Skip to main content
Download PDF
  • Study protocol
  • Open access
  • Published:

Partnering around cancer clinical trials (PACCT): study protocol for a randomized trial of a patient and physician communication intervention to increase minority accrual to prostate cancer clinical trials

BMC Cancer volume 17, Article number: 807 (2017) Cite this article

Abstract

Background

Cancer clinical trials are essential for testing new treatments and represent state-of-the-art cancer treatment, but only a small percentage of patients ever enroll in a trial. Under-enrollment is an even greater problem among minorities, particularly African Americans, representing a racial/ethnic disparity in cancer care. One understudied cause is patient-physician communication, which is often of poor quality during clinical interactions between African-American patients and non-African-American physicians. Partnering Around Cancer Clinical Trials (PACCT) involves a transdisciplinary theoretical model proposing that patient and physician individual attitudes and beliefs and their interpersonal communication during racially discordant clinical interactions influence outcomes related to patients’ decisions to participate in a trial. The overall goal of the study is to test a multilevel intervention designed to increase rates at which African-American and White men with prostate cancer make an informed decision to participate in a clinical trial.

Methods/design

Data collection will occur at two NCI-designated comprehensive cancer centers. Participants include physicians who treat men with prostate cancer and their African-American and White patients who are potentially eligible for a clinical trial. The study uses two distinct research designs to evaluate the effects of two behavioral interventions, one focused on patients and the other on physicians. The primary goal is to increase the number of patients who decide to enroll in a trial; secondary goals include increasing rates of physician trial offers, improving the quality of patient-physician communication during video recorded clinical interactions in which trials may be discussed, improving patients’ understanding of trials offered, and increasing the number of patients who actually enroll. Aims are to 1) determine the independent and combined effects of the two interventions on outcomes; 2) compare the effects of the interventions on African-American versus White men; and 3) examine the extent to which patient-physician communication mediates the effect of the interventions on the outcomes.

Discussion

PACCT has the potential to identify ways to increase clinical trial rates in a diverse patient population. The research can also improve access to high quality clinical care for African American men bearing the disproportionate burden of disparities in prostate and other cancers.

Trial registration

Clinical Trials.gov registration number: NCT02906241 (September 8, 2016).

Peer Review reports

Background

Cancer clinical trials are essential for testing the safety and efficacy of promising treatments and translating new knowledge into tangible benefits for patients; they also represent state-of-the art treatment for individuals with cancer [1, 2]. However, only a small percentage of cancer patients ever enroll in a trial [3, 4]. Estimates of the proportion of trials that fail to meet scientific objectives because of insufficient accrual range from 22 to 50% [5, 6]. Low accrual jeopardizes researchers’ ability to assess the safety and effectiveness of new approaches to cancer care, wastes resources, and precludes follow-up studies [6, 7].

Despite NIH requirements to include minorities in clinical research, [8] under-enrollment is an even greater problem among minorities, particularly African Americans [4, 9,10,11,12,13]. Minority under-enrollment can limit the generalizability of findings to those racial/ethnic groups studied [10, 13, 14]. Further, given the National Academy of Science’s recommendation that every individual with cancer should have access to high quality clinical trials [2], minority under-enrollment represents a racial/ethnic disparity in cancer treatment that may lead to disparities in outcomes and survival [1, 15, 16].

Under-enrollment of African Americans and other minorities is often attributed to patients’ negative attitudes toward trials [17,18,19], but research suggests a more complicated picture [13, 20,21,22,23]. National and system factors, such as a lack of available trials, strict eligibility criteria, and competing demands on under-resourced hospitals also present significant barriers that likely have a disproportionate effect on minority enrollment [2, 9, 21, 24,25,26,27]. Several national, regional, and consortia efforts are addressing either patient or system factors [13, 22, 28, 29]. However, even when medical institutions have an adequate trial infrastructure and trials are available, physicians are often unwilling or unprepared to discuss trials with some patients, and patients are often mistrustful of physicians or of trials, especially racial/ethnic minority patients [27].

Partnering Around Cancer Clinical Trials (PACCT) is a behavioral intervention based on a conceptual model (Fig. 1) that translates theories from social psychology and communication science to address the critical need to increase minority participation in clinical trials. The conceptual model proposes that patient and physician individual attitudes and beliefs prior to a clinic visit and their interpersonal communication during the clinic visit interact to directly and indirectly influence outcomes related to patients’ decisions about trial participation. The conceptual model provides a theoretical framework for the intervention designed to improve rates of clinical trial participation among African-American and White men with prostate cancer. The following paragraphs describe the conceptual model.

Fig. 1
figure 1

Conceptual Model

Full size image

As illustrated in Fig. 1, the quality of patient-physician communication during clinical interactions is considered the most central and proximal influence on patients’ decisions about participating in trials. We focus on communication for two reasons: first, because it is through these interpersonal processes among health care organizations, providers, patients, and families that health care is transacted [30, 31]; and second, because communication during clinical interactions with African-American patients and non-African-American physicians (i.e., racially discordant interactions) has been shown in our and others’ research to be lower in quality than in comparable clinical interactions with White patients [32,33,34,35,36,37,38,39,40]. This is particularly important because very few oncologists are African American, and thus oncology interactions for African-American patients are almost always racially discordant [41].

Our model suggests that patients’ and physicians’ individual attitudes and beliefs prior to a clinical interaction directly and indirectly affect the quality of communication during the interaction, and in turn, affect decisions that physicians make about discussing trials and that patients make about participating in the trial. These attitudes and beliefs include those that prior research shows may affect the quality of communication during clinical interactions in which trials are discussed. With regard to African-American patients, research shows that overall, members of this racial group are as likely as White patients to consent if they are offered a trial [18, 26, 42,43,44]. However, some African Americans hold race-related attitudes and beliefs that could directly and indirectly influence whether and how a physician discusses a trial with them and how they respond to these discussions [17, 45,46,47]. These attitudes, derived in great part from the legacy of racism and poorer health care for minorities in the U.S [48,49,50], include greater mistrust in medical institutions and physicians, higher suspicion about how healthcare systems treat African-American patients, and increased perceptions of having been the target of discrimination [51,52,53,54,55,56,57,58,59]. Our work and that of others show that these attitudes lower the quality of communication in interactions with African-American patients and their non-African-American physicians [36, 53, 59], and may, in part, explain why communication in these racially discordant interactions is often of lower quality compared to communication with White patients. With regard to physicians, research suggests that some physicians have negative feelings about African-American patients [60] and may believe they are poor candidates for clinical trials because of racial stereotypes that they are less educated, less trustworthy, or less compliant [61,62,63,64]. These attitudes, which are often implicit rather than explicit, could influence whether and how a physician discusses a trial, and could also result in physicians opting for less aggressive treatments for African American patients [65,66,67].

As also illustrated in Fig. 1, the conceptual model focuses on the quality of patient-physician communication during clinical interactions as a primary influence on patients’ decisions about participating and their understanding of the key aspects of the trial. In PACCT, we are primarily concerned with aspects of communication that may be affected by the topic of clinical trials, and that may vary with patient race. These aspects of communication include patient active participation in clinical interactions (e.g., asking questions, stating concerns) [68, 69], physician patient-centeredness (e.g., patient-centered communication, shared decision making) [31, 70], and the extent to which physicians discuss a trial and clearly explain key aspects of consent (e.g., purpose, risks, benefits of trial participation) [38, 71].

Based on the conceptual model, and consistent with recent calls to move beyond single-level interventions [72,73,74], PACCT will test two interventions: one focused on patients and the other on physicians. Independently and together, these interventions are designed to influence patients’ attitudes about physicians and about trials; physicians’ attitudes about patients and about trials; and patient-physician clinical interactions in which trials may be discussed. The primary goal is to improve the rates at which men decide to participate in a prostate cancer clinical trial, based on high-quality communication with their physicians. Secondary goals are to improve rates at which physicians discuss and offer trials to eligible patients, the quality of patient-physician communication during interactions in which trials may be discussed, patients’ understanding of trials offered, and rates of actual accrual to clinical trials. More specifically, PACCT is designed to achieve the following aims and test the following hypotheses:

  • Aim 1. Determine the effects of the patient- and physician-focused interventions on outcomes. The primary outcome is improved rates of patients’ decisions to enroll in a clinical trial; the secondary outcomes are physicians’ offers of a trial, the quality of patient-physician communication during clinical interactions, patients’ understanding of the trial offered, and patients’ actual enrollment in the trial.

    1. a)

      Determine the effects of the patient-focused intervention on outcomes. Hypothesis 1a: Outcomes will be significantly improved in the patient intervention group, relative to a usual care group.

    2. b)

      Determine the effects of physician-focused intervention on outcomes. Hypothesis 1b: Outcomes will be significantly improved for patients after the physician intervention, as compared to outcomes before the physician intervention.

    3. c)

      Determine the combined effects of the two interventions on outcomes. Hypothesis 1c: There will be a significant multiplicative effect of the two interventions that yield improvements in primary and secondary outcomes over and above the independent effects of each intervention.

  • Aim 2. Compare the effects of the interventions on outcomes for African American versus White men. Hypothesis 2: The effects of the two interventions will be significantly greater among African American than White men.

  • Aim 3. Examine the extent to which patient-physician communication mediates the relationship between the intervention and outcomes. Hypothesis 3: The quality of communication will mediate the effects of the patient and physician intervention on trial offers, and, in turn, on patient understanding of trials offered and decisions to participate. Because the specific meditational variables to be tested will emerge from the analyses related to the first two hypotheses, this is an exploratory hypothesis.

Methods/Design

Study design

PACCT is a clinical trial involving two behavioral interventions, one focused on patients and the other on physicians, each evaluated with a distinct research design. The patient-focused intervention is evaluated with a between-subjects randomized controlled trial in which patients are randomized to an intervention or usual care group, and outcomes are compared between groups. The physician-focused intervention is evaluated with a within-subjects interrupted time series design in which physicians participate during a pre-intervention period (approximately 20 months) followed by the intervention (2 months), and then a post-intervention period (approximately 20 months). In order to assess change, the planned outcomes are assessed prior to and then following the intervention.

Participants and setting

PACCT will be conducted at two National Cancer Institute-designated comprehensive cancer centers: Wayne State University/Karmanos Cancer Institute (WSU/KCI) in Detroit, Michigan, and John Hopkins Medicine/Sidney Kimmel Comprehensive Cancer Center (SKCCC) in Baltimore, Maryland. Physicians (medical oncologists, urologists, and radiation oncologists) are eligible to participate if they regularly treat patients with prostate cancer at one of the two research sites and can recruit patients to available trials. Adult patients are eligible to participate if they have a confirmed diagnosis of prostate cancer; self-identify as Black, African American, or White and non-Hispanic; have been seeing a participating oncologist for less than a year and expect to see the physician at least once in the following year; are able to read and write English well enough to understand the consent documents and respond to questionnaire; and are potentially eligible for a clinical trial within two years of consent.

Procedures

Physicians

Up to 24 physicians will be recruited at the beginning of data collection, prior to patient recruitment. To recruit physicians, research staff will attend departmental meetings to explain the study, and then invite interested physicians to meet individually to answer questions and obtain consent. Physicians who consent will agree to complete baseline measures, to inform their eligible patients about this study during a regularly scheduled clinic visit, to allow video recording of selected patient visits, to complete a brief questionnaire after video recorded patient visits, and to participate in a training intervention in approximately two years. Physicians will continue their participation throughout the study period (approximately 4 years). Baseline measures (see Table 1) will include socio-demographic characteristics, attitudes toward trials and toward the patient-physician relationship, and widely-used assessments of explicit and implicit racial attitudes about African-American and White people. Post-interaction measures will assess physicians’ perceptions of patients and whether a trial was discussed. Physicians receive a $50 gift card for their participation in the study.

Table 1 Study measures
Full size table

Patients

Patient procedures are illustrated in Fig. 2. Up to 440 patients will be recruited in two waves, the first half immediately following physician consent and the second half immediately following the physician intervention. Within each wave, equal numbers of African-American and White patients will be recruited. Up to 16 patients will be recruited per physician in each wave. Research staff will identify eligible patients who have an appointment with a participating physician. Physicians (or their designee) will inform these patients about the study. Research staff will meet with interested patients to explain the study, obtain consent, and have them complete a brief questionnaire (see Table 1). Patients will receive a $20 gift card at this time. Research staff will then track patients until they become potentially eligible for an available clinical trial and have a scheduled appointment with a participating physician. Patients who do not become eligible for a clinical trial during the study period will have no further contact with research staff. Patients who are found to be potentially eligible for a trial will be asked to participate in up to four more study sessions.

Fig. 2
figure 2

Flow Diagram of Patient Enrollment, Randomization, and Procedures

Full size image

Time 1 (prior to clinic visit)

When research staff determines that a participating patient is potentially eligible for an available clinical trial and has an appointment with a participating physician, they will contact him approximately one week before the appointment, remind him about the study, and arrange to meet with him at a convenient time and place to complete a questionnaire (see Table 1). The research staff will NOT directly inform patients about their potential trial eligibility; if asked, they will encourage patients to discuss this with their physician. Once the questionnaire is completed, an automated computer program provided by Qualtrics@ will randomly assign the patient to either the usual care or intervention group (1:1). Intervention group patients will receive the intervention (i.e., booklet and instructions) at this time. All patients will receive a $20.00 gift card and be told that their next clinic visit may be video recorded.

Time 2 (clinic visit)

On the day of the clinic visit, research staff will meet with patients to remind them that the visit will be video recorded and to ask patients to complete brief questionnaires just prior to and following the visit (see Table 1). If family members or companions are present, they will be told about the study and asked for consent to be video recorded, but will not complete any questionnaires. Similarly, clinical staff who will be in the exam room during video recording will be asked for consent. Patients will receive a $10.00 gift card following this visit. Patients will be asked whether they were offered a clinical trial; if they were not, they will be told that they are still in the study and may be contacted again in the future. They will continue to be tracked for up to a total of four visits or until a trial is offered. If they still receive no offer after a fourth visit, they will no longer be tracked. If they are offered a trial, they will proceed to Time 3.

Time 3 (follow-up interview)

A week after the visit, research staff will contact patients (on the phone or in person as convenient to patients) who were offered a trial to conduct a brief interview (see Table 1). Patients will receive a $10.00 gift card at the end of this interview.

Time 4 (medical record review)

Research staff will examine patient medical records to identify potential covariates to be included in the analysis, such as patients’ disease status and co-morbidities. Staff will also determine whether patients completed procedures for trial enrollment and/or enrolled in a trial; and trial characteristics (e.g., difficulty, complexity).

Interventions

Patient intervention

The patient-focused intervention includes both attitude and communication components and is in the form of a booklet. The first section, the attitude component, is based on the well-researched Common Ingroup Identity Model [75, 76]. Extensive research shows that establishing a sense of common identity or purpose between interaction participants increases cooperation and trust among members of different social groups. Briefly, the booklet tells patients that they and their physicians have equally important roles and need to work together as a team to provide the best care for the patient’s cancer. Research assistants will briefly review this section with patients randomized to the intervention group and ask them to place their initials at the bottom of the page to confirm their role as member of the patient-doctor team. The second section, the communication component, is a Question Prompt List (QPL), which includes instructions and a list of questions related to clinical trials. This communication tool has been used in several settings to encourage and assist patients to participate actively during medical visits [77,78,79]. Patients prepared with a QPL may be more likely to ask questions and state their concerns about trials and/or treatments, potentially enabling a shared decision making process. The QPL for PACCT was adapted from two existing QPLs. The first was a booklet developed in collaboration with patients, oncologists, and community members for use as an intervention in a study of African-American patients facing a discussion with an oncologist about chemotherapy [69, 80]. The second was a QPL developed specifically for use during interactions involving a discussion of clinical trials [81]. After patients have finished reading the “team” component of the booklet, research staff will tell patients that the list was developed by doctors and patients, and that patients might find it helpful during the clinic visit, especially if they discuss a clinical trial with their doctor. The research assistants will be trained NOT to answer questions nor discuss trials, but rather to encourage patients to ask questions during clinic visits. The intervention meetings will be audio-recorded to assess fidelity to the protocol.

Physician intervention

The physician-focused intervention, which begins about 20 months after the start of the overall study, includes two components: a communication and an attitude component. The communication component consists of a web-based training module whose objective is to improve physicians’ communication skills in general (e.g., patient-centeredness, shared decision making) and specific to discussing trials with patients (e.g., key aspects of consent). During the training, physicians will view a video that provides information about the importance of recruiting a diverse population of patients to cancer clinical trials, and reflect on communication skills that facilitate effective patient-centered communication and shared decision-making about trials. Training methods will include brief explanations and discussions and video illustrations.

The training is based on communication theory that suggest that in clinical communication, participants exchange both informational and relational messages [71]; the web-based training will include training in how to provide both. Skill-building in informational communication involves guidelines for discussing information patients need to make an informed decision about participating in a trial based on the International Ethical Guidelines for Biomedical Research Involving Human Subjects prepared by the Council for International Organizations of Medical Sciences (CIOMS) and the World Health Organization (WHO) (https://cioms.ch/wp-content/uploads/2017/01/WEB-CIOMS-EthicalGuidelines.pdf). [82] Skill-building in relational communication involves explanations and illustrations of communication strategies such as using organizing statements, eliciting questions and concerns (e.g., “Ask-Tell-Ask”), using lay language, assessing understanding by using the “teach-back” method, acknowledging and responding directly and empathically to questions and concerns, and using shared-decision making principles [82,83,84,85,86,87].

The attitude component of the intervention will take place after physicians complete the communication component, and is designed to increase the likelihood that physicians will discuss and offer trials to their patients. There are two elements of the attitude component: an attitude accessibility element and a situation-specific plan element. The attitude accessibility element is intended to make positive attitudes about the scientific and clinical benefits of offering a trial more accessible and salient to physicians. The situation-specific plan element is intended to further increase the probability that attitudes will be translated into actions. Together, both elements will be provided to physicians via a brief email a few days before each visit with a participating patient in the second wave (post physician intervention) who is potentially eligible for a clinical trial. The email will ask physicians to rate the clinical and scientific benefits of offering this patient a trial, and to indicate what they will do to prepare each patient for a discussion about trials.

Observational measures (see Table 1)

Trained raters will observe and rate video recorded visits. We will follow procedures used in our prior studies to train raters and ensure acceptable inter-rater reliability. Raters will determine whether a trial was discussed and/or offered and assess the quality of trial-related communication [38]; physician patient-centeredness [37], and patient active participation in the interaction [37].

Sample size calculation/analyses

A randomized controlled trial will be used to evaluate the patient-focused intervention and a within-subjects design to evaluate the physician intervention. However, the outcomes of both interventions will be modeled at the patient level in a single multilevel model (MLM; i.e., patients nested within physicians). This model allows us to simultaneously examine the main effect of each intervention and multiplicative effects of having been exposed to both interventions. We will use binomial logistic models for binary outcomes (e.g., trial offer) and multinomial logistic regression for categorical outcomes (e.g., patients’ self-reported participation decision - “yes”, “no”, “undecided”). We will model other outcomes (specifically, patients’ perceptions of patient-centeredness, trust in physician, team perceptions, active participation, physician patient-centeredness, and patient understanding of informed consent) as continuous variables. We used the person-level multi-site/block trial design within Optimal Design to conduct power analyses because the unit of analysis is the patient-physician visit and data from these visits will likely be more similar within physicians than between physicians. The first power analysis is based on the 216 patients who are found to be eligible for a clinical trial and randomized to receive the intervention or usual care (See Fig. 2). We define the primary outcome of our study as patients’ decisions to enroll in a clinical trial. Aim 1 is to examine the extent to which the patient- and physician-focused interventions affect patients’ decisions to enroll, and thus we seek a sample size that gives us sufficient power to detect both the main effect of intervention and important interaction effects. We chose the power analysis in General Estimating Equations (GEE) for nested binomial outcomes with within-cluster treatments [88] as the best available model to estimate power for our primary outcome; such estimates are lacking for Hierarchical Linear Modeling (HLM) models. With 24 physicians and a miniumum of 9 patients per physician who are eligible for a clinical trial (i.e., a minimum of 216 patients for whom outcome measures can be obtained), a Type I error rate (α) of .05, and ICC of .05, and probability of success under the null hypotheses (pH0) of .25, we are well powered to detect pH1 of .35 (b = 0.48, odds-ratio = 1.61) with power > .99. Aim 2 is to examine whether patient race influences the effectiveness of both patient- and physician-focused interventions on our primary outcome; and we remain well powered to detect 2-way and 3-way interactions involving intervention condition and patient race. We will also examine effects of the interventions, and between-race differences in effects of the interventions on other binary or continuous secondary outcomes (e.g., trial offers, patients’ perceptions of patient-centeredness, trust in physician, etc.). For the continuous outcomes, we used block person-randomized trial module in Optimal Design [89] to estimate power. Considering each of the 24 physicians as “blocks” and assuming a minimum of 9 patients per physician, a Type I error rate (α) of .05, between-physicians variability in effect size (\( {\upsigma}_{\updelta}^2 \)) of .05, 5% of variance in outcomes due to physicians and a medium effect size (d) of .50, power to find effects exceeds .90. Our final objective (Aim 3) is to explore the extent to which patient-physician communication mediates the effects of the interventions on the outcomes. We will use Multi-level Structural Equation Modeling (MSEM) that control for patient-physician nesting to fit path analyses. The specific structure (i.e. direct and indirect paths of the models) will be guided by results from analyses conducted for our first and second aims. We therefore consider the MSEM exploratory in that we are the first researchers to examine these effects in this context. Thus, at this point we lack the specification of the model parameters needed to provide accurate estimates of power for this exploratory aim.

Discussion

PACCT is highly significant in several ways. First, it can increase clinical trial participation rates of African-American and White men with prostate cancer, thus improving the generalizability of findings from these trials to a diverse patient population. Second, the research will provide empirical data regarding the theroretical mechanisms through which the interventions affect outcomes. Third, the design will provide descriptive information which is currently unavailable on the proportion of patients with prostate cancer who are eligible for a trial, are offered a trial, agree to participate, and/or enroll. Fourth, findings can inform the development of future interventions to improve trial enrollment of other underrepresented populations (e.g., Hispanic patients, older patients) and in other contexts. Fifth, multilevel interventions have the potential to achieve substantial and sustained change, and to produce effects that are at least additive and possibly multiplicative. Finally, this research directly addresses racial disparities in cancer care by improving access to high quality clinical care for African American men suffering the disproportionate burden of disparities in prostate and other cancers.

Although there are several strengths of the study, PACCT has some potential limitations. One of these is the focus on physicians, rather than on other members of the health care team, such as research nurses, who are clearly critical to enrolling patients in clinical trials. However, PACCT focuses on physicians because they make the final decision about the clinical appropriateness of a trial for a specific patient and are generally responsible for introducing the study to patients [90]. Also, patients consider physicians to be their primary and preferred source of information [71, 91,92,93,94]. Thus, physicians can present a primary barrier or facilitator to the enrollment process.

Another potential limitation is the focus on African Americans rather than on members of other minority groups. African Americans are the focus of this study primarily because members of this community bear the disproportionate burden of prostate and other cancers, as compared to White patients [95]. Increasing participation rates of African-American men with prostate cancer is particularly important because of the higher incidence, morbidity, and mortality rates among African-American men as compared to White men [96]. Additionally, the conceptual model and preliminary data upon which PACCT is based focus on research specific to African Americans. However, a strength of this study is that it will provide evidence for interventions and the mechanisms through which these interventions affect outcomes; this research can therefore inform interventions to benefit other minority communities in the future.

Abbreviations

CIOMS:

Council for International Organizations of Medical Sciences

GEE:

General Estimating Equation

HLM:

Hierarchical Linear Modeling

KCI:

Karmanos Cancer Institute

MLM:

Multilevel Model

MSEM:

Multi-level Structural Equation Modeling

NCI:

National Cancer Institute

PACCT:

Partnering Around Cancer Clinical Trials

QPL:

Question Prompt List

SKCCC:

Sidney Kimmel Comprehensive Cancer Center

WHO:

World Health Organization

WSU:

Wayne State University

References

  1. Newman LA, Roff NK, Weinberg AD. Cancer clinical trials accrual: missed opportunities to address disparities and missed opportunities to improve outcomes for all. Ann Surg Oncol. 2008;15(7):1818–9.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Nass SJ, Moses HL, Mendelsohn J. A National Cancer Clinical trials system for the 21st century: reinvigorating the NCI cooperative group program. Institute of Medicine, Committee on Cancer Clinical Trials and the NCI cooperative group. Program; 2010.

  3. Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA. 2004;291(22):2720–6.

    Article  CAS  PubMed  Google Scholar 

  4. Stewart JH, Bertoni AG, Staten JL, Levine EA, Gross CP. Participation in surgical oncology clinical trials: gender-, race/ethnicity-, and age-based disparities. Ann Surg Oncol. 2007;14(12):3328–34.

    Article  PubMed  Google Scholar 

  5. Cheng SK, Dietrich MS, Dilts DM. A sense of urgency: evaluating the link between clinical trial development time and the accrual performance of cancer therapy evaluation program (NCI-CTEP) sponsored studies. Clin Cancer Res. 2010;16(22):5557–63.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Korn EL, Freidlin B, Mooney M, Abrams JS. Accrual experience of National Cancer Institute cooperative group phase III trials activated from 2000 to 2007. J Clin Oncol. 2010;28(35):5197–201.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Penner LA, Manning M, Eggly S, Albrecht TL. Prosocial Behavior in Cancer Research: Patient Participation in Cancer Clinical Trials. In: Graziano B, Schroeder D, editors. Handbook of Prosocial Behavior. Oxford, UK: Oxford University Press; 2014. p. 653–69.

    Google Scholar 

  8. Freedman LS, Simon R, Foulkes MA, Friedman L, Geller NL, Gordon DJ, Mowery R. Inclusion of women and minorities in clinical trials and the NIH Revitalization Act of 1993--the perspective of NIH clinical trialists. Control Clin Trials. 1995;16(5):277–85. discussion 286–279, 293–309

    Article  CAS  PubMed  Google Scholar 

  9. Ford JG, Howerton MW, Lai GY, Gary TL, Bolen S, Gibbons MC, Tilburt J, Baffi C, Tanpitukpongse TP, Wilson RF, et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer. 2008;112(2):228–42.

    Article  PubMed  Google Scholar 

  10. Wissing MD, Kluetz PG, Ning YM, Bull J, Merenda C, Murgo AJ, Pazdur R. Under-representation of racial minorities in prostate cancer studies submitted to the US Food and Drug Administration to support potential marketing approval, 1993-2013. Cancer. 2014;120(19):3025–32.

    Article  PubMed  Google Scholar 

  11. Pang HH, Wang X, Stinchcombe TE, Wong ML, Cheng P, Ganti AK, Sargent DJ, Zhang Y, Hu C, Mandrekar SJ, et al. Enrollment Trends and Disparity Among Patients With Lung Cancer in National Clinical Trials, 1990 to 2012. J Clin Oncol. 2016;34(33):3992–9.

    Article  PubMed Central  Google Scholar 

  12. Fouad MN. Enrollment of minorities in clinical trials: did we overcome the barriers? Contemp Clin Trials. 2009;30(2):103–4.

    Article  PubMed  Google Scholar 

  13. Banda DR, Germain DS, McCaskill-Stevens W, Ford JG, Swain SM. A critical review of the enrollment of black patients in cancer clinical trials. Am Soc Clin Oncol Educ Book. 2012:153–7.

  14. Carpenter WR, Tyree S, Wu Y, Meyer AM, DiMartino L, Zullig L, Godley PA. A surveillance system for monitoring, public reporting, and improving minority access to cancer clinical trials. Clin Trials. 2012;9(4):426–35.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bolen S, Tilburt J, Baffi C, Gary TL, Powe N, Howerton M, Ford J, Lai G, Wilson R, Bass E. Defining “success” in recruitment of underrepresented populations to cancer clinical trials: moving toward a more consistent approach. Cancer. 2006;106(6):1197–204.

    Article  PubMed  Google Scholar 

  16. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin. 2011;61(4):212–36.

    Article  PubMed  Google Scholar 

  17. Rivers D, August EM, Sehovic I, Lee Green B, Quinn GP. A systematic review of the factors influencing African Americans' participation in cancer clinical trials. Contemp Clin Trials. 2013;35(2):13–32.

    Article  PubMed  Google Scholar 

  18. Svensson K, Ramirez OF, Peres F, Barnett M, Claudio L. Socioeconomic determinants associated with willingness to participate in medical research among a diverse population. Contemp Clin Trials. 2012;33(6):1197–205.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mills EJ, Seely D, Rachlis B, Griffith L, Wu P, Wilson K, Ellis P, Wright JR. Barriers to participation in clinical trials of cancer: a meta-analysis and systematic review of patient-reported factors. Lancet Oncol. 2006;7(2):141–8.

  20. Paskett ED, Reeves KW, McLaughlin JM, Katz ML, McAlearney AS, Ruffin MT, Halbert CH, Merete C, Davis F, Gehlert S. Recruitment of minority and underserved populations in the United States: the centers for population health and health disparities experience. Contemp Clin Trials. 2008;29(6):847–61.

    Article  PubMed  PubMed Central  Google Scholar 

  21. McCaskill-Stevens W, Pinto H, Marcus AC, Comis R, Morgan R, Plomer K, Schoentgen S. Recruiting minority cancer patients into cancer clinical trials: a pilot project involving the eastern cooperative oncology group and the National Medical Association. J Clin Oncol. 1999;17(3):1029–39.

    Article  CAS  PubMed  Google Scholar 

  22. Diehl KM, Green EM, Weinberg A, Frederick WA, Holmes DR, Green B, Morris A, Kuerer HM, Beltran RA, Mendez J, et al. Features associated with successful recruitment of diverse patients onto cancer clinical trials: report from the American College of Surgeons oncology group. Ann Surg Oncol. 2011;18(13):3544–50.

    Article  PubMed  Google Scholar 

  23. Du W, Gadgeel SM, Simon MS. Predictors of enrollment in lung cancer clinical trials. Cancer. 2006;106(2):420–5.

    Article  PubMed  Google Scholar 

  24. Weiner BJ, Jacobs SR, Minasian LM, Good MJ. Organizational designs for achieving high treatment trial enrollment: a fuzzy-set analysis of the community clinical oncology program. J Oncol Pract. 2012;8(5):287–91.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Somkin CP, Altschuler A, Ackerson L, Geiger AM, Greene SM, Mouchawar J, Holup J, Fehrenbacher L, Nelson A, Glass A, et al. Organizational barriers to physician participation in cancer clinical trials. Am J Manag Care. 2005;11(7):413–21.

    PubMed  Google Scholar 

  26. Langford AT, Resnicow K, Dimond EP, Denicoff AM, Germain DS, McCaskill-Stevens W, Enos RA, Carrigan A, Wilkinson K, Go RS. Racial/ethnic differences in clinical trial enrollment, refusal rates, ineligibility, and reasons for decline among patients at sites in the National Cancer Institute's community cancer centers program. Cancer. 2014;120(6):877–84.

    Article  PubMed  Google Scholar 

  27. Hamel LM, Penner LA, Albrecht TL, Heath E, Gwede CK, Eggly S. Barriers to clinical trial enrollment in racial and ethnic minority patients with cancer. Cancer Control. 2016;23(4):327–37.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wujcik D, Wolff SN. Recruitment of African Americans to National Oncology Clinical Trials through a clinical trial shared resource. J Health Care Poor Underserved. 2010;21(1 Suppl):38–50.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Minasian LM, Carpenter WR, Weiner BJ, Anderson DE, McCaskill-Stevens W, Nelson S, Whitman C, Kelaghan J, O'Mara AM, Kaluzny AD. Translating research into evidence-based practice: the National Cancer Institute Community clinical oncology program. Cancer. 2010;116(19):4440–9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Albrecht TL, Penner LA, Cline RJ, Eggly SS, Ruckdeschel JC. Studying the process of clinical communication: issues of context, concepts, and research directions. J Health Commun. 2009;14(Suppl 1):47–56.

    Article  PubMed  Google Scholar 

  31. Epstein RM, Street RL Jr. Patient-centered communication in cancer care: promoting healing and reducing suffering. Bethesda, MD: National Cancer Institute; 2007.

    Google Scholar 

  32. Eggly S, Harper FW, Penner LA, Gleason MJ, Foster T, Albrecht TL. Variation in question asking during cancer clinical interactions: a potential source of disparities in access to information. Patient Educ Couns. 2011;82(1):63–8.

    Article  PubMed  Google Scholar 

  33. Johnson RL, Roter D, Powe NR, Cooper LA. Patient race/ethnicity and quality of patient-physician communication during medical visits. Am J Public Health. 2004;94(12):2084–90.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gordon HS, Street RL Jr, Sharf BF, Souchek J. Racial differences in doctors' information-giving and patients' participation. Cancer. 2006;107(6):1313–20.

    Article  PubMed  Google Scholar 

  35. Siminoff LA, Graham GC, Gordon NH. Cancer communication patterns and the influence of patient characteristics: disparities in information-giving and affective behaviors. Patient Educ Couns. 2006;62(3):355–60.

    Article  PubMed  Google Scholar 

  36. Gordon HS, Street RL Jr, Sharf BF, Kelly PA, Souchek J. Racial differences in trust and lung cancer patients' perceptions of physician communication. J Clin Oncol. 2006;24(6):904–9.

    Article  PubMed  Google Scholar 

  37. Street RL Jr, Gordon H, Haidet P. Physicians' communication and perceptions of patients: is it how they look, how they talk, or is it just the doctor? Soc Sci Med. 2007;65(3):586–98.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Eggly S, Barton E, Winckles A, Penner LA, Albrecht TL. A disparity of words: racial differences in oncologist-patient communication about clinical trials. Health Expect. 2015;18(5):1316–26.

    Article  PubMed  Google Scholar 

  39. Cooper LA, Roter DL, Johnson RL, Ford DE, Steinwachs DM, Powe NR. Patient-centered communication, ratings of care, and concordance of patient and physician race. Ann Intern Med. 2003;139(11):907–15.

    Article  PubMed  Google Scholar 

  40. Cooper LA, Roter DL, Carson KA, Beach MC, Sabin JA, Greenwald AG, Inui TS. The associations of clinicians' implicit attitudes about race with medical visit communication and patient ratings of interpersonal care. Am J Public Health. 2012;102(5):979–87.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hamel LM, Chapman R, Malloy M, Eggly S, Penner LA, Shields AF, Simon MS, Klamerus JF, Schiffer C, Albrecht TL. Critical shortage of African American medical oncologists in the United States. J Clin Oncol. 2015;33(32):3697–700.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wendler D, Kington R, Madans J, Van Wye G, Christ-Schmidt H, Pratt LA, Brawley OW, Gross CP, Emanuel E. Are racial and ethnic minorities less willing to participate in health research? PLoS Med. 2006;3(2):e19.

    Article  PubMed  Google Scholar 

  43. Katz RV, Green BL, Kressin NR, Claudio C, Wang MQ, Russell SL. Willingness of minorities to participate in biomedical studies: confirmatory findings from a follow-up study using the Tuskegee legacy project questionnaire. J Natl Med Assoc. 2007;99(9):1052–60.

    PubMed  PubMed Central  Google Scholar 

  44. Nodora JN, Komenaka IK, Bouton ME, Ohno-Machado L, Schwab R, Kim HE, Farcas C, Perez G, Elena Martinez M. Biospecimen sharing among Hispanic women in a safety-net clinic: implications for the precision medicine initiative. J Natl Cancer Inst. 2017;109(2)

  45. Evans KR, Lewis MJ, Hudson SV. The role of health literacy on African American and Hispanic/Latino perspectives on cancer clinical trials. J Cancer Educ. 2012;27(2):299–305.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Shavers VL, Lynch CF, Burmeister LF. Racial differences in factors that influence the willingness to participate in medical research studies. Ann Epidemiol. 2002;12(4):248–56.

    Article  PubMed  Google Scholar 

  47. Corbie-Smith G, Viscoli CM, Kernan WN, Brass LM, Sarrel P, Horwitz RI. Influence of race, clinical, and other socio-demographic features on trial participation. J Clin Epidemiol. 2003;56(4):304–9.

    Article  PubMed  Google Scholar 

  48. Smedley BD, Stith AY, Nelson AR. Unequal treatment: confronting racial and ethnic disparities in health care. Washington DC: National Academies Press; 2003.

    Google Scholar 

  49. Skloot R. The immortal life of Henrietta lacks. New York: Crown Publishers; 2010.

    Google Scholar 

  50. Byrd WM, Clayton LA. Race, medicine, and health care in the United States: a historical survey. J Natl Med Assoc. 2001;93(3 Suppl):11S–34S.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Nguyen GC, LaVeist TA, Harris ML, Datta LW, Bayless TM, Brant SR. Patient trust-in-physician and race are predictors of adherence to medical management in inflammatory bowel disease. Inflamm Bowel Dis. 2009;15(8):1233–9.

    Article  PubMed  PubMed Central  Google Scholar 

  52. O'Malley AS, Sheppard VB, Schwartz M, Mandelblatt J. The role of trust in use of preventive services among low-income African-American women. Prev Med. 2004;38(6):777–85.

    Article  PubMed  Google Scholar 

  53. Penner LA, Dovidio JF, Edmondson D, Dailey RK, Markova T, Albrecht TL, Gaertner SL. The experience of discrimination and black-white health disparities in medical care. J Black Psychol. 2009;35(2):180–203.

    Article  Google Scholar 

  54. Halbert CH, Armstrong K, Gandy OH Jr, Shaker L. Racial differences in trust in health care providers. Arch Intern Med. 2006;166(8):896–901.

    Article  PubMed  Google Scholar 

  55. Halbert CH, Weathers B, Delmoor E, Mahler B, Coyne J, Thompson HS, Have TT, Vaughn D, Malkowicz SB, Lee D. Racial differences in medical mistrust among men diagnosed with prostate cancer. Cancer. 2009;115(11):2553–61.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Boulware LE, Cooper LA, Ratner LE, LaVeist TA, Powe NR. Race and trust in the health care system. Public Health Rep. 2003;118(4):358–65.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Scharff DP, Mathews KJ, Jackson P, Hoffsuemmer J, Martin E, Edwards D. More than Tuskegee: understanding mistrust about research participation. J Health Care Poor Underserved. 2010;21(3):879–97.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Dovidio JF, Penner LA, Albrecht TL, Norton WE, Gaertner SL, Shelton JN. Disparities and distrust: the implications of psychological processes for understanding racial disparities in health and health care. Soc Sci Med. 2008;67(3):478–86.

    Article  PubMed  Google Scholar 

  59. Hagiwara N, Penner LA, Gonzalez R, Eggly S, Dovidio JF, Gaertner SL, West T, Albrecht TL. Racial attitudes, physician-patient talk time ratio, and adherence in racially discordant medical interactions. Soc Sci Med. 2013;87:123–31.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Sabin J, Nosek BA, Greenwald A, Rivara FP. Physicians' implicit and explicit attitudes about race by MD race, ethnicity, and gender. J Health Care Poor Underserved. 2009;20(3):896–913.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Moskowitz D, Thom DH, Guzman D, Penko J, Miaskowski C, Kushel M. Is primary care providers' trust in socially marginalized patients affected by race? J Gen Intern Med. 2011;26(8):846–51.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Sabin JA, Rivara FP, Greenwald AG. Physician implicit attitudes and stereotypes about race and quality of medical care. Med Care. 2008;46(7):678–85.

    Article  PubMed  Google Scholar 

  63. Moskowitz GB, Stone J, Childs A. Implicit stereotyping and medical decisions: unconscious stereotype activation in practitioners' thoughts about African Americans. Am J Public Health. 2012;102(5):996–1001.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Penner LA, Dovidio JF, Gonzalez R, Albrecht TL, Chapman R, Foster T, Harper FW, Hagiwara N, Hamel LM, Shields AF, et al. The effects of oncologist implicit racial bias in racially discordant oncology interactions. J Clin Oncol. 2016;34(24):2874–80.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Green AR, Carney DR, Pallin DJ, Ngo LH, Raymond KL, Iezzoni LI, Banaji MR. Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 2007;22(9):1231–8.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Sabin JA, Greenwald AG. The influence of implicit bias on treatment recommendations for 4 common pediatric conditions: pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma. Am J Public Health. 2012;102(5):988–95.

    Article  PubMed  PubMed Central  Google Scholar 

  67. van Ryn M, Burgess D, Malat J, Griffin J. Physicians' perceptions of patients' social and behavioral characteristics and race disparities in treatment recommendations for men with coronary artery disease. Am J Public Health. 2006;96(2):351–7.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Street RL Jr, Millay B. Analyzing patient participation in medical encounters. Health Commun. 2001;13(1):61–73.

    Article  PubMed  Google Scholar 

  69. Eggly S, Hamel LM, Foster TS, Albrecht TL, Chapman R, Harper FW, Thompson H, Griggs JJ, Gonzalez R, Berry-Bobovski L, et al. Randomized trial of a question prompt list to increase patient active participation during interactions with black patients and their oncologists. Patient Educ Couns. 2017;100(5):818–26.

    Article  PubMed  Google Scholar 

  70. McCormack LA, Treiman K, Rupert D, Williams-Piehota P, Nadler E, Arora NK, Lawrence W, Street RL Jr. Measuring patient-centered communication in cancer care: a literature review and the development of a systematic approach. Soc Sci Med. 2011;72(7):1085–95.

    Article  PubMed  Google Scholar 

  71. Albrecht TL, Eggly SS, Gleason ME, Harper FW, Foster TS, Peterson AM, Orom H, Penner LA, Ruckdeschel JC. Influence of clinical communication on patients' decision making on participation in clinical trials. J Clin Oncol. 2008;26(16):2666–73.

    Article  PubMed  Google Scholar 

  72. Clauser SB, Taplin SH, Foster MK, Fagan P, Kaluzny AD. Multilevel intervention research: lessons learned and pathways forward. J Natl Cancer Inst Monogr. 2012;2012(44):127–33.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Taplin SH, Anhang Price R, Edwards HM, Foster MK, Breslau ES, Chollette V, Prabhu Das I, Clauser SB, Fennell ML, Zapka J. Introduction: understanding and influencing multilevel factors across the cancer care continuum. J Natl Cancer Inst Monogr. 2012;2012(44):2–10.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Stange KC, Breslau ES, Dietrich AJ, Glasgow RE. State-of-the-art and future directions in multilevel interventions across the cancer control continuum. J Natl Cancer Inst Monogr. 2012;2012(44):20–31.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Gaertner S, Dovidio JF. A common ingroup identity: A categorization-based approach for reducing intergroup bias. In: Nelson T, editor. Handbook of Prejudice. Thousand Oaks: Sage; 2009. p. 489–505.

    Google Scholar 

  76. Gaertner S, Dovidio JF, Houlette MA. Social categorization. In: Dovidio JF, Hewstone M, Esses VM, editors. Handbook of Prejudice, Stereotyping, and Discrimination. Thousand Oaks, CA: Sage; 2010. p. 526–43.

    Chapter  Google Scholar 

  77. Brandes K, Linn AJ, Butow PN, van Weert JC. The characteristics and effectiveness of question prompt list interventions in oncology: a systematic review of the literature. Psychooncology. 2015;24(3):245–52.

    Article  PubMed  Google Scholar 

  78. Henselmans I, de Haes HC, Smets EM. Enhancing patient participation in oncology consultations: a best evidence synthesis of patient-targeted interventions. Psychooncology. 2013;22(5):961–77.

    Article  PubMed  Google Scholar 

  79. Sansoni JE, Grootemaat P, Duncan C. Question prompt lists in health consultations: a review. Patient Educ Couns. 2015;

  80. Eggly S, Tkatch R, Penner LA, Mabunda L, Hudson J, Chapman R, Griggs JJ, Brown R, Albrecht T. Development of a question prompt list as a communication intervention to reduce racial disparities in cancer treatment. J Cancer Educ. 2013;28(2):282–9.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Brown RF, Bylund CL, Li Y, Edgerson S, Butow P. Testing the utility of a cancer clinical trial specific question prompt list (QPL-CT) during oncology consultations. Patient Educ Couns. 2012;88(2):311–7.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Wendler D, Grady C. What should research participants understand to understand they are participants in research? Bioethics. 2008;22(4):203–8.

    Article  PubMed  Google Scholar 

  83. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. 1997;44(5):681–92.

    Article  CAS  PubMed  Google Scholar 

  84. Kemp EC, Floyd MR, McCord-Duncan E, Lang F. Patients prefer the method of "tell back-collaborative inquiry" to assess understanding of medical information. J Am Board Fam Med. 2008;21(1):24–30.

    Article  PubMed  Google Scholar 

  85. Kornburger C, Gibson C, Sadowski S, Maletta K, Klingbeil C. Using "teach-back" to promote a safe transition from hospital to home: an evidence-based approach to improving the discharge process. J Pediatr Nurs. 2013;28(3):282–91.

    Article  PubMed  Google Scholar 

  86. Bylund CL, Brown R, Gueguen JA, Diamond C, Bianculli J, Kissane DW. The implementation and assessment of a comprehensive communication skills training curriculum for oncologists. Psychooncology. 2010;19(6):583–93.

    Article  PubMed  Google Scholar 

  87. Back AL, Arnold RM, Baile WF, Tulsky JA, Fryer-Edwards K. Approaching difficult communication tasks in oncology. CA Cancer J Clin. 2005;55(3):164–77.

    Article  PubMed  Google Scholar 

  88. Pan W. Sample size and power calculations with correlated binary data. Control Clin Trials. 2001;22(3):211–27.

    Article  CAS  PubMed  Google Scholar 

  89. Raudenbush SW, Spybrook J, Congdon R, Liu X, Martinez A, Bloom H, Hill C. Optimal Design Plus Empirical Evidence. 3.01 ed. HLM Software, University of Chicago; 2011.

  90. Kanarek NF, Kanarek MS, Olatoye D, Carducci MA. Removing barriers to participation in clinical trials, a conceptual framework and retrospective chart review study. Trials. 2012;13:237.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Hesse BW, Moser RP, Rutten LJ. Surveys of physicians and electronic health information. N Engl J Med. 2010;362(9):859–60.

    Article  CAS  PubMed  Google Scholar 

  92. Hesse BW, Nelson DE, Kreps GL, Croyle RT, Arora NK, Rimer BK, Viswanath K. Trust and sources of health information: the impact of the internet and its implications for health care providers: findings from the first health information National Trends Survey. Arch Intern Med. 2005;165(22):2618–24.

    Article  PubMed  Google Scholar 

  93. Sidana A, Hernandez DJ, Feng Z, Partin AW, Trock BJ, Saha S, Epstein JI. Treatment decision-making for localized prostate cancer: what younger men choose and why. Prostate. 2012;72(1):58–64.

    Article  PubMed  Google Scholar 

  94. Eggly S, Albrecht TL, Harper FW, Foster T, Franks MM, Ruckdeschel JC. Oncologists' recommendations of clinical trial participation to patients. Patient Educ Couns. 2008;70(1):143–8.

    Article  PubMed  Google Scholar 

  95. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7–30.

    Article  PubMed  Google Scholar 

  96. DeSantis CE, Siegel RL, Sauer AG, Miller KD, Fedewa SA, Alcaraz KI, Jemal A. Cancer statistics for African Americans, 2016: progress and opportunities in reducing racial disparities. CA Cancer J Clin. 2016;66(4):290–308.

    Article  PubMed  Google Scholar 

  97. Pisu M, Kenzik KM, Oster RA, Drentea P, Ashing KT, Fouad M, Martin MY. Economic hardship of minority and non-minority cancer survivors 1 year after diagnosis: another long-term effect of cancer? Cancer. 2015;121(8):1257–64.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Benson T, Sizmur S, Whatling J, Arikan S, McDonald D, Ingram D. Evaluation of a new short generic measure of health status: howRu. Inform Prim Care. 2010;18(2):89–101.

    PubMed  Google Scholar 

  99. Chew LD, Griffin JM, Partin MR, Noorbaloochi S, Grill JP, Snyder A, Bradley KA, Nugent SM, Baines AD, Vanryn M. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561–6.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Schwartz KL, Bartoces M, Campbell-Voytal K, West P, Monsur J, Sartor A, Neale AV. Estimating health literacy in family medicine clinics in metropolitan Detroit: a MetroNet study. J Am Board Fam Med. 2013;26(5):566–70.

    Article  PubMed  Google Scholar 

  101. Dugan E, Trachtenberg F, Hall MA. Development of abbreviated measures to assess patient trust in a physician, a health insurer, and the medical profession. BMC Health Serv Res. 2005;5:64.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Thompson HS, Valdimarsdottir HB, Winkel G, Jandorf L, Redd W. The group-based medical mistrust scale: psychometric properties and association with breast cancer screening. Prev Med. 2004;38(2):209–18.

    Article  PubMed  Google Scholar 

  103. Jacobsen PB, Wells KJ, Meade CD, Quinn GP, Lee JH, Fulp WJ, Gray JE, Baz RC, Springett GM, Levine RM, et al. Effects of a brief multimedia psychoeducational intervention on the attitudes and interest of patients with cancer regarding clinical trial participation: a multicenter randomized controlled trial. J Clin Oncol. 2012;30(20):2516–21.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Degner LF, Sloan JA, Venkatesh P. The control preferences scale. Can J Nurs Res. 1997;29(3):21–43.

    CAS  PubMed  Google Scholar 

  105. Krupat E, Rosenkranz SL, Yeager CM, Barnard K, Putnam SM, Inui TS. The practice orientations of physicians and patients: the effect of doctor-patient congruence on satisfaction. Patient Educ Couns. 2000;39(1):49–59.

    Article  CAS  PubMed  Google Scholar 

  106. Mackinnon A, Jorm AF, Christensen H, Korten AE, Jacomb PA, Rodgers B. A short form of the positive and negative affect schedule: evaluation of factorial validity and invariance across demographic variables in a community sample. Pers Indiv Differ. 1999;27(3):405–16.

    Article  Google Scholar 

  107. Brown TN. Measuring self-perceived racial and ethnic discrimination in social surveys. Sociol Spectr. 2001;21:377–92.

    Article  Google Scholar 

  108. Koenig HG, Bussing A. The Duke University religion index (DUREL): a five-item measure for use in Epidemological studies. Religions. 2010;1(1):78–85.

    Article  Google Scholar 

  109. Hodge DR. The intrinsic spirituality scale: a new six-item instrument for assessing the salience of spirituality as a motivational construct. J Soc Serv Res. 2003;30(1):41–61.

    Article  Google Scholar 

  110. Sarason IG, Levine HM, Basham RB, Sarason BR. Assessing social support - the social support questionnaire. J Pers Soc Psychol. 1983;44(1):127–39.

    Article  Google Scholar 

  111. Janz NK, Wren PA, Copeland LA, Lowery JC, Goldfarb SL, Wilkins EG. Patient-physician concordance: preferences, perceptions, and factors influencing the breast cancer surgical decision. J Clin Oncol. 2004;22(15):3091–8.

    Article  PubMed  Google Scholar 

  112. Stewart M, Brown JB, Donner A, McWhinney IR, Oates J, Weston WW, Jordan J. The impact of patient-centered care on outcomes. J family pract. 2000;49(9):796–804.

    CAS  Google Scholar 

  113. Joffe S, Cook EF, Cleary PD, Clark JW, Weeks JC. Quality of informed consent: a new measure of understanding among research subjects. J Natl Cancer Inst. 2001;93(2):139–47.

    Article  CAS  PubMed  Google Scholar 

  114. Penner LA, Dovidio JF, West TV, Gaertner SL, Albrecht TL, Dailey RK, Markova T. Aversive racism and medical interactions with black patients: a field study. J Exp Soc Psychol. 2010;46(2):436–40.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Somkin CP, Altschuler A, Ackerson L, Tolsma D, Rolnick SJ, Yood R, Weaver WD, Von Worley A, Hornbrook M, Magid DJ, et al. Cardiology clinical trial participation in community-based healthcare systems: obstacles and opportunities. Contemp Clin Trials. 2008;29(5):646–53.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Henry PJ, Sears DO. The symbolic racism 2000 scale. Polit Psychol. 2002;23(2):253–83.

    Article  Google Scholar 

  117. Greenwald AG, Nosek BA, Banaji MR. Understanding and using the implicit association test: I. An improved scoring algorithm. J Pers Soc Psychol. 2003;85(2):197–216.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None

Funding

This study is funded by the National Cancer Institute (NCI) R01CA200718–01 and P30 CA022453. The NCI did not contribute to the design of the study, data collection, analysis, interpretation of the data, or in writing of the manuscript.

Availability of data and materials

Not applicable.

Author information

Authors and Affiliations

  1. Department of Oncology, Wayne State University/Karmanos Cancer Institute, 4100 John R, Detroit, MI, 48201, USA

    Susan Eggly, Lauren M. Hamel, Elisabeth Heath, Mark A. Manning, Terrance L. Albrecht, Mark Wojda, Tanina Foster, Seongho Kim, Nicole Senft & Louis A. Penner

  2. Department of English, Wayne State University, 5057 Woodward Suite 9408, Detroit, MI, 48202, USA

    Ellen Barton

  3. Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center, 1M59 Bunting –Blaustein Cancer Research Building, 1650 Orleans Street, Baltimore, MD, 21287, USA

    Michael Carducci

  4. Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center, 550 North Broadway, 1003-G, Baltimore, MD, 21205, USA

    Dina Lansey, Ting Wang, Rehab Abdallah & Narineh Abrahamian

Authors
  1. Susan Eggly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lauren M. Hamel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elisabeth Heath
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark A. Manning
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Terrance L. Albrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ellen Barton
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mark Wojda
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tanina Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Michael Carducci
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dina Lansey
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rehab Abdallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Narineh Abrahamian
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Seongho Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nicole Senft
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Louis A. Penner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have read and approved the manuscript. SE: Principal Investigator, involved in all aspects of conceptualization and study design. LMH: Co-Investigator, involved in all aspects of conceptualization and study design. EH: Co-Investigator, involved in all aspects of conceptualization and study design. Responsible for implementation of the study at Karmanos Cancer Institute/Wayne State University. MAM: Co-Investigator, involved in all aspects of conceptualization and study design. Responsible for design and implementation of statistical analyses. TLA: Co-Investigator, involved in all aspects of conceptualization and study design. EB: Co-Investigator, involved in all aspects of conceptualization and study design. Specifically involved in video analysis procedures. MW: Project Director, responsible for oversight of all study procedures at both data collection sites. TF: Data Manager, responsible for managing quantitative and qualitative data. MC: Co-Investigator, involved in all aspects of conceptualization and study design. Site principal investigator, responsible for implementation of the study at Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center. DL: Co-Investigator, involved in all aspects of conceptualization and study design. Responsible for implementation of the study at Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center. TW: Responsible for implementation of study procedures at Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center. RA: Responsible for implementation of study procedures at Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center. NA: Responsible for implementation of study procedures at Johns Hopkins School of Medicine/Sidney Kimmel Comprehensive Cancer Center. SK: Biostatician, responsible for design and implementation of statistical analyses. NS: Post-doctoral fellow, responsible for selection and analysis of participant self-report measures. LAP: Co-Investigator, involved in all aspects of conceptualization and study design.

Corresponding author

Correspondence to Lauren M. Hamel.

Ethics declarations

Ethics approval and consent to participate

This study and all procedures were approved by the Institutional Review Boards of Wayne State University and Johns Hopkins University. This is a report of a study protcol, and thus human subject consent was not necessary. In order to participate, all participants will provide written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eggly, S., Hamel, L.M., Heath, E. et al. Partnering around cancer clinical trials (PACCT): study protocol for a randomized trial of a patient and physician communication intervention to increase minority accrual to prostate cancer clinical trials. BMC Cancer 17, 807 (2017). https://doi.org/10.1186/s12885-017-3804-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-017-3804-5

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us