁰ Whether these campaigns will ultimately increase enrollment and diversity remains to be seen.

This study was subject to several limitations and has several strengths. First, we examined cross-sectional data. Longitudinal data may have provided deeper insight into the trends that motivate and deter trial participation. Second, as the study design is cross-sectional, findings are not sufficient for assessing causality. Third, the age/year when the patient was diagnosed with cancer is not reported, which may have led to inaccurate reporting by participants. Similarly, the number of clinical trials has increased substantially and patients diagnosed with cancer at an earlier timepoint may have had fewer trial options. Next, HINTS does not report a respondent’s cancer histology or stage, which may influence the effect of motivating and deterring factors. Histology and stage may also serve to influence clinical trial eligibility. Further, HINTS does not address factors such as proximity to an academic hospital, or comprehensive cancer center, which may influence outcomes, especially for those residing in non-metro areas. Respondents may have been invited to participate in a non-oncology clinical trial which may have overestimated our results.

In addition, our decision to collapse Likert-scale responses into binary categories, while done for interpretability and to address sample size constraints, may have reduced variability and masked more nuanced attitudinal patterns.

Moreover, several important contextual factors may explain why providers do not discuss or offer clinical trials to eligible patients such as provider oversight, patient ineligibility due to exclusion criteria, existence of a highly effective standard-of-care treatment, lack of an open clinical trial matching the patient’s diagnosis, limited provider interest or engagement, or another unidentified or unmeasured factor. These limitations underscore the complex, multifactorial nature of trial access and highlight areas for future investigation into structural and provider-level determinants of clinical trial discussion.

Our analysis does capture and document a national representative population and presents a detailed assessment of clinical trial motivators and barriers that have previously not been reported Future work may benefit from modeling these outcomes using ordinal or latent class methods. Nevertheless, results of this study may ultimately inform strategies to improve patient enrollment, and may serve as a foundation for further exploration of specific motivators and barriers introduced.

Conclusions

Clinical trials are not routinely discussed with patients with cancer and rates of invitation to participate in clinical trials are low. Even when invited to participate, enrollment rates are low. Addressing the identified barriers and motivators that influence clinical trial participation may serve to improve patient engagement and enrollment.

Supplemental Material

sj-pdf-1-tmj-10.1177_03008916251347175 – Supplemental material for Engagement in cancer clinical trials among a nationally representative cancer survivor sample: Motivators, barriers and opportunities for improvement

Supplemental material, sj-pdf-1-tmj-10.1177_03008916251347175 for Engagement in cancer clinical trials among a nationally representative cancer survivor sample: Motivators, barriers and opportunities for improvement by Zachary S. Feuer, Richard S. Matulewicz, Ramsankar Basak, Donna A. Culton, Kimberly Weaver, Kristalyn Gallagher, Hung-Jui Tan, Tracy L. Rose, Matthew Milowsky and Marc A. Bjurlin in Tumori Journal

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: MAB is supported by a Lineberger Comprehensive Cancer Center Innovation award, and a NC TraCS grant 550KR221903. MAB is supported by the New York State Department of Health, Empire Clinical Research Investigators Program.

Ethical Approval Statement

Approval from the Office of Human Research Ethics Institutional Review Board at UNC was waived for this study (#23-2213)

ORCID iD

Zachary S. Feuer

Supplemental material

Supplemental material for this article is available online.

References

National Cancer Institute. What Are Cancer Clinical Trials? https://www.cancer.gov/about-cancer/treatment/clinical-trials/what-are-trials (2016, accessed 8 November 2022).

Ross

Grant

Counsell

, et al. Barriers to participation in randomised controlled trials: a systematic review. J Clin Epidemiol 1999; 52: 1143-1156.

Fogel

DB.

Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun 2018; 11: 156–164.

Harris Interactive. Cancer Clinical Trials: Opportunities for Increasing Enrollment (Study No. 11799). New York, NY: Harris Interactive, 2000.

Comis

Miller

Aldigé

, et al. Public attitudes toward participation in cancer clinical trials. J Clin Oncol 2003; 21: 830–835.

Unger

Vaidya

Hershman

, et al. Systematic review and meta-analysis of the magnitude of structural, clinical, and physician and patient barriers to cancer clinical trial participation. J Natl Cancer Inst 2019; 111: 245–255.

Caston

Williams

Wan

, et al. Associations between geography, decision-making style, and interest in cancer clinical trial participation. Cancer 2022; 128: 3977-3984.

Unger

Hershman

Fleury

, et al. Association of patient comorbid conditions with cancer clinical trial participation. JAMA Oncol 2019; 5: 326-333.

Granda-Cameron

McLean Florence

Whitfield-Harris

, et al. Perceptions of clinical trial participation in African American cancer survivors and caregivers. Oncol Nurs Forum 2022; 49: 113-124.

10.

Sedrak

Freedman

Cohen

, et al. Older adult participation in cancer clinical trials: A systematic review of barriers and interventions. CA Cancer J Clin 2021; 71: 78-92.

11.

Keim-Malpass

Alcalá

HE.

Association of age at cancer diagnosis and clinical trial participation. JAMA Netw Open 2021; 4: e2037573.

12.

Murthy

Krumholz

Gross

CP.

Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA 2004; 291: 2720-2726.

13.

Awidi

Al Hadidi

Participation of black Americans in cancer clinical trials: current challenges and proposed solutions. JCO Oncol Pract 2021; 17: 265-271.

14.

Iyizoba-Ebozue

Fatimilehin

Mbanu

, et al. Reflection on black and ethnic minority participation in clinical trials. Clin Oncol (R Coll Radiol) 2022; 34: 674-677.

15.

Parekh

Desai

Demographic and socioeconomic disparities among cancer survivors in clinical trials participation, USA, 2016-2018. J Cancer Educ 2022; 37: 88-90.

16.

Unger

Hershman

Albain

, et al. Patient income level and cancer clinical trial participation. J Clin Oncol 2013; 31: 536-542.

17.

Green

Tabatabai

Aghajanian

, et al. Clinical trial participation among older adult Medicare fee-for-service beneficiaries with cancer. JAMA Oncol 2022; 8: 1786-1792.

18.

National Cancer Institute. Health Information National Trends Survey, https://hints.cancer.gov/ (accessed 14 July 2021).

19.

Tejeda

Green

Trimble

, et al. Representation of African-Americans, Hispanics, and whites in National Cancer Institute cancer treatment trials. J Natl Cancer Inst 1996; 88: 812–816.

20.

Unger

Hershman

Till

, et al. “When offered to participate”: A systematic review and meta-analysis of patient agreement to participate in cancer clinical trials. J Natl Cancer Inst 2021; 113: 244–257.

21.

Unger

Moseley

Symington

, et al. Geographic distribution and survival outcomes for rural patients with cancer treated in clinical trials. JAMA Netw Open 2018; 1: e181235.

22.

Nappo

Iafrate

Sanchez

ZM.

Motives for participating in a clinical research trial: a pilot study in Brazil. BMC Public Health 2013; 13: 19.

23.

Stunkel

Grady

More than the money: a review of the literature examining healthy volunteer motivations. Contemp Clin Trials 2011; 32: 342–352.

24.

Bernstein

Feldman

Incentives to participate in clinical trials: practical and ethical considerations. Am J Emerg Med 2015; 33: 1197-1200.

25.

Anderson

Borfitz

Getz

Global public attitudes about clinical research and patient experiences with clinical trials. JAMA Netw Open 2018; 1: e182969.

26.

Hunter

Frelick

Feldman

, et al. Selection factors in clinical trials: results from the Community Clinical Oncology Program Physician’s Patient Log. Cancer Treat Rep 1987; 71: 559–565.

27.

Nipp

Hong

Paskett

. Overcoming barriers to clinical trial enrollment. Am Soc Clin Oncol Educ Book 2019; 39: 105–114.

28.

Unger

Cook

Tai

, et al. The role of clinical trial participation in cancer research: barriers, evidence, and strategies. Am Soc Clin Oncol Educ Book 2016; 35: 185–198.

29.

Mackay

Antonelli

Bruinooge

, et al. Insurance denials for cancer clinical trial participation after the Affordable Care Act mandate. Cancer 2017; 123: 2893-2900.

30.

Iglehart

JK.

The challenging quest to improve rural health care. N Engl J Med 2018; 378: 473-479.

31.

Smith

Lopez-Olivo

Advani

, et al. Financial burdens of cancer treatment: A systematic review of risk factors and outcomes. J Natl Compr Canc Netw 2019; 17: 1184–1192.

32.

Luján

B. R.

H.R. 913 – CLINICAL TREATMENT Act, 116th Congress (2019–2020). Congress.gov. https://www.congress.gov/bill/116th-congress/house-bill/913 (accessed 26 August 2021).

33.

Nazha

Mishra

Pentz

, et al. Enrollment of racial minorities in clinical trials: old problem assumes new urgency in the age of immunotherapy. Am Soc Clin Oncol Educ Book 2019; 39: 3–10.

34.

Langford

Resnicow

Clinical trial awareness among racial/ethnic minorities in HINTS 2007: sociodemographic, attitudinal, and knowledge correlates. J Health Commun 2010; 15 Suppl 3: 92–101.

35.

ACCC. ACCC Community Oncology Research Institute, https://www.accc-cancer.org/home/learn/research-clinical-trials/accc-community-oncology-research-institute (accessed 8 September 2021).

36.

Fashoyin-Aje

Beaver

Pazdur

. Promoting inclusion of members of racial and ethnic minority groups in cancer drug development. JAMA Oncol. 2021;7(10):1445-1446.

37.

Carpten

Fashoyin-Aje

Garraway

, et al. Making cancer research more inclusive. Nat Rev Cancer. 2021;21(10):613-618.

38.

Coalition for Clinical Trials Awareness. Coalition for Clinical Trials Awareness Home, http://cctawareness.org/ (accessed 8 November 2022).

39.

Center for Information & Study on Clinical Research Participation. Home, https://www.ciscrp.org/ (accessed 8 November 2022).

40.

Magnuson

Bruinooge

Singh

, et al. Modernizing clinical trial eligibility criteria: recommendations of the ASCO-friends of cancer research performance status work group. Clin Cancer Res 2021; 27: 2424-2429.

Supplementary Material

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