African American Screening and Enrollment in the CLEAR III Trial

Clinical Researcher—August 2018 (Volume 32, Issue 7)


Karen Lane, CCRP; Maningbe Keita, BA; Radhika Avadhani, MS; Rachel Dlugash, MSPH; Steven Mayo, PD, CCRA, PMP; Richard E. Thompson, PhD; Issam A. Awad, MD, MSc, FACS, FAHA; Nichol McBee, MPH, CCRP; Wendy Ziai, MD; Daniel F. Hanley


By 2050, members of racial and ethnic minorities will represent the majority of the population in the United States.{1} While clinical trials are designed to inform the scientific workforce about the safety, efficacy, and effectiveness of medical strategies, treatments, or devices for evidence-based healthcare decision-making, the under-enrollment of minority patients reduces the generalizability of research findings.{2} Enrolling an adequate proportion of minorities into clinical trials has proven difficult in the past; however, concerted efforts must be made to overcome barriers to enrollment.{3–12} Proportional recruitment practices can provide data about health disparities and better serve the needs of minority populations.

Such is the case with hemorrhagic stroke, a devastating disease with a global mortality of 45%. Recent estimates indicate that 70,000 new hemorrhages occur in the United States each year.{13} Minority patients are disproportionally affected in incidence and severity; African Americans, particularly, have a greater risk, incidence, prevalence, and mortality compared to white Americans.{14–26} Not only does this evidence contribute to the overwhelming economic burden of sustained health disparities, it also suggests a barrier to health equity and social justice.

In 2009, the total direct and indirect cost of stroke in the United States was estimated at $68.9 billion.{17} Minority populations contribute to a significant portion of stroke costs due to higher admission rates, greater severity and mortality, increased disability-adjusted life-years, and loss of productivity from stroke incidence at younger ages.{13,16,27} Enrolling more minorities into stroke trials is an important part of any solution to alleviate the economic burden incurred through health disparities, improve the generalizability of trial results, and raise the standard of patient-centered stroke care.

Clot Lysis: Evaluating Accelerated Resolution of Intraventricular Hemorrhage III (CLEAR III) (; NCT00784134), a 500-participant randomized controlled trial evaluation of alteplase in hemorrhagic stroke, presented an opportunity to assess African American (AA) trial enrollment in a hemorrhagic stroke population. As detailed in the following sections, the authors of this paper evaluated the CLEAR III screening and enrollment data to better understand if recruitment efforts provided diversity and, more importantly, to improve recruitment efforts in the future.



This Phase III randomized, double-blinded, placebo-controlled, multicenter trial was conducted at 73 sites in Brazil, Canada, Germany, Hungary, Israel, Spain, the United Kingdom, and the United States from 2009 to 2014.{28} The investigators were either neurointensive care or neurosurgical service teams. This was a first-of-a-kind trial; it combined a catheter device with up to four days of intensive care unit (ICU)–based drug treatment.

For the analysis of AA to non-AA participation, we limited the evaluation to U.S. sites. Over a five-year period, investigators across 61 U.S. hospitals screened 8,587 patients (see Figure 1) admitted to ICUs in 42 U.S. cities with stable, small non-traumatic intracerebral hemorrhage (ICH), intraventricular hemorrhage (IVH) with a clinical diagnosis of obstructive hydrocephalus, and an extraventricular drain (EVD) placed pre-trial. Participants were randomly assigned to receive alteplase (Genentech, Inc.) or normal saline (placebo) via the EVD.


Figure 1: CLEAR III trial screens from 2009 to 2014. AAs comprised 25.1% of the U.S. screens for which race was listed.



Participants were aged 18 to 80 years with known symptom onset within 24 hours of the initial CT scan. CT scans were obtained every 24 hours throughout dosing. Initial eligibility criteria required supratentorial ICH volume 30 mL or less; additional criteria included a historical modified Rankin Scale (mRS) score of 1 or less (no disability prior to ICH), no limitations to hospital care, and no ongoing coagulopathy, suspicion of aneurysm, arteriovenous malformation, or other vascular anomaly.{28}


CLEAR III was a complex trial, with a long screening window of 72 hours. After the local principal investigator determined eligibility, the patient’s family was approached and informed of relevant risks, benefits, and alternative treatments. During the study, the investigators were provided guidelines, a checklist for consent, a smartphone application with procedural bedside guidance, and training consent videos modeling best and worst consent practices, both in the general case and specific to the CLEAR III intervention.{29}

The consent training program included an annual, mandatory refresher webinar on best practices, as well as training on how to engage colleagues to refer patients into the trial. After the families were given time to consider and comprehend the elements of participation, the families of fully eligible patients were again approached, and informed consents were obtained or refused. We then compared AA and non-AA timelines for presentation, signed consent, and randomization.


All data were captured electronically, and pertinent source documents were uploaded by local site personnel using a web-based electronic data capture (EDC) system (VISION, Prelude Dynamics, LLC). All participants and trial personnel, except for the local and central pharmacists and the unblinded statistician, were masked to treatment assignments. Site personnel randomly assigned patients (1:1) within 72 hours of ictus. The EDC system transmitted a treatment allocation by e-mail directly to the local, trained pharmacist.


The same EDC system was used to enter all participants screened. Study coordinators were trained to enter all admissions with a primary or secondary diagnosis of IVH in the electronic screening log. Protocol inclusion/exclusion (I/E) criteria were collected in the EDC via prespecified selections and then categorized as either medical reasons (e.g., biologically ineligible or predetermined I/E ineligible) or nonmedical reasons (e.g., access, personal choices, mistrust).

EDC compliance was monitored, and sites were encouraged to make screening entries in real time. To limit coordinator burden, only a single exclusion factor was required for screen failures; sites were compensated for screening activities. Enrolling teams were trained to screen admissions, in person, every morning and afternoon or round with the ICU care teams. Remote screening, using electronic admission and medical records, was discouraged. Teams were trained to consider some I/E conditions as temporary and to conduct multiple screening attempts on such subjects during the 72-hour window.


Race was collected as part of screening data and entered locally into VISION. Investigators or study coordinators selected one or more of the following to report race: American Indian or Alaskan Native, Native Hawaiian or Other Pacific Islander, Black or African American, Asian, or White. From these categories, we grouped patients into two categories when race was listed—as either AA if Black or African American was selected (including those who chose other races in addition to AA) or non-AA if Black or African American was not selected (see Figure 1).


We analyzed AA participation using randomization (Stage 1) and screening (Stage 2) data. Our first inspection compared trial enrollment to an National Institutes of Health aggregate report{30} and to U.S. population data from 1990, 2000, and 2010, obtained via To inform end-of-trial comparisons, forecast projections were calculated to determine the likely AA percentage for a 2014 U.S. population.

With CLEAR III demonstrating such substantial AA participation and robust conversion rates, we stratified AA trial randomization rate by site geographic region. We then examined city census data at our CLEAR III locations, examining whether hospital location mattered. We retrieved census percentages{31} and used simple linear regression modeling to assess the relationship between AA census in 42 cities and the AA percent screened, as well as the AA percent randomized in each city. Site and city data for CLEAR III sites that did not enroll any patients (regardless of race) were excluded from the analysis.

We next stratified screening data by gender and age to test for significant demographic differences. Last, we interrogated the data for AA vs. non-AA distribution among medical, nonmedical, or combination (both medical and nonmedical) reasons for screen failure. Chi-square was used to compare the proportions between AA and non-AA for each screen failure reason.


Stage 1: African American vs. Non-African American Enrollment


The U.S. respective trial enrollment rates were: African American, 45.1%; Asian, 3.5%; American Indian/Alaskan Native, 0.3%; Native Hawaiian or Other Pacific Islander, 0.8%; White, 48.6%; remaining mixed races, 0.3%; and Unknown, 1.4%. For our analyses, we grouped the race categories into AA and non-AA. When we compared CLEAR III recruitment to other National Institute of Neurological Disorders and Stroke (NINDS) participation data and to U.S. population data during the same period as the trial, CLEAR III recruitment outperformed population expectations and that of other NINDS trials (see Table 1). AAs comprised 45.1% of total U.S. enrollments (n=370), or more than twice the 19.8% participation rate reported by NINDS in 2011{30} and triple the projected 13.9% U.S. population in 2014.


Table 1: CLEAR III Enrollment Rates Compared to NINDS Rates and the U.S. Census Population During the Same Periods
  Period AA Trial
Representation (%)
U.S. Population
(%, Year)
Pre-NIH Revitalization Act 1985-1995 11.6% 12.1% (1990)
56 NINDS trials 1996-2008 19.8% 12.9% (2000)*

13.0% (2010)**

CLEAR III U.S. trial subjects (AAs) 2009-2014 45.1% 14.1% (2014)***
* Includes persons identifying as African American and one or more additional races

** An additional 1% of the U.S. population identified as African American in addition to one or more other races

*** Projected U.S. population


Conversion (Randomization) Rate by Geographic Region

Conversion rates for both AA and non-AA participants were calculated as total number of enrolled divided by total number screened (see Table 2). Our planned conversion rate for trial enrollment was 5%. The randomized-to-screened ratio for AAs was 8.7% vs. 3.4% non-AA (p<0.001). Regional analysis showed similar differentials with AA conversion rates: Northeast (7.7% vs. 2.9%, p<0.001); South (8.2% vs. 4.0%, p<0.001); Midwest (10.3% vs. 3.6%, p<0.01); and West (8.9% vs. 3.8%, p=0.02).


Table 2: Conversion (Randomization) Rates: U.S. Overall and by Geographic Regions
Regions (n=sites)  

AA (%)


Non-AA (all other) (%) p value
U.S. overall (n=61) 8.7 3.4 <0.001
Northeast (n=20) 7.7 2.9 <0.001
South (n=16) 8.2 4.0 <0.001
Midwest (n=16) 10.3 3.6 <0.01
West (n=9) 8.9 3.8 0.02


Conversion (Randomization) Rate and City Census Comparisons

Trial sites were grouped by city, and their AA enrollment percentages were compared to corresponding city census data. The proportion of AAs enrolled per city ranged from 0% to 100%, with a mean of 40.4% (see Figure 2a). The AA city census ranged from 1.3% to 82.7%, with a mean of 28.0%. The enrollment mean of 40.4% robustly exceeded the census mean (28.0%). Higher AA census was associated with higher AA enrollment percentage (R² = 0.17, p value = 0.004; β ̂ (95% CI) = 0.7 (0.25, 1.21)). The symbol β ̂ defines the slope of the regression line. The AA percent enrolled in a city increased, on average, 0.7% for each percent increase in AA census.



Stage 2: African American vs. non-African American Screening

We next looked at screening to understand conversion performance, assess who was excluded, and evaluate whether reasons for exclusion related to relevant demographic and biological variables.

Screening Rate and City Census Comparisons

The proportion of AAs screened per city ranged from 0% to 63.7%, with a mean of 23.2% (see Figure 2b). Higher AA census was associated with higher AA screening percentage; the AA percent screened in a city increased, on average, 0.6 for each percent increase in AA census (R² = 0.46, p value < 0.001; β ̂ (95% CI) = 0.62 (0.41, 0.83)). Comparing the census and screening means, CLEAR III investigators screened slightly less than the census mean (23.2% vs. 28%).

Screening by Gender and Age

We then assessed gender and age for overall U.S. screens and screen failures, where race was listed, to detect any significant demographic differences. Out of the 8,587 U.S. screens, race was reported for 7,663 participants (see Figure 1). Of the 7,663 race-listed participants, 7,298 were screen failures and 365 were enrolled; further, gender was missing on four non-AA participants, with all of these being among the screen failures.

Of the race-listed U.S. screens, AAs consisted of 918 (47.7%) females and 1,005 (52.3%) males. Equivalently, non-AAs consisted of 2,735 (47.7%) females and 3,001 (52.3%) males. Of the screen failures, AAs consisted of 839 (47.8%) females and 917 (52.2%) males. Similarly, non-AAs consisted of 2,640 (47.6%) females and 2,898 (52.3%) males. There was no statistically significant difference in gender between race-listed U.S. screens and screen failures.

For race-listed U.S. screens, the average age of AA participants was 58 years old (standard deviation 13.7), compared to an average age of 66 years for non-AA participants (standard deviation 15.6) with a p value <0.001. For the screen failure subset, similar results hold; the average age of AA participants was 58 years old (standard deviation 14.0), compared to an average age of 66 years for non-AA participants (standard deviation 15.7) with a p value <0.001.

Medical vs. Nonmedical-Related Screen Failures

Upon review of screen failure reasons within the AA and non-AA race groups, African Americans were less frequently excluded due to biological/research strategy reasons (see Table 3).


 Table 3: Screen Failure Categories (N = 7,298)
  AA Non-AA p value
  N % N %
Medical 1,292 73.6% 4,581 82.7% <0.001
Abnormal PTT, PLT < 100K, INR > 1.3 34 1.9% 122 2.2% 0.503
Age < 18 or > 80 years 98 5.6% 884 16.0% <0.001
Aneurysm, mycotic aneurysm, moyamoya, etc. 161 9.2% 743 13.4% <0.001
Craniectomy/other surgical procedures 21 1.2% 51 0.9% 0.308
Etiology – tumor 3 0.2% 47 0.8% 0.003
GCS < 3/herniation/brain dead/deceased 25 1.4% 43 0.8% 0.014
Historic (pre-bleed) Rankin not 0 or 1 47 2.7% 96 1.7% 0.013
ICH > 30 cc on diagnostic CTC 256 14.6% 700 12.6% 0.035
Infratentorial bleed 150 8.5% 451 8.1% 0.591
No EVD placed 220 12.5% 733 13.2% 0.449
No obstruction of 3rd and/or 4th 261 14.9% 636 11.5% <0.001
Unstable bleeding 16 0.9% 75 1.4% 0.146
Non-medical 199 11.3% 471 8.5% <0.001
Improper screening 9 0.5% 21 0.4% 0.446
Participation in another trial 6 0.3% 10 0.2% 0.208
Patient eligible but refused consent 54 3.1% 66 1.2% <0.001
Patient is DNR 59 3.4% 250 4.5% 0.037
Study staff not notified within window 11 0.6% 21 0.4% 0.171
Study staff unavailable 2 0.1% 8 0.1% 0.764
Unable to dose within time window 58 3.3% 95 1.7% <0.001
Combination medical and non-medical reasons 265 15.1% 490 8.8% <0.001
MD/Surgeon chose not to enroll 56 3.2% 65 1.2% <0.001
Not an IVH patient 18 1.0% 49 0.9% 0.59
Other 191 10.9% 376 6.8% <0.001
Total 1,756 100.0% 5,542 100.0%  


For the medical screen failure category, AA had a lower percentage of patients excluded at the Upper Age Limit (AA: 5.6% vs. non-AA: 16.0%), Aneurysm (AA: 9.2% vs. non-AA: 13.4%), and Etiology Tumor (AA: 0.2% vs. non-AA: 0.8%). However, AAs had a higher percentage of exclusions for GCS/Herniation/Brain Dead/Deceased (AA: 1.4% vs. non-AA: 0.8%), Historic Rankin not 0 or 1 (AA: 2.7% vs. non-AA: 1.7%), ICH > 30 cc (AA: 14.6% vs. non-AA: 12.6%), and no obstruction of 3rd and/or 4th (AA: 14.9% vs. non-AA: 11.5%). Other remaining screen failure reasons were statistically insignificant.

For the nonmedical reasons screen failure category, AAs had a lower percentage of patients who were DNR (AA: 3.4% vs. non-AA: 4.5%) and a higher percentage of patients who were eligible but refused consent (AA: 3.1% vs. non-AA: 1.2%). Remaining screen failure reasons for this category were statistically insignificant.

One category, “MD/Surgeon chose not to enroll,” had too broad a response, combining both medical and nonmedical reasons. For screen failure category, AA had a higher percentage of screen failures for MD/Surgeon chose not to enroll (AA: 3.2% vs. non-AA: 1.2%) and Other (AA: 10.9% vs. non-AA: 6.8%). Other reasons were statistically insignificant (see Table 3).


AAs enrolled in CLEAR III at a rate greater than expected by available census data, regardless of city or geographic region. Although AAs refused consent at a greater rate, they enrolled 2.5 times more often than non-AAs.

When we compared CLEAR III performance to other brain hemorrhage randomized clinical trials during the same period, CLEAR III enrolled AAs at 45.1% compared to 9% to 30% in the other trials, though AA screening and enrollment data are not available for some trials, limiting the comparison (see Table 4). Further limiting comparison is that these trials were international and did not break out racial data by countries.


Table 4: Enrollment Window and Race Reporting in Major ICH Clinical Trials
Trial Inter-national Medical or Surgical Trial Enrollment Window (Hours) F/U (Days) Total Enrolled % White Reported % AA or Black Reported
CHANT N Medical 6 90 607 * *
ICES N Surgical 48 365 24 45.8 33.3
FAST Y Medical 4 90 841 9.0
ATACH-2 Y Medical 4.5 90 1,000 13.1
PREDICT Y Medical 6 90 268 86.0
Deferoxamine N Medical 18 90 20 85.0
NovoSeven Y Medical 3 90 399 81.0
MISTIE II Y Surgical 48 365 96 56.0 30.0
CLEAR III Y Medical 72 365 500 61.0 34.0
Medical 72 365 370 48.6 45.1
* Race not reported


When comparing reported enrollment windows and follow-up intervals, there is one notable difference—time from onset to randomization. CLEAR III participants had a much longer enrollment window, allowing more time to communicate with families. Moreover, the communication period occurred in the ICU rather than the Emergency Department. Prospective research on the relationship between enrollment windows, follow-up intervals, social support, recruitment monitoring, and minority enrollment/retention may provide stronger correlations.

Gorelick et al. published the recruitment triangle in 1998,{32} illustrating the social support triangle that reduces barriers and lessens disparities. The design of the 72-hour enrollment time window could be essential to enrollment and retention, particularly among AA participants, allowing communication time with the social support stakeholders and within the insulated ICU where trust reduces barriers, regardless of race or ethnicity. Initial and ongoing training of site teams emphasized that temporary I/E factors could resolve over a three-day period and the use of the entire time window.

CLEAR III utilized intensive site management oversight with strong emphasis on best screening, consenting, and enrollment practices. We evaluated recruitment monthly and retrained annually on best consent practices, and we gave a presentation on common reasons for refusals both from families and investigators and on how to solve fixable refusal reasons. Furthermore, our training included the recruitment triangle social support principles{30} of taking time and connecting with families; earning trust, not only of families but also of the ICU teams involved in the treatment and care of the patient; using best consent practices; providing family access to an interested and caring investigator; and respecting the cognitive and physical concerns of families in distress and sensory overload throughout the trial participation continuum.


While biological/research strategy exclusions, city census, and being younger may have contributed to CLEAR III’s high enrollment of AAs, any causal mechanisms behind these associations remain unclear. Several limitations impact the interpretation of our analysis.

Race categories were presented as checkboxes in the EDC and no specific definition for each category was provided, nor were directions for choosing race included in training. Thus, different interpretations of race categories may have occurred at the time of data entry. Furthermore, we recognize that there may have been inconsistencies across sites whether the race reported was determined by the patient, patient relative(s), medical record, site coordinator, or physician.

While race was more closely monitored for enrollment data, the same standards were not applied to screen failures. Of the 8,587 screens, 924 were missing race data (of which five were enrolled), introducing potential sampling error. Screen failure reasons such as “MD/Surgeon chose not to enroll,” “Patient eligible but refused consent,” and “Other” did not allow details, possibly obscuring causal factors related to race and recruitment. Another possible limitation is that the traditional categories “comorbidity,” “likely not able to complete the protocol,” and “…otherwise, in the investigator opinion, not eligible…” were grouped together and labeled as “Investigator Decision,” thus not identifying whether these screen failures were for medical or nonmedical reasons or providing further details as to who made the decision.

Screening logs were not monitored prospectively. Tracking diversity in clinical trials is essential, and monitoring screening logs monthly for content (and not just submission) can determine how teams are doing (beyond overall screening and conversion rates) as they recruit the underrepresented and underserved. Additionally, recognizing minority screen failures early allows the opportunity to redesign poorly constructed forms and retrain poorly performing teams. Further, including recruitment diversity and disparities metrics when publishing clinical trial results is imperative for comparative research where sub-populations are under active investigation.

Last, the analysis covered only city-level data; data are limited on the demographic characteristics of eligible patients at non-trial hospitals and patients coming to trial hospitals from other cities.


AAs were willing to enroll in a novel, acute stroke trial, such as CLEAR III. Enrollment was systematically consistent in proportion to the subjects’ demographics, taken from census data, suggesting higher enrollment was a function of the overall trial characteristics and national population characteristics. The enrollment of AAs was proportional to disease prevalence and allows for a robust estimate of minority population characteristics and responses.

That CLEAR III AA enrollment exceeded census percentages is an important finding that requires further exploration. Cities densely populated by AAs should be considered when selecting recruitment sites. Census rates may be useful when setting recruitment goals, particularly for ICH trials.

Consent training in disparity recruitment methods appears to have been rewarded. Better screening instruments, screening standardization, and recruitment metrics will be important to the design of any trial. Prospective recruitment monitoring, along with surveys and interviews following refusals, could improve understanding of screening-to-enrollment conversion rates among research participants.

Efforts are under way to understand and improve recruitment of AAs and other underrepresented minorities into clinical trials. If we are to improve proportions of minorities enrolled, then we should apply the recruitment triangle to minority recruitment, interviewing, and data-entry training at investigator meetings and as part of best consent coaching.

This trial may provide some structure to those “trial-in-progress” practices. When designing clinical trials, determining underlying reasons for participation probably helps find solutions for eliminating disparities. Interestingly for CLEAR III, such an approach during the trial might have provided information about lower participation rates of non-AAs. When the incidence of stroke or other diseases is higher in minorities, we must develop minority-specific training programs to teach investigative teams about the importance of diversity.

Future trials should consider such factors as incorporating minority recruitment goals in data collection design and consent training; incorporating targeted enrollment data into screening logs to manage enrollments during the trial to avoid falling short of minority representation; and bringing diversity awareness to the design of I/E criteria, data collection materials, and consent practices.


The authors thank the CLEAR III patients and their families for participating in the trial and contributing to this very important research to find a treatment for a devastating and otherwise untreatable form of stroke. We especially acknowledge and thank our AA patients, who historically share the greater burden of severity, mortality, and disability, for improving the generalizability of the CLEAR III trial results and raising the standard of patient-centered stroke care. We also thank Megan Clark for her editorial assistance.

CLEAR III is supported by the grant 5U01 NS062851-05, awarded to Daniel Hanley from the National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS). This work is also supported through a NINDS for Conference Funding Support of the Health Equity Symposium (Grant R13NS101924, 6th World Intracranial Hemorrhage Conference Grant), awarded to Karen Lane. We also thank Genentech, Inc. for its donation of alteplase for use in the CLEAR III trial.


  1. Colby SL, Ortman JM. 2015. Projections of the size and composition of the US population: 2014 to 2060. Current Population Reports 25-1143:9.
  2. Paskett ED, Reeves KW, McLaughlin JM, et al. 2008. Recruitment of minority and underserved populations in the United States: the Centers for Population Health and Health Disparities experience. Contemporary Clinical Trials 29(6):847-61.
  3. Bonner GJ, Miles TP. 1997. Participation of African Americans in clinical research. Neuroepidemiology 16(6):281-4.
  4. Levkoff S, Sanchez H. 2003. Lessons learned about minority recruitment and retention from the Centers on Minority Aging and Health Promotion. The Gerontologist 43(1):18-26.
  5. Harris Y, Gorelick PB, Samuels P, Bempong I. 1996. Why African Americans may not be participating in clinical trials. J Nat Med Assoc 88(10):630.
  6. Dancy BL, Wilbur J, Talashek M, Bonner G, Barnes-Boyd C. 2004. Community-based research: barriers to recruitment of African Americans. Nursing Outlook 52(5):234-40.
  7. Ross S, Grant A, Counsell C, Gillespie W, Russell I, Prescott R. 1999. Barriers to participation in randomised controlled trials: a systematic review. J Clin Epidem 52(12):1143-56.
  8. Corbie-Smith G, Thomas SB, Williams MV, Moody-Ayers S. 1999. Attitudes and beliefs of African Americans toward participation in medical research. J Gen Int Med 14(9):537-46.
  9. Branson RD, Davis K, Butler KL. 2007. African Americans’ participation in clinical research: importance, barriers, and solutions. Am J Surgery 193(1):32-9.
  10. Chandra A, Paul III DP. 2003. African American participation in clinical trials: recruitment difficulties and potential remedies. Hospital Topics 81(2):33-8.
  11. Huang H-h, Coker AD. 2010. Examining issues affecting African American participation in research studies. J Black Studies 40(4):619-36.
  12. Shavers VL, Lynch CF, Burmeister LF. 2002. Racial differences in factors that influence the willingness to participate in medical research studies. Ann Epidem 12(4):248-56.
  13. Krishnamurthi RV, Moran AE, Forouzanfar MH, et al. 2014. The global burden of hemorrhagic stroke: a summary of findings from the GBD 2010 study. Global Heart 9(1):101-6.
  14. Howard G, Cushman M, Howard VJ, et al. 2013. Risk factors for intracerebral hemorrhage. Stroke 44(5):1282-7.
  15. Qureshi AI, Giles WH, Croft JB. 1999. Racial differences in the incidence of intracerebral hemorrhage: effects of blood pressure and education. Neurology 52(8):1617-21.
  16. Rincon F, Mayer SA. 2013. The epidemiology of intracerebral hemorrhage in the United States from 1979 to 2008. Neurocritical Care 19(1):95-102.
  17. Ovbiagele B, Nguyen-Huynh MN. 2011. Stroke epidemiology: advancing our understanding of disease mechanism and therapy. Neurotherapeutics 8(3):319.
  18. Flaherty ML, Woo D, Haverbusch M, et al. 2005. Racial variations in location and risk of intracerebral hemorrhage. Stroke 36(5):934-7.
  19. Broderick JP, Brott T, Tomsick T, Huster G, Miller R. 1992. The risk of subarachnoid and intracerebral hemorrhages in blacks as compared with whites. NEJM 326(11):733-6.
  20. Broderick J, Brott T, Kothari R, et al. 1998. The greater Cincinnati/northern Kentucky stroke study. Stroke 29(2):415-21.
  21. Collaborators NMSS, Sacco RL, Boden-Albala B, et al. 1998. Stroke incidence among white, black, and Hispanic residents of an urban community: the Northern Manhattan Stroke Study. Am J Epidem 147(3):259-68.
  22. Sturgeon JD, Folsom AR, Longstreth W, Shahar E, Rosamond WD, Cushman M. 2007. Risk factors for intracerebral hemorrhage in a pooled prospective study. Stroke 38(10):2718-25.
  23. Grysiewicz RA, Thomas K, Pandey DK. 2008. Epidemiology of ischemic and hemorrhagic stroke: incidence, prevalence, mortality, and risk factors. Neurologic Clinics 26(4):871-95.
  24. Labovitz DL, Halim A, Boden-Albala B, Hauser W, Sacco R. 2005. The incidence of deep and lobar intracerebral hemorrhage in whites, blacks, and Hispanics. Neurology 65(4):518-22.
  25. Johnston SC, Selvin S, Gress DR. 1998. The burden, trends, and demographics of mortality from subarachnoid hemorrhage. Neurology 50(5):1413-8.
  26. Howard G, Anderson R, Sorlie P, Andrews V, Backlund E, Burke GL. 1994. Ethnic differences in stroke mortality between non-Hispanic whites, Hispanic whites, and blacks. The National Longitudinal Mortality Study. Stroke 25(11):2120-5.
  27. Boan AD, Feng WW, Ovbiagele B, et al. 2014. Persistent racial disparity in stroke hospitalization and economic impact in young adults in the buckle of stroke belt. Stroke 45(7):1932-8.
  28. Hanley DF, Lane K, McBee N, et al. 2017. Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial. Lancet 389(10069):603-11.
  29. Freeman W, Macek M, Lane K, et al. 2014. Simulation of best-consent and failed-consent elements from CLEAR III: a randomized, placebo controlled trial. Poster 170. Paper presented at the 12th Annual Neurocritical Care Society Meeting, Seattle, Wash.
  30. Burke J, Brown D, Lisabeth L, Sanchez B, Morgenstern L. 2011. Enrollment of women and minorities in NINDS trials. Neurology 76(4):354-60.
  31. U.S. Census Bureau. American FactFinder.
  32. Gorelick PB, Harris Y, Burnett B, Bonecutter FJ. 1998. The recruitment triangle: reasons why African Americans enroll, refuse to enroll, or voluntarily withdraw from a clinical trial. An interim report from the African-American Antiplatelet Stroke Prevention Study (AAASPS). J Nat Med Assoc 90(3):141.

Karen Lane, CCRP, ( is an assistant professor of neurology and the administrative director of research in the Johns Hopkins University Division of Brain Injury Outcomes.

Maningbe Keita, BA, ( is a research assistant in the Johns Hopkins University Division of Brain Injury Outcomes and a doctoral student in health policy and management at the Johns Hopkins Bloomberg School of Public Health.

Radhika Avadhani, MS, ( is a senior research data manager in the Johns Hopkins University Division of Brain Injury Outcomes.

Rachel Dlugash, MSPH, ( is a senior research data manager in the Johns Hopkins University Division of Brain Injury Outcomes.

Steven Mayo, PD, CCRA, PMP, ( is the founder and president of the contract research organization Emissary International and director of quality assurance in the Johns Hopkins University Division of Brain Injury Outcomes.

Richard E. Thompson, PhD, ( is a senior scientist in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

Issam A. Awad, MD, MSc, FACS, FAHA, (, the John Harper Seeley Professor in Neurological Sciences and director of Neurovascular Surgery at the University of Chicago Medicine and Biological Sciences, was the co-principal investigator and surgical chairman of the CLEAR III trial.

Nichol McBee, MPH, CCRP, ( is the division manager of the Johns Hopkins University Division of Brain Injury Outcomes.

Wendy Ziai, MD, ( is an associate professor of neurology, neurosurgery, and anesthesiology and critical care medicine at the Johns Hopkins University School of Medicine.

Daniel F. Hanley ( is the Jeffrey and Harriet Legum Chair of Acute Care Neurology and director of the Johns Hopkins University Division of Brain Injury Outcomes.

Back to Clinical Researcher