Next Article in Journal
Eye-Catching Microbes—Polyphasic Analysis of the Microbiota on Microscope Oculars Verifies Their Role as Fomites
Next Article in Special Issue
Managing Bladder Cancer Care during the COVID-19 Pandemic Using a Team-Based Approach
Previous Article in Journal
Serum Zinc Level Is Associated with Frailty in Chronic Liver Diseases
Previous Article in Special Issue
Advancing the Science and Management of Renal Cell Carcinoma: Bridging the Divide between Academic and Community Practices
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 9
Issue 5
10.3390/jcm9051571
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Strategies to Improve Participation of Older Adults in Cancer Research

by
Jennifer Liu
1,†,
Eutiquio Gutierrez
2,†,
Abhay Tiwari
1,
Simran Padam
1,
Daneng Li
1,
William Dale
3,
Sumanta K. Pal
1,
Daphne Stewart
1,
Shanmugga Subbiah
1,
Linda D. Bosserman
1,
Cary Presant
1,
Tanyanika Phillips
1,
Kelly Yap
1,
Addie Hill
1,
Geetika Bhatt
1,
Christina Yeon
1,
Mary Cianfrocca
1,
Yuan Yuan
1,
Joanne Mortimer
1 and
Mina S. Sedrak
1,*
1
Department of Medical Oncology and Therapeutics Research, City of Hope, Duarte, CA 91010, USA
2
Department of Internal Medicine, Harbor-UCLA Medical Center, Los Angeles, CA 90502, USA
3
Department of Supportive Care Medicine, City of Hope, Duarte, CA 91010, USA
*
Author to whom correspondence should be addressed.
†
Authors contributed equally.
J. Clin. Med. 2020, 9(5), 1571; https://doi.org/10.3390/jcm9051571
Submission received: 17 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine)
Browse Figures
Versions Notes

Abstract

:
Cancer is a disease associated with aging. As the US population ages, the number of older adults with cancer is projected to dramatically increase. Despite this, older adults remain vastly underrepresented in research that sets the standards for cancer treatments and, consequently, clinicians struggle with how to interpret data from clinical trials and apply them to older adults in practice. A combination of system, clinician, and patient barriers bar opportunities for trial participation for many older patients, and strategies are needed to address these barriers at multiple fronts, five of which are offered here. This review highlights the need to (1) broaden eligibility criteria, (2) measure relevant end points, (3) expand standard trial designs, (4) increase resources (e.g., institutional support, interdisciplinary care, and telehealth), and (5) develop targeted interventions (e.g., behavioral interventions to promote patient enrollment). Implementing these solutions requires a substantial investment in engaging and collaborating with community-based practices, where the majority of older patients with cancer receive their care. Multifaceted strategies are needed to ensure that older patients with cancer, across diverse healthcare settings, receive the highest-quality, evidence-based care.

1. Introduction

Aging is a major risk factor for cancer, with 28% of cancers in the US diagnosed in adults aged 65–74, 18% in adults aged 75–84, and 8% in adults aged 85 and older [1]. Globally, the US has the third largest number of older adults [2], and as the US population ages, the number of older adults with cancer is increasing and will make up a growing share of the oncology population [3,4,5]. Despite this, older adults remain vastly underrepresented in the research that sets the standards for cancer treatments [6,7]. Consequently, most of what is known about cancer therapeutics is based on clinical trials conducted in younger and healthier patients [8,9,10]. Furthermore, despite a plethora of literature documenting barriers to the accrual of older adults to cancer clinical trials (Figure 1), there are few evidence-based strategies to mitigate these barriers [11,12]. A recent systematic review revealed that among 8691 studies screened, only 12 relevant observational studies examined barriers hindering the participation of older adults in cancer clinical trials, and one (negative) randomized controlled trial evaluated an intervention to increase the enrollment of older adults in trials [8]. Moreover, few studies have focused on understanding the complex and multifactorial influences affecting the clinical trial participation of older patients with cancer in the community, where the majority of this population is often treated [13,14].
Recognizing this problem, numerous organizations, including the Institute of Medicine (IOM), have cited the growing population of older adults with cancer and highlighted the need to generate high-quality, evidence-based data for the treatment of older adults [6,15,16,17,18]. The primary aim of this review is to lay out a multipronged approach that addresses barriers to the participation of older adults in cancer clinical trials. A secondary aim is to highlight how a collaborative clinical research network of academic and community-based oncology practices can facilitate the inclusion of older adults in cancer research.

2. Make Eligibility Criteria More Inclusive and Less Restrictive

Restrictive eligibility criteria often exclude a large proportion of patients from participating in clinical trials—a loss which sacrifices the generalizability of results to the overall patient population. Older adults, in particular, are often not offered the opportunity to participate in trials due to concerns for multiple comorbidities, organ dysfunction, treatment toxicity, and/or frailty [8,19,20,21,22]. Numerous trials explicitly restrict inclusion to patients with an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 1 or a Karnofsky performance score (KPS) of ≥70 [23,24]. As a result, trial data and findings are often derived from younger patients who are more fit and without organ dysfunction and comorbidities, as commonly seen in older adults [23].
A variety of efforts are underway to broaden the eligibility criteria to make trials more relevant to patients of all ages, including initiatives by the National Institutes of Health (“Inclusion Across the Lifespan policy”), American Society of Clinical Oncology (ASCO), and the Food and Drug Administration (FDA) [16,25,26]. Sponsors and investigators are encouraged to work together to follow these policy recommendations aimed at making the eligibility criteria less restrictive and more inclusive of demographically and clinically diverse patients, representative of the populations seen in community-based oncology settings. Investigators should review the eligibility criteria closely and forgo exclusion on the basis of lab values (e.g., creatinine), performance status, comorbid conditions, or second malignancy when designing clinical trials. Instead, a patient’s functional or biological age should be taken into consideration, which may be a better indicator of how a patient will tolerate a certain treatment regimen.
Additionally, investigators should work closely with community clinicians and other stakeholders (i.e., patient advocates) to understand the patient populations seen in community-based practices and design eligibility criteria that are more inclusive, thus allowing trial results to be applicable to a broader patient population. Inclusive clinical trial systems within a collaborative research network ought to consider patients as they are, rather than as they should be. Trial eligibility should be evaluated and revised to ensure that investigators are broadening study access to new, successful cancer treatment regimens for older adults with cancer across diverse healthcare delivery settings.

3. Capture Relevant End Points that Matter to Older Patients

Sponsors and investigators should capture relevant end points that are important to the older patient, including measures of tolerability as well as clinical and biological aging consequences of cancer and its treatment [17,27,28,29].
Studies have shown that most older adults do not want to compromise their quality of life or function for survival benefits [30]. Hence, trials designed to collect information on this population must include patient-reported measures that are relevant to the patient’s experience and move beyond clinician-reported toxicity. Therefore, data are needed to understand the immediate (short-term) and longitudinal (long-term) impact of cancer and cancer therapy on patient health and quality of life [29,31]. Given that each individual is unique and can respond differently to therapy, incorporating assessments that focus on patient-reported outcomes may allow investigators to better describe a patient’s tolerability beyond the traditional numerical grades (NCI CTCAE) [32,33,34], and the use of mixed-methods approaches (e.g., surveys, interviews, focus groups) may allow investigators to better understand a patient’s experience and priorities for treatment. Furthermore, measuring the frequency of toxicities over time can help investigators to characterize the longitudinal impact of treatment and better determine the relevance to the older patient population. This information can inform new ways treatment approaches and optimal strategies to reduce toxicities, avoid early treatment discontinuation, and achieve an effective dose intensity.
In addition to capturing measures of quality of life and tolerability, clinical measures of aging should also be considered as end points in cancer trials of older adults. This is important because aging is a heterogeneous process. While certain declines in organ function are universal as the human body ages, the consequences of this decline on everyday function proceed at a unique pace in each individual [35,36]. Therefore, chronologic age tells us relatively little about a specific individual [37]. A more detailed evaluation of an older patient is needed to capture factors that more effectively predict morbidity and mortality. A geriatric assessment (GA) may serve this purpose. The GA includes an evaluation of functional status, co-morbid medical conditions, cognitive function, nutritional status, social support, and psychological state, as well as a review of medications [38]. Additionally, geriatric screening tools (i.e., G8, VES-13), instruments that assess life expectancy (i.e., ePrognosis), and predictors for risks of chemotherapy toxicity (i.e., CARG or CRASH Score) can be utilized in trials to better describe or risk-stratify this population [39,40,41]. The ASCO guidelines now recommend the use of these assessments in the evaluation and management of older patients with cancer, and several NCI-sponsored cooperative groups have demonstrated the feasibility of using these measures in clinical trials [40].
Biological measures of aging are also important to consider when designing studies for older patients with cancer. These measures may provide insights on mechanisms behind aging-related clinical consequences due to cancer treatment, such as the biological drivers of functional and cognitive decline. Several studies have hypothesized that cancer and cancer treatment may accentuate or accelerate the rate of aging, leading to a decrease in multisystem reserve and increased risk for cardiomyopathy, secondary malignant neoplasms, frailty, muscular weakness, and neurocognitive issues [42]. Biological processes including stem cell exhaustion, cellular senescence, telomere attrition, increased free radical production, and epigenetic modifications have all been shown to play a role in accelerated aging due to cancer therapies [42,43]. Understanding the hallmarks of aging and the implications of cancer therapies associated with accelerated aging may provide insights into the short- and long-term effects of certain therapies, and further research in this area is needed. Knowledge of the underlying mechanisms to accelerated aging could ultimately help us to identify new strategies for targeting these processes in order to ameliorate accelerated aging and improve the health and well-being of this growing population.

4. Optimize Trial Designs for a Special Population

Inclusion of older patients may, in some cases, impose the need for larger sample sizes, which is not always feasible or fundable. Sponsors and investigators should consider high-yield approaches to collect data on older patients to efficiently facilitate the conduct of larger-scale trials at a reasonable cost. For example, pragmatic or innovative trials (e.g., adaptive, extended, embedded, N-of-1 studies) designed specifically for older patients may be used to fill knowledge gaps and improve the evidence base guiding the treatment of older adults [29,44].
There are several innovative trial designs that may be leveraged when considering a special population such as older adults with cancer (Table 1 adapted from prior literature [16,39]). For example, adaptive trials can be leveraged to allow for modifications to be made as the study proceeds [45,46]. Based on interim data analysis, the underperforming treatment arm may be eliminated to allow for a larger proportion of participants to be assigned to the more effective treatment arm. This is ideal for older adults because it reduces the number of participants in the treatment group that is performing poorly [16,39,47]. While adapted trial designs can be applicable to both exploratory and confirmatory studies, prospective cohort studies can be used to generate data on current standard-of-care treatments in older patients and provide insight into patterns of care and decision making [16,39].
Extended trial design can also be used in cases where the results of a trial have been reported, but there was an insufficient number of older adults enrolled to draw conclusions. A cohort of older adults can be added to the superior treatment arm to fill knowledge gaps and obtain data on the older population [16]. Embedded studies, also known as correlative or ancillary studies, can also be used to include additional measures of interest that are specific to older adults (i.e., toxicity, GA domains) within the infrastructure of a parent study [39]. Lastly, an N-of-1 or single-subject trial presents a feasible and innovative approach to better understanding how to care for older adults by following one patient over time, to examine aging-related consequences that occur throughout the cancer continuum [48].
When designing clinical trials appropriate for the older patient population, incorporating older adults into the study design phase, such as through the utilization of community-based participatory research methodology, may facilitate their participation in clinical trials [49,50]. Moreover, it is important for sponsors and investigators to engage all key stakeholders, including patient advocates and community-based clinicians, in the process of trial design in order to understand the types of patients seen in community practices (Figure 2). These discussions can inform protocol design and ensure participants are more representative of the patient populations beyond the academic setting. Furthermore, these collaborations can foster improved accrual, conduct, and dissemination of the research, thus ultimately increasing generalizability and clinical relevance of the trial findings.

5. Increase Institutional Support, Interdisciplinary Care, and Telehealth Use

Structural barriers at the system or institutional level dominate trial decision-making, and successful participation in research requires substantial institutional guidance, resources, and infrastructure [51]. There are many barriers to the conduct and support of a clinical trial research program. Studies have shown that community-based practices often struggle with understanding the value of trials, covering the costs of supporting a research program, meeting program requirements, managing the clinic workflow changes as they pertain to clinician involvement, and sustaining hospital leadership support, among other barriers [13,52,53]. In order to overcome these barriers to entry, partnerships with larger practices or academic centers with existing infrastructure can facilitate the conduct of clinical trials at community practices. Additionally, leadership is needed at the institutional level to ensure that the mission, interests, and workflow of community sites are aligned with the academic medical centers in order to cover the ever-increasing requirements of implementing and maintaining a clinical research program, especially one that aims to increase the representation of older adults with cancer (Figure 2) [54].
In addition to institutional leadership, accessibility to specialized geriatric care may be an important facilitator in the recruitment and retention of older adults in cancer clinical trials. Clinical programs that meld geriatric and oncology communities to meet the complex needs of older adults may facilitate the improved management of older adults [17,30,39]. City of Hope, for example, has jointly launched two interdisciplinary clinical programs: (1) the Specialized Oncology Care & Research in the Elderly (SOCARE) clinic and (2) the Aging Wellness Clinic. Similar models have also been established at the Memorial Sloan Kettering Cancer Center, Thomas Jefferson University, and the University of Rochester, among others [55,56,57]. These specialized programs offer interdisciplinary, individualized, and integrated treatment for older patients and survivors with cancer. However, these programs require resources and institutional support, and further research is warranted to understand how these care models lead to improved patient outcomes and participation in clinical trials.
Furthermore, the integration of technology in the form of virtual visits and telehealth encounters may facilitate the enrollment of older adults in cancer research. There are two potential strategies for this. First, technology may be leveraged to reduce the burden of the patient and caregiver—a known barrier to older participation in research [8,17,39]. Studies have shown that telehealth use is associated with high patient satisfaction, improved access to care, and reduced health disparities among older adults [58,59,60,61,62]. Challenges to telehealth use among the older patient population must be taken into consideration, including practical barriers to adoption, anxiety using technology, and financial burden, among other factors that promote the digital divide [53]. To address some of these barriers, simple and intuitive graphical interfaces, the availability of technical support, and the provision of telehealth services at no cost may reduce the barriers to entry and improve adoption of telehealth in this population [63]. However, further efforts are needed to better understand the limits and benefits of telehealth in geriatric oncology. Moreover, the COVID-19 pandemic has led to the rapid implementation of telehealth services for both standard care and clinical research [64,65]. This swift paradigm shift in how patients receive their care provides a unique opportunity to gain insight on how telehealth can further be used to improve patient participation in cancer clinical trials—particularly in older adults. However, the feasibility, adoption, and sustainability of telehealth in clinical trials is limited, and further research is needed [66].
Second, telehealth can be a key player in providing specialized geriatric care to sites that may not have access to multidisciplinary programs with geriatric expertise. For example, City of Hope has an ongoing pilot study to evaluate the feasibility of delivering a GA-driven intervention at a community-affiliated site using telehealth. In this way, telehealth may help fill the gap in care for older patients who otherwise would not have access to multidisciplinary, specialized geriatric-based care [67]. Whether this improves clinical trial accrual warrants further investigation.

6. Leverage Principles of Behavioral Economics

There is a growing interest in the general medical literature around the use of behavioral economics to shape clinician and patient behavior, with some even suggesting the use of “nudges” to facilitate patient enrollment in research [68]. A nudge is defined as a change in the way choices are presented or information is framed that alters a person’s behavior in a predictable way without restricting choice [68,69]. If implemented properly, nudges are transparent, easy to opt out of if needed, and aligned with the best interest or welfare of the person being nudged. It is a principle of behavioral economics that leverages the fact that our decisions and behaviors are heavily influenced by the environment in which they occur, and interventions can be systematically developed to influence how individuals behave (e.g., weight loss, exercise, and statin utilization) [70,71,72,73,74].
Nudges may also be a novel way to facilitate patient participation in cancer clinical trials, especially among highly underrepresented groups such as older adults [75]. For example, surveys that assess patients’ interest in participating in clinical research can help identify eligible patients who are likely to participate and provide insight into the types of trials that are desirable. Using these interest surveys, clinicians can identify patients who may be more inclined to participate in studies when presented the opportunity to do so due to their prior expressed interest (i.e., foot-in-the-door technique) [75]. Another nudging strategy that can be utilized to encourage the enrollment of older adults in cancer trials is the personalization of trials [75]. Explaining why a patient was specifically chosen to participate, how they may personally benefit, and how their participation will contribute to the scientific community and future patients may improve patient enrollment. Furthermore, nudges can be directed towards providers. Clinicians, for example, can be nudged by incorporating the trial information in the clinical pathway of electronic health records [69]. Including the trial information in electronic health records may improve clinician awareness of potentially beneficial trials regarding their patients and help facilitate enrollment of older adults.
Evidence on the use of nudges to influence patient and clinician behavior in the context of clinical trials is still in its infancy. Further research is warranted to examine whether these strategies can be employed to influence enrollment and how they can be successfully implemented (e.g., feasible, acceptable, and sustainable) in both academic and community-based cancer practices.

7. Conclusions

The underrepresentation of older adults in cancer clinical trials is undeniably a multifaceted problem that requires a multifaceted solution. There have been promising steps toward improving trial participation among older adults, but there are still significant gaps in knowledge that hinder older patients from receiving high-quality, individualized, evidence-based care. The recommendations presented in this review range from small changes that can be adopted by individuals and research teams to large-scale, systemic changes that are needed at the institutional and/or policy level. Regardless, multiple steps are needed on multiple fronts across a collaborative network of academic and community-based cancer practices in order to have a cumulative and palpable effect. An investment in academic and community clinical trial partnerships could help to further cancer research, and more importantly, ensure that older adults with cancer have equal access to new treatments and advances in care.

Author Contributions

Conceptualization, J.L., E.G., A.T., S.P., and M.S.; Writing—Original Draft Preparation, J.L., E.G., A.T., S.P., and M.S.; Writing—Review and Editing, all authors; Visualization, J.L. and A.T.; Supervision, M.S.; Project Administration, M.S.; Funding Acquisition, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institute of Aging (NIA R03AG064377), the National Cancer Institute (NCI K12CA001727), the Waisman Innovation Fund, and Circle 1500. The funder had no role in the design and conduct of the study; in the collection, management, analysis, and interpretation of the data; in the preparation, review, or approval of the manuscript; or in the decision to submit the manuscript for publication.

Acknowledgments

We dedicate this work to the late Dr. Arti Hurria, a leader in geriatric oncology and an advocate for expanding inclusion of older adults in cancer clinical trials. We hope this manuscript will help carry Dr. Hurria’s legacy forward and foster new collaborations in a way that she would have advocated for.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Howlader, N.; Noone, A.M.; Krapcho, M.; Miller, D.; Brest, A.; Yu, M.; Ruhl, J.; Tatalovich, Z.; Mariotto, A.; Lewis, D.R.; et al. SEER Cancer Statistics Review, 1975–2017; National Cancer Institute: Bethesda, MD, USA, 2020. Available online: https://seer.cancer.gov/csr/1975_2017/ (accessed on 1 April 2020).
  2. PRB. Countries with the Oldest Populations in the World, March 23, 2020 ed.; Population Reference Bureau: Washington, DC, USA, 2020. [Google Scholar]
  3. Smith, B.D.; Smith, G.L.; Hurria, A.; Hortobagyi, G.N.; Buchholz, T.A. Future of cancer incidence in the United States: Burdens upon an aging, changing nation. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2009, 27, 2758–2765. [Google Scholar] [CrossRef] [PubMed]
  4. Mather, M.; Jacobsen, L.A.; Pollard, K.M. Population Bulletin: Aging in the United States, 2015. Available online: http://www.prb.org/pdf16/aging-us-population-bulletin.pdf (accessed on 1 April 2020).
  5. Bluethmann, S.M.; Mariotto, A.B.; Rowland, J.H. Anticipating the “Silver Tsunami”: Prevalence Trajectories and Comorbidity Burden among Older Cancer Survivors in the United States. Cancer Epidemiol. Biomark. Prev. 2016, 25, 1029–1036. [Google Scholar] [CrossRef] [Green Version]
  6. Hurria, A.; Naylor, M.; Cohen, H.J. Improving the Quality of Cancer Care in an Aging Population: Recommendations from an IOM Report. JAMA 2013, 310, 1795–1796. [Google Scholar] [CrossRef] [PubMed]
  7. Talarico, L.; Chen, G.; Pazdur, R. Enrollment of Elderly Patients in Clinical Trials for Cancer Drug Registration: A 7-Year Experience by the US Food and Drug Administration. J. Clin. Oncol. 2004, 22, 4626–4631. [Google Scholar] [CrossRef] [PubMed]
  8. Sedrak, M.S.; Hurria, A.; Li, D.; George, K.; Padam, S.; Liu, J.; Wong, A.R.; Vargas, N.; Eskandar, J.; Katheria, V.; et al. Barriers to clinical trial enrollment of older adults with cancer: A systematic review. J. Clin. Oncol. 2019, 37, e18130. [Google Scholar] [CrossRef]
  9. Singh, H.; Kanapuru, B.; Smith, C.; Fashoyin-Aje, L.A.; Myers, A.; Kim, G.; Pazdur, R. FDA analysis of enrollment of older adults in clinical trials for cancer drug registration: A 10-year experience by the U.S. Food and Drug Administration. J. Clin. Oncol. 2017, 35, 10009. [Google Scholar] [CrossRef]
  10. Singh, H.; Beaver, J.A.; Kim, G.; Pazdur, R. Enrollment of older adults on oncology trials: An FDA perspective. J. Geriatr. Oncol. 2017, 8, 149–150. [Google Scholar] [CrossRef]
  11. Sedrak, M.S.; Mohile, S.G.; Sun, V.; Sun, C.L.; Chen, B.T.; Li, D.; Wong, A.R.; George, K.; Padam, S.; Liu, J.; et al. Barriers to clinical trial enrollment of older adults with cancer: A qualitative study of the perceptions of community and academic oncologists. J. Geriatr. Oncol. 2020, 11, 327–334. [Google Scholar] [CrossRef] [Green Version]
  12. Townsley, C.A.; Selby, R.; Siu, L.L. Systematic review of barriers to the recruitment of older patients with cancer onto clinical trials. J. Clin. Oncol. 2005, 23, 3112–3124. [Google Scholar] [CrossRef]
  13. Wong, A.R.; Sun, V.; George, K.; Liu, J.; Padam, S.; Chen, B.A.; George, T.; Amini, A.; Li, D.; Sedrak, M.S. Barriers to Participation in Therapeutic Clinical Trials as Perceived by Community Oncologists. JCO Oncol. Pract. 2020. JOP.19.00662. [Google Scholar] [CrossRef]
  14. Nabhan, C.; Jeune-Smith, Y.; Klinefelter, P.; Kelly, R.J.; Feinberg, B.A. Challenges, Perceptions, and Readiness of Oncology Clinicians for the MACRA Quality Payment Program. JAMA Oncol. 2018, 4, 252–253. [Google Scholar] [CrossRef] [PubMed]
  15. Committee on Improving the Quality of Cancer Care: Addressing the Challenges of an Aging Population, B.o.H.C.S.; Institute of Medicine. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis; Levit, L., Balogh, E., Nass, S., Ganz, P.A., Eds.; National Academies Press: Washington, DC, USA, 2013. [Google Scholar] [CrossRef] [Green Version]
  16. Hurria, A.; Levit, L.A.; Dale, W.; Mohile, S.G.; Muss, H.B.; Fehrenbacher, L.; Magnuson, A.; Lichtman, S.M.; Bruinooge, S.S.; Soto-Perez-de-Celis, E.; et al. Improving the Evidence Base for Treating Older Adults with Cancer: American Society of Clinical Oncology Statement. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2015, 33, 3826–3833. [Google Scholar] [CrossRef] [PubMed]
  17. Levit, L.A.; Singh, H.; Klepin, H.D.; Hurria, A. Expanding the Evidence Base in Geriatric Oncology: Action Items From an FDA-ASCO Workshop. J. Natl. Cancer Inst. 2018, 110, 1163–1170. [Google Scholar] [CrossRef] [PubMed]
  18. Oncology Center of Excellence, Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) at the Food and Drug Administration. Inclusion of Older Adults in Cancer Clinical Trials, Guidance for Industry; U.S. Department of Health and Human Services, Food and Drug Administration: Silver Spring, MD, USA, 2020.
  19. Kornblith, A.B.; Kemeny, M.; Peterson, B.L.; Wheeler, J.; Crawford, J.; Bartlett, N.; Fleming, G.; Graziano, S.; Muss, H.; Cohen, H.J. Survey of oncologists’ perceptions of barriers to accrual of older patients with breast carcinoma to clinical trials. Cancer 2002, 95, 989–996. [Google Scholar] [CrossRef]
  20. McCleary, N.J.; Hubbard, J.; Mahoney, M.R.; Meyerhardt, J.A.; Sargent, D.; Venook, A.; Grothey, A. Challenges of conducting a prospective clinical trial for older patients: Lessons learned from NCCTG N0949 (alliance). J. Geriatr. Oncol. 2018, 9, 24–31. [Google Scholar] [CrossRef]
  21. Hamaker, M.E.; Seynaeve, C.; Nortier, J.W.; Wymenga, M.; Maartense, E.; Boven, E.; van Leeuwen-Stok, A.E.; de Rooij, S.E.; van Munster, B.C.; Smorenburg, C.H. Slow accrual of elderly patients with metastatic breast cancer in the Dutch multicentre OMEGA study. Breast 2013, 22, 556–559. [Google Scholar] [CrossRef]
  22. Freedman, R.A.; Dockter, T.J.; Lafky, J.M.; Hurria, A.; Muss, H.J.; Cohen, H.J.; Jatoi, A.; Kemeny, M.M.; Ruddy, K.J. Promoting Accrual of Older Patients with Cancer to Clinical Trials: An Alliance for Clinical Trials in Oncology Member Survey (A171602). Oncology 2018, 23, 1016–1023. [Google Scholar] [CrossRef] [Green Version]
  23. Lichtman, S.M.; Harvey, R.D.; Smit, M.-A.D.; Rahman, A.; Thompson, M.A.; Roach, N.; Schenkel, C.; Bruinooge, S.S.; Cortazar, P.; Walker, D.; et al. Modernizing Clinical Trial Eligibility Criteria: Recommendations of the American Society of Clinical Oncology–Friends of Cancer Research Organ Dysfunction, Prior or Concurrent Malignancy, and Comorbidities Working Group. J. Clin. Oncol. 2017, 35, 3753–3759. [Google Scholar] [CrossRef]
  24. Unger, J.M.; Cook, E.; Tai, E.; Bleyer, A. The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies. Am. Soc. Clin. Oncol. Educ. Book 2016, 35, 185–198. [Google Scholar] [CrossRef] [Green Version]
  25. Kim, E.S.; Bruinooge, S.S.; Roberts, S.; Ison, G.; Lin, N.U.; Gore, L.; Uldrick, T.S.; Lichtman, S.M.; Roach, N.; Beaver, J.A.; et al. Broadening Eligibility Criteria to Make Clinical Trials More Representative: American Society of Clinical Oncology and Friends of Cancer Research Joint Research Statement. J. Clin. Oncol. 2017, 35, 3737–3744. [Google Scholar] [CrossRef]
  26. Vaughan, C.P.; Dale, W.; Allore, H.G.; Binder, E.F.; Boyd, C.M.; Bynum, J.P.W.; Gurwitz, J.H.; Lundebjerg, N.E.; Trucil, D.E.; Supiano, M.A.; et al. AGS Report on Engagement Related to the NIH Inclusion Across the Lifespan Policy. J. Am. Geriatr. Soc. 2019, 67, 211–217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Wildiers, H.; Mauer, M.; Pallis, A.; Hurria, A.; Mohile, S.G.; Luciani, A.; Curigliano, G.; Extermann, M.; Lichtman, S.M.; Ballman, K.; et al. End points and trial design in geriatric oncology research: A joint European organisation for research and treatment of cancer—Alliance for Clinical Trials in Oncology—International Society Of Geriatric Oncology position article. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2013, 31, 3711–3718. [Google Scholar] [CrossRef] [PubMed]
  28. McKenna, R.J., Sr. Clinical aspects of cancer in the elderly. Treatment decisions, treatment choices, and follow-up. Cancer 1994, 74, 2107–2117. [Google Scholar] [CrossRef]
  29. BrintzenhofeSzoc, K.; Krok-Schoen, J.L.; Canin, B.; Parker, I.; MacKenzie, A.R.; Koll, T.; Vankina, R.; Hsu, C.D.; Jang, B.; Pan, K.; et al. The underreporting of phase III chemo-therapeutic clinical trial data of older patients with cancer: A systematic review. J. Geriatr. Oncol. 2020, 11, 369–379. [Google Scholar] [CrossRef]
  30. Mohile, S.G.; Hurria, A.; Cohen, H.J.; Rowland, J.H.; Leach, C.R.; Arora, N.K.; Canin, B.; Muss, H.B.; Magnuson, A.; Flannery, M.; et al. Improving the quality of survivorship for older adults with cancer. Cancer 2016, 122, 2459–2568. [Google Scholar] [CrossRef]
  31. Sacks, C.A.; Miller, P.W.; Longo, D.L. Talking about Toxicity—“What We’ve Got Here Is a Failure to Communicate”. N. Engl. J. Med. 2019, 381, 1406–1408. [Google Scholar] [CrossRef]
  32. Handforth, C.; Hall, P.; Marshall, H.; Seymour, M. Overall treatment utility: A novel outcome measure to convey the balance of benefits and harms from cancer treatment. J. Geriatr. Oncol. 2013, 4, S49. [Google Scholar] [CrossRef]
  33. Sloan, J.A.; Mahoney, M.R.; Sargent, D.J.; Hubbard, J.M.; Liu, H.; Basch, E.M.; Shields, A.F.; Chan, E.; Goldberg, R.M.; Gill, S.; et al. Was it worth it (WIWI)? Patient satisfaction with clinical trial participation: Results from North Central Cancer Treatment Group (NCCTG) phase III trial N0147. J. Clin. Oncol. 2011, 29, 6122. [Google Scholar] [CrossRef]
  34. Dueck, A.C.; Mendoza, T.R.; Mitchell, S.A.; Reeve, B.B.; Castro, K.M.; Rogak, L.J.; Atkinson, T.M.; Bennett, A.V.; Denicoff, A.M.; O’Mara, A.M.; et al. Validity and Reliability of the US National Cancer Institute’s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). JAMA Oncol. 2015, 1, 1051–1059. [Google Scholar] [CrossRef]
  35. Lowsky, D.J.; Olshansky, S.J.; Bhattacharya, J.; Goldman, D.P. Heterogeneity in Healthy Aging. J. Gerontol. Ser. A 2013, 69, 640–649. [Google Scholar] [CrossRef] [Green Version]
  36. Hurria, A.; Wildes, T.; Blair, S.L.; Browner, I.S.; Cohen, H.J.; Deshazo, M.; Dotan, E.; Edil, B.H.; Extermann, M.; Ganti, A.K.; et al. Senior adult oncology, version 2.2014: Clinical practice guidelines in oncology. J. Natl. Compr. Cancer Netw. JNCCN 2014, 12, 82–126. [Google Scholar] [CrossRef] [PubMed]
  37. Soto-Perez-de-Celis, E.; Li, D.; Yuan, Y.; Lau, Y.M.; Hurria, A. Functional versus chronological age: Geriatric assessments to guide decision making in older patients with cancer. Lancet. Oncol. 2018, 19, e305–e316. [Google Scholar] [CrossRef]
  38. Mohile, S.G.; Epstein, R.M.; Hurria, A.; Heckler, C.E.; Canin, B.; Culakova, E.; Duberstein, P.; Gilmore, N.; Xu, H.; Plumb, S.; et al. Communication With Older Patients With Cancer Using Geriatric Assessment: A Cluster-Randomized Clinical Trial From the National Cancer Institute Community Oncology Research Program. JAMA Oncol. 2020, 6, 196–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Hurria, A.; Dale, W.; Mooney, M.; Rowland, J.H.; Ballman, K.V.; Cohen, H.J.; Muss, H.B.; Schilsky, R.L.; Ferrell, B.; Extermann, M.; et al. Designing therapeutic clinical trials for older and frail adults with cancer: U13 conference recommendations. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2014, 32, 2587–2594. [Google Scholar] [CrossRef]
  40. Association of Community ‘Cancer Centers. Multidisciplinary Approaches to Caring for Older Adults with Cancer; Association of Community Cancer Centers: Rockville, MD, USA, 2019. [Google Scholar]
  41. Hurria, A.; Siccion, E.P. Assessing the ‘fit’ older patient for chemotherapy. Oncology 2014, 28, 598–599. [Google Scholar]
  42. Cupit-Link, M.C.; Kirkland, J.L.; Ness, K.K.; Armstrong, G.T.; Tchkonia, T.; LeBrasseur, N.K.; Armenian, S.H.; Ruddy, K.J.; Hashmi, S.K. Biology of premature ageing in survivors of cancer. Esmo Open 2017, 2, e000250. [Google Scholar] [CrossRef] [Green Version]
  43. Hill, A.; Sadda, J.; LaBarge, M.A.; Hurria, A. How cancer therapeutics cause accelerated aging: Insights from the hallmarks of aging. J. Geriatr. Oncol. 2020, 11, 191–193. [Google Scholar] [CrossRef] [Green Version]
  44. Nipp, R.D.; Yao, N.A.; Lowenstein, L.M.; Buckner, J.C.; Parker, I.R.; Gajra, A.; Morrison, V.A.; Dale, W.; Ballman, K.V. Pragmatic study designs for older adults with cancer: Report from the U13 conference. J. Geriatr. Oncol. 2016, 7, 234–241. [Google Scholar] [CrossRef]
  45. Papadimitrakopoulou, V.; Lee, J.J.; Wistuba, I.I.; Tsao, A.S.; Fossella, F.V.; Kalhor, N.; Gupta, S.; Byers, L.A.; Izzo, J.G.; Gettinger, S.N.; et al. The BATTLE-2 Study: A Biomarker-Integrated Targeted Therapy Study in Previously Treated Patients With Advanced Non–Small-Cell Lung Cancer. J. Clin. Oncol. 2016, 34, 3638–3647. [Google Scholar] [CrossRef]
  46. Park, J.W.; Liu, M.C.; Yee, D.; Yau, C.; van’t Veer, L.J.; Symmans, W.F.; Paoloni, M.; Perlmutter, J.; Hylton, N.M.; Hogarth, M.; et al. Adaptive Randomization of Neratinib in Early Breast Cancer. N. Engl. J. Med. 2016, 375, 11–22. [Google Scholar] [CrossRef]
  47. Bhatt, D.L.; Mehta, C. Adaptive Designs for Clinical Trials. N. Engl. J. Med. 2016, 375, 65–74. [Google Scholar] [CrossRef] [PubMed]
  48. Lillie, E.O.; Patay, B.; Diamant, J.; Issell, B.; Topol, E.J.; Schork, N.J. The n-of-1 clinical trial: The ultimate strategy for individualizing medicine? Pers. Med. 2011, 8, 161–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Blair, T.; Minkler, M. Participatory Action Research With Older Adults: Key Principles in Practice. The Gerontologist 2009, 49, 651–662. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Higginbottom, G.; Liamputtong, P. Participatory Qualitative Research Methodologies in Health; SAGE Publications Ltd: London, UK, 2015. [Google Scholar] [CrossRef]
  51. Dimond, E.P.; St. Germain, D.; Nacpil, L.M.; Zaren, H.A.; Swanson, S.M.; Minnick, C.; Carrigan, A.; Denicoff, A.M.; Igo, K.E.; Acoba, J.D.; et al. Creating a “culture of research” in a community hospital: Strategies and tools from the National Cancer Institute Community Cancer Centers Program. Clin. Trials 2015, 12, 246–256. [Google Scholar] [CrossRef] [Green Version]
  52. McAlearney, A.S.; Reiter, K.L.; Weiner, B.J.; Minasian, L.; Song, P.H. Challenges and facilitators of community clinical oncology program participation: A qualitative study. J. Healthc. Manag. 2013, 58, 29–46. [Google Scholar] [CrossRef]
  53. Likumahuwa, S.; Song, H.; Singal, R.; Weir, R.C.; Crane, H.; Muench, J.; Sim, S.-C.; DeVoe, J.E. Building Research Infrastructure in Community Health Centers: A Community Health Applied Research Network (CHARN) Report. J. Am. Board Fam. Med. 2013, 26, 579–587. [Google Scholar] [CrossRef] [Green Version]
  54. Minasian, L.M.; Unger, J.M. What Keeps Patients Out of Clinical Trials? JCO Oncol. Pract. 2020, 16, 125–127. [Google Scholar] [CrossRef]
  55. Shahrokni, A.; Kim, S.J.; Bosl, G.J.; Korc-Grodzicki, B. How We Care for an Older Patient With Cancer. J. Oncol. Pr. 2017, 13, 95–102. [Google Scholar] [CrossRef] [Green Version]
  56. Chapman, A.E.; Swartz, K.; Schoppe, J.; Arenson, C. Development of a comprehensive multidisciplinary geriatric oncology center, the Thomas Jefferson University Experience. J. Geriatr. Oncol. 2014, 5, 164–170. [Google Scholar] [CrossRef]
  57. Magnuson, A.; Dale, W.; Mohile, S. Models of Care in Geriatric Oncology. Curr. Geriatr. Rep. 2014, 3, 182–189. [Google Scholar] [CrossRef]
  58. Greenwald, P.; Stern, M.E.; Clark, S.; Sharma, R. Older adults and technology: In telehealth, they may not be who you think they are. Int. J. Emerg. Med. 2018, 11, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Gellis, Z.D.; Kenaley, B.; McGinty, J.; Bardelli, E.; Davitt, J.; Ten Have, T. Outcomes of a Telehealth Intervention for Homebound Older Adults With Heart or Chronic Respiratory Failure: A Randomized Controlled Trial. The Gerontologist 2012, 52, 541–552. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Weinstock, R.S.; Teresi, J.A.; Goland, R.; Izquierdo, R.; Palmas, W.; Eimicke, J.P.; Ebner, S.; Shea, S.; Consortium, I.D. Glycemic control and health disparities in older ethnically diverse underserved adults with diabetes: Five-year results from the Informatics for Diabetes Education and Telemedicine (IDEATel) study. Diabetes Care 2011, 34, 274–279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Lum, H.D.; Nearing, K.; Pimentel, C.B.; Levy, C.R.; Hung, W.W. Anywhere to Anywhere: Use of Telehealth to Increase Health Care Access for Older, Rural Veterans. Public Policy Aging Rep. 2019, 30, 12–18. [Google Scholar] [CrossRef]
  62. Merrell, R.C. Geriatric Telemedicine: Background and Evidence for Telemedicine as a Way to Address the Challenges of Geriatrics. Healthc Inf. Res 2015, 21, 223–229. [Google Scholar] [CrossRef] [Green Version]
  63. Cimperman, M.; Brenčič, M.M.; Trkman, P.; Stanonik, M.d.L. Older adults’ perceptions of home telehealth services. Telemed. eHealth 2013, 19, 786–790. [Google Scholar] [CrossRef]
  64. Liu, R.; Sundaresan, T.; Reed, M.E.; Trosman, J.R.; Weldon, C.B.; Kolevska, T. Telehealth in Oncology During the COVID19 Outbreak: Bringing the house call back virtually. JCO Oncol. Pract. 2020. [Google Scholar] [CrossRef]
  65. Waterhouse, D.; Harvey, R.D.; Hurley, P.; Levit, L.A.; Kim, E.S.; Klepin, H.D.; Mileham, K.F.; Nowakowski, G.; Schenkel, C.; Davis, C.; et al. Early Impact of COVID-19 on the Conduct of Oncology Clinical Trials and Long-term Opportunities for Transformation: Findings from an American Society of Clinical Oncology Survey. JCO Oncol. Pract. Press 2020. [Google Scholar] [CrossRef]
  66. Hede, K. Teleoncology Gaining Acceptance with Physicians, Patients. JNCI J. Natl. Cancer Inst. 2010, 102, 1531–1533. [Google Scholar] [CrossRef] [Green Version]
  67. Chien, L.; Roberts, E.; Soto-Perez-de-Celis, E.; Katheria, V.; Hite, S.; Tran, R.; Bhatt, D.; Donner, A.; Burhenn, P.; Charles, K.; et al. Telehealth in geriatric oncology: A novel approach to deliver multidisciplinary care for older adults with cancer. J. Geriatr. Oncol. 2020, 11, 197–199. [Google Scholar] [CrossRef] [Green Version]
  68. Thaler, R.; Sunstein, C. Nudge: Improving Decisions about Health, Wellness, and Happiness; Penguin Random House: New York, NY, USA, 2009. [Google Scholar]
  69. Patel, M.S.; Volpp, K.G.; Asch, D.A. Nudge Units to Improve the Delivery of Health Care. N. Engl. J. 2018, 378, 214–216. [Google Scholar] [CrossRef] [PubMed]
  70. Arno, A.; Thomas, S. The efficacy of nudge theory strategies in influencing adult dietary behaviour: A systematic review and meta-analysis. BMC Public Health 2016, 16, 676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Kulendran, M.; King, D.; Schmidtke, K.A.; Curtis, C.; Gately, P.; Darzi, A.; Vlaev, I. The use of commitment techniques to support weight loss maintenance in obese adolescents. Psychol. Health 2016, 31, 1332–1341. [Google Scholar] [CrossRef] [PubMed]
  72. Patel, M.S.; Kurtzman, G.W.; Kannan, S.; Small, D.S.; Morris, A.; Honeywell, S., Jr.; Leri, D.; Rareshide, C.A.L.; Day, S.C.; Mahoney, K.B.; et al. Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial. JAMA Netw. Open 2018, 1, e180818. [Google Scholar] [CrossRef] [PubMed]
  73. Forberger, S.; Reisch, L.; Kampfmann, T.; Zeeb, H. Nudging to move: A scoping review of the use of choice architecture interventions to promote physical activity in the general population. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 77. [Google Scholar] [CrossRef] [Green Version]
  74. Landais, L.L.; Damman, O.C.; Schoonmade, L.J.; Timmermans, D.R.M.; Verhagen, E.A.L.M.; Jelsma, J.G.M. Choice architecture interventions to change physical activity and sedentary behavior: A systematic review of effects on intention, behavior and health outcomes during and after intervention. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 47. [Google Scholar] [CrossRef] [Green Version]
  75. VanEpps, E.M.; Volpp, K.G.; Halpern, S.D. A nudge toward participation: Improving clinical trial enrollment with behavioral economics. Sci. Transl. Med. 2016, 8, 348fs313. [Google Scholar] [CrossRef] [Green Version]
Jcm 09 01571 g001 550
Figure 1. The barriers to older adult participation in cancer clinical trials are multifaceted and interrelated, with barriers existing at the system, clinician, patient, and caregiver levels. System-related barriers include trial design, overly stringent eligibility criteria, and lack of infrastructure support, appropriate and representative trials, and funding. Clinician-related barriers include concerns for side effects or toxicities, patient age, comorbid conditions, personal bias, costs to clinicians, and lack of time, support and staff, awareness of trials, and engagement amongst clinicians. Patient-related barriers include attitudes towards clinical trials, knowledge, side effects/toxicities, burden, and financial limitations. Caregiver-related barriers include caregiver burden and preference against participation.
Figure 1. The barriers to older adult participation in cancer clinical trials are multifaceted and interrelated, with barriers existing at the system, clinician, patient, and caregiver levels. System-related barriers include trial design, overly stringent eligibility criteria, and lack of infrastructure support, appropriate and representative trials, and funding. Clinician-related barriers include concerns for side effects or toxicities, patient age, comorbid conditions, personal bias, costs to clinicians, and lack of time, support and staff, awareness of trials, and engagement amongst clinicians. Patient-related barriers include attitudes towards clinical trials, knowledge, side effects/toxicities, burden, and financial limitations. Caregiver-related barriers include caregiver burden and preference against participation.
Jcm 09 01571 g001
Jcm 09 01571 g002 550
Figure 2. Strategies to connect academic centers and community-based practices. Ensuring collaboration between academic centers and community sites is essential in improving the participation of older adults in cancer research. First, academic centers should invest resources such as study personnel, regulatory oversight, and funding to community sites in order to help create trials and participation where most patients receive treatment. Second, both community sites and academic centers need to exchange tools, advice, and guidance to help increase the quality of trials being done and make sure that the needs of older adults are met. Lastly, community sites must have their clinicians participate in the designing of trials and eligibility criteria so that trials better fit the present patient population.
Figure 2. Strategies to connect academic centers and community-based practices. Ensuring collaboration between academic centers and community sites is essential in improving the participation of older adults in cancer research. First, academic centers should invest resources such as study personnel, regulatory oversight, and funding to community sites in order to help create trials and participation where most patients receive treatment. Second, both community sites and academic centers need to exchange tools, advice, and guidance to help increase the quality of trials being done and make sure that the needs of older adults are met. Lastly, community sites must have their clinicians participate in the designing of trials and eligibility criteria so that trials better fit the present patient population.
Jcm 09 01571 g002
Table
Table 1. Clinical trial designs for geriatric oncology research.
Table 1. Clinical trial designs for geriatric oncology research.
DesignDescription/CharacteristicsPotential Objectives and OutcomesAdvantagesLimitations
Pragmatic TrialIntervention typically performed in the context of standard care
Patients recruited from a variety of practice settings, using broader, more inclusive eligibility criteria		Determine the effectiveness of an intervention in day-to-day practice-More accessible
-Less resource-intensive
-Places minimal additional burden on participants
-Outcomes relevant to those who will use trial results		-Non-adherence and loss to follow-up
-Poor internal validity limits the ability to determine definitive effects
Randomized Controlled TrialSubjects are randomly assigned to treatment arms
To generate data on the older patient population:
-Accrue only older adults,
-Stratify enrollment into age groups
Adaptive design: modifications are made as the study proceeds based on interim data analysis		Compare efficacy and tolerability of different treatments regimens-Direct comparison of treatment regimens
-Unbiased
-Minimizes confounding
-Generalizable to the overall population being studied		-Requires a large sample size
-Costly and time-consuming
-Logistically demanding
-Slow accrual
Prospective Cohort StudyAssessment of treatments already approved by the FDA
Cohort defined by host, tumor, or treatment factors
Observational
Hypothesis-driven		Understand decision-making, patterns of care-Findings are generalizable-Lack of randomization
-Requires a large sample size
-Logistically demanding
Extended TrialAddition of a cohort of older patients to the superior treatment armDetermine tolerability in older adultsTrial infrastructure already established
Existing data on treatment efficacy will make accrual of older patients easier		-Lack of data on the inferior treatment arm in older adults
Embedded Study (Correlative or Ancillary Study)Includes additional measures of interest in the infrastructure of a parent studyDescribe a cohort or understand the impact of treatment using geriatric assessment measures-Better understand the characteristics of the older patient population
-Identification of predictors of functional decline		-In studies not specific to older adults, the sample size of older patients may be limited
N-of-1 (Single-Subject Trial)Determines the optimal therapy for a single individualDetermine the optimal or best intervention for an individual patient using objective data-driven criteria-Individualized medicine
-Gain insights into comparative treatment effectiveness among a wide variety of patients		-Randomization of treatment order
-Carryover effects
-Wash-out periods
-Blinding

Share and Cite

MDPI and ACS Style

Liu, J.; Gutierrez, E.; Tiwari, A.; Padam, S.; Li, D.; Dale, W.; Pal, S.K.; Stewart, D.; Subbiah, S.; Bosserman, L.D.; et al. Strategies to Improve Participation of Older Adults in Cancer Research. J. Clin. Med. 2020, 9, 1571. https://doi.org/10.3390/jcm9051571

AMA Style

Liu J, Gutierrez E, Tiwari A, Padam S, Li D, Dale W, Pal SK, Stewart D, Subbiah S, Bosserman LD, et al. Strategies to Improve Participation of Older Adults in Cancer Research. Journal of Clinical Medicine. 2020; 9(5):1571. https://doi.org/10.3390/jcm9051571

Chicago/Turabian Style

Liu, Jennifer, Eutiquio Gutierrez, Abhay Tiwari, Simran Padam, Daneng Li, William Dale, Sumanta K. Pal, Daphne Stewart, Shanmugga Subbiah, Linda D. Bosserman, and et al. 2020. "Strategies to Improve Participation of Older Adults in Cancer Research" Journal of Clinical Medicine 9, no. 5: 1571. https://doi.org/10.3390/jcm9051571

APA Style

Liu, J., Gutierrez, E., Tiwari, A., Padam, S., Li, D., Dale, W., Pal, S. K., Stewart, D., Subbiah, S., Bosserman, L. D., Presant, C., Phillips, T., Yap, K., Hill, A., Bhatt, G., Yeon, C., Cianfrocca, M., Yuan, Y., Mortimer, J., & Sedrak, M. S. (2020). Strategies to Improve Participation of Older Adults in Cancer Research. Journal of Clinical Medicine, 9(5), 1571. https://doi.org/10.3390/jcm9051571

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop