SUMMARY
Aim:
This study aims to determine whether socioeconomic status (SES) is associated with participation in melanoma clinical trials.
Patients & methods:
A retrospective, single-center observational study was conducted at the Melanoma Institute Australia. Factors affecting clinical trial participation were assessed using logistic regression.
Results:
Of 9074 patients, 2304 (25%) participated in a clinical trial. Multivariate analysis indicated males compared with females (odds ratio [OR]: 1.18; 95% CI: 1.07–1.30) and patients with American Joint Cancer Committee stage II or III disease (but not stage IV disease) were more likely to participate in trials than patients with stage I disease (OR: 2.81 [95% CI: 2.50–3.16] and OR: 4.55 [95% CI: 3.91–5.30], respectively). SES did not affect trial participation.
Conclusion:
Our data suggest that SES is not a significant predictor of melanoma clinical trial participation when adjusted for other factors.
KEYWORDS : clinical trials, equity, melanoma, participation, socioeconomic status
Summary points.
The importance of recruiting a wide range of subjects to melanoma clinical trials
In the last decade alone, evidence from major melanoma trials has changed the way diagnostic imaging, surgery, chemotherapy, radiotherapy and immunotherapy are practiced and now tailored to suit the individual.
In order to ensure the generalizability of the results from clinical trials, a wide range of subjects needs to be recruited.
People of low socioeconomic status (SES) have been reported to miss out on the opportunity to participate in many cancer clinical trials.
Study methods
We performed a retrospective, single-center observational study at the Melanoma Institute Australia and assessed the factors affecting melanoma clinical trial participation using logistic regression.
Factors predicting clinical trial participation
Clinical trial participation occurred in 2304 out of 9074 patients (25%), with surgical trials recruiting the largest number of participants (1453; 63%).
Multivariate analyses showed a lower likelihood of trial participation with each year of increasing age (odds ratio [OR]: 0.99; 95% CI: 0.98–0.99) and males were more likely to participate than females (OR: 1.18; 95% CI: 1.07–1.30). Patients with American Joint Cancer Committee stage II or III disease at initial presentation were much more likely to participate than patients with stage I disease (OR: 2.81 [95% CI: 2.50–3.16] and OR: 4.55 [95% CI: 3.91–5.30], respectively).
SES did not affect trial participation.
Implications for policy & practice
The identification of ‘low-participation’ groups could help tailor recruitment strategies in order to ensure the representative participation of those who are affected by melanoma.
Explanations for why men participate in melanoma trials more than women may include the exclusion criteria for women who are pregnant or planning to become pregnant, financial barriers or perceived out-of-pocket costs associated with participation or insufficient time to participate as a consequence of family care-giving responsibilities.
The reporting of the SES of trial participants enables judgments to be made regarding the generalizability of the trial findings.
Cutaneous melanoma imposes a heavy burden of morbidity and mortality on fair-skinned populations worldwide [1–3]. The use of clinical trials in order to evaluate the efficacy of new treatments is fundamental to guiding future clinical practice and improving melanoma health outcomes. In the last decade alone, evidence from major melanoma trials has changed the way diagnostic imaging, surgery, chemotherapy, radiotherapy and immunotherapy are practiced and now tailored to suit the individual [4–7]. In order to ensure the generalizability of results from clinical trials, a wide range of subjects needs to be recruited. This includes variation in patient and disease-related characteristics, including participant age, sex, socioeconomic status (SES), geographic residence, melanoma subtype, genetic factors, comorbid conditions and sun protection behaviors [8].
People of low SES have been reported to miss out on the opportunity to participate in clinical trials [9]. This not only raises an issue of potential inequity, assuming there are benefits to be gained from trial participation regardless of intervention allocation, but also hampers the ability to generalize the results to all sectors of the community. In the past, melanoma was considered to be a disease that affected more affluent populations; however, there has recently been a rise in the incidence of melanoma in the more socially deprived areas of the UK [10] and poorer coastal towns in Australia [11]. In fact, in 2011, nine of the ten local government areas with the highest standardized incidence ratio of melanoma in New South Wales, Australia, were in areas of relative socioeconomic disadvantage [11].
Low SES has been reported to be a barrier to participation in clinical trials of other cancers due to several factors, including the costs associated with trial participation (e.g., additional specialist visits or out-of-pocket payments for diagnostic tests) [12,13], logistic barriers to attending multiple services in multiple centers, particularly for the assessment of trial eligibility (screening) [14,15] and associated cultural or linguistic barriers [16,17]. The single study of trial participation we identified in the melanoma population (albeit ocular melanoma) found somewhat different results from those in many other cancers, whereby people of an older age (≥60 years), lower education level and those with nonmanagerial jobs were more likely to participate in a clinical trial than their younger, more educated counterparts [18]. The aim of the present study is therefore to determine whether SES is associated with participation in clinical trials for people with cutaneous melanoma. Our hypotheses were that low SES would be negatively associated with trials participation and that close proximity to the trial center would be positively associated with participation. Our research report follows the format of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [19].
Patients & methods
• Study design
We conducted a retrospective, single-center observational study among consecutive patients of the Melanoma Institute Australia (MIA) diagnosed with cutaneous melanoma between 1 January 1995 and 31 December 2010. Based in Sydney, Australia, the MIA is one of the largest melanoma treatment centers in the world. The center hosts a range of clinical trials for all American Joint Cancer Committee (AJCC) stages of melanoma across the disciplines of surgery, radiotherapy, chemotherapy, immunotherapy and the behavioral sciences. Data for this study were obtained from the MIA research database, in which patients have given consent for their records to be used for research studies. Data were extracted in December 2012, which allowed sufficient time for the data entry of initial treatment and outcomes for patients diagnosed in the final year of the study. The information that was collected included demographic data, detailed clinical treatment data, diagnostic investigations data, clinical trial participation data, including trial type, follow-up consultations and relevant melanoma, and general health outcomes data.
Patients of any age with a biopsy-proven invasive cutaneous melanoma and any AJCC stage of disease at initial presentation were included. Patients referred from overseas centers who did not have an Australian residential address were excluded from the study. Follow-up after initial treatment was recorded by clinicians at the MIA and via correspondence received from other health professionals, such as the patient's primary care physician.
The primary outcome was participation in a clinical trial (yes/no) at any time during the patient's melanoma treatment or follow-up. A clinical trial was defined as an experimental research study of any phase (i.e., Phase I to Phase IV) that required written informed consent and was not a part of routine melanoma care. The clinical trial could be randomized or nonrandomized and could involve an intervention from any discipline or combination of disciplines (e.g., surgery and immunotherapy). Explanatory variables included patient characteristics, such as age at melanoma diagnosis, sex, SES, country of birth, marital status, residential address and year of diagnosis, as well as tumor characteristics, such as Breslow thickness, ulceration, histopathological subtype, tumor mitotic rate, anatomical body site, AJCC stage of disease at initial presentation, date of last follow-up and follow-up status. SES was calculated using the Index for Relative Socioeconomic Advantage and Disadvantage based on Australian residence census collection districts, which is described in detail below [20].
Three variables for SES and access to healthcare were used: the Index for Relative Socioeconomic Advantage and Disadvantage, which categorizes census collection districts (small areas of ˜200 similar households) across Australia into deciles of increasing SES, known as Socioeconomic Index For Areas (SEIFA) scores; the Accessibility Remoteness Index of Australia (ARIA), which allocates a remoteness index between 1 (metropolitan cities) to 5 (very remote areas) on the basis of access to essential services; and the straight-line distance from the patient's home residence to the MIA trials center. Each of these variables was generated in a similar fashion. First, the patient's residential addresses were geocoded into latitudes and longitudes, using the GPS Visualizer's Easy Batch geocoder [21]. Second, a specialized mapping software program (ArcGIS [22]) was used to map these coordinates to the 2006 Australian Bureau of Statistics census collection districts. Each district was assigned an individual SEIFA and ARIA score that was then attributed to each patient. Finally, the Euclidean or straight-line distances between the patients’ residences and the MIA were calculated using the ArcGIS software's spatial analyst function and reported in kilometers.
• Statistical methods
Our analyses were designed to examine the relevance of SES as a predictor of melanoma clinical trial participation. Variables with missing data for more than 30% of the study sample were excluded. Patient age, the SEIFA index deciles, the ARIA index quintiles and distance in kilometers were treated as continuous variables. AJCC stage of disease at initial presentation was treated as a categorical variable, with stage I as the referent group. Following univariate analyses and the consideration of confounding factors, a backward stepwise method was used in order to construct logistic regression models, where variables that had a p-value of greater than 0.25 were excluded. Goodness of fit was tested using the Hosmer and Lemeshow χ2 test and the results of the regression models were presented as odds ratios (ORs) for the likelihood of participating in a melanoma clinical trial. Ratios with a p-value of <0.05 were considered to be statistically significant.
Results
Between 1995 and 2010, 9074 patients with invasive melanoma were identified (mean age: 58 years; 57% males). The majority of patients (86%) were AJCC stage I or II at initial presentation, and the median Breslow tumor thickness was 1.10 mm (Table 1). The median distance between the patient's home and the MIA was 27 km (interquartile range: 12–87 km); over three-quarters of the patients lived in metropolitan cities, with the majority (82%) from areas of medium-to-high SES. Patients resided in all states of Australia, but most were from New South Wales. Three-quarters of the study cohort were diagnosed between 2004 and 2010 and the median follow-up or the whole group was 35 months (interquartile range: 13–73 months), with 915 out of 9074 patients (10%) lost to follow-up at the final recording of melanoma status.
Table 1. . Sociodemographic and tumor characteristics of the study sample (n = 9074).
| Factor | Patients (n) | Percentage |
|---|---|---|
| Age at diagnosis of primary melanoma | ||
| Median (IQR) | 58 (45–69) | – |
| ≤30 years | 685 | 7.5 |
| 31–50 years | 2535 | 27.9 |
| 51–70 years | 3801 | 41.9 |
| >70 years | 2053 | 22.6 |
| Sex | ||
| Males | 5126 | 56.5 |
| Females | 3948 | 43.5 |
| Birth country | ||
| Australia/New Zealand | 1401 | 15.4 |
| Outside Australia | 3933 | 43.3 |
| Not documented | 3740 | 41.2 |
| Marital status | ||
| Single | 291 | 3.2 |
| Married/de facto | 1264 | 13.9 |
| Divorced/widowed/separated | 169 | 1.9 |
| Not documented | 7350 | 81.0 |
| Socioeconomic status | ||
| Low (SEIFA deciles 1–3) | 1601 | 17.6 |
| Medium to high (SEIFA deciles 4–10) | 7473 | 82.4 |
| ARIA (remoteness index) | ||
| 1 (metropolitan cities) | 6949 | 76.6 |
| 2 (inner regional) | 1570 | 17.3 |
| 3 (outer regional) | 519 | 5.7 |
| 4 and 5 (remote and very remote) | 36 | 0.4 |
| Distance between home & melanoma clinic (km) | ||
| Median (IQR) | 27.0 (12.2–86.8) | – |
| ≤10 | 1855 | 20.4 |
| 10.1–30 | 2877 | 31.7 |
| 30.1–100 | 2265 | 25.0 |
| 100.1–400 | 1637 | 18.0 |
| >400 | 440 | 4.8 |
| Melanoma subtype | ||
| Acral lentiginous | 84 | 0.9 |
| Desmoplastic melanoma† | 565 | 6.2 |
| Lentigo maligna melanoma | 399 | 4.4 |
| Superficial spreading melanoma‡ | 4220 | 46.5 |
| Nodular melanoma | 1613 | 17.8 |
| Mixed§ | 2193 | 24.2 |
| AJCC stage | ||
| Stage I | 5543 | 61.1 |
| Stage II | 2268 | 25.0 |
| Stage III | 876 | 9.7 |
| Stage IV | 115 | 1.3 |
| Unclassified | 272 | 3.0 |
| Breslow thickness (mm) | ||
| Median (IQR) | 1.10 (0.60–2.20) | – |
| 0.01–1.0 | 4028 | 44.4 |
| 1.01–4.0 | 3748 | 41.3 |
| >4.0 | 900 | 9.9 |
| Not documented | 398 | 4.4 |
| Last follow-up status | ||
| Alive, no sign of recurrence | 6473 | 71.3 |
| Alive, status unknown | 130 | 1.4 |
| Alive, with melanoma | 316 | 3.5 |
| Dead, cause unknown | 223 | 2.5 |
| Dead, cause melanoma | 896 | 9.9 |
| Dead, cause not melanoma | 121 | 1.3 |
| Lost to follow-up | 915 | 10.1 |
†Desmoplastic melanoma (pure and with neurotropia).
‡Superficial spreading melanoma (pure, with Hutchinson's melanotic freckle and with nodular melanoma).
§Mixed, occult and malignant blue nevus plus histology unknown.
AJCC: American Joint Cancer Committee; ARIA: Accessibility Remoteness Index of Australia; IQR: Interquartile range; SEIFA: Socioeconomic Index For Areas.
Clinical trial participation occurred in 2304 out of 9074 patients (25%), with surgical trials recruiting the largest number of participants (1453, 63%; Table 2). Of the 2304 clinical trial participants, 1384 (60%) were male. Over 1000 AJCC stage I patients and 794 stage II patients at initial presentation were enrolled in trials over the 15-year study period.
Table 2. . Clinical trial type by American Joint Cancer Committee stage of disease at initial presentation.
| Clinical trial type | American Joint Cancer Committee stage of disease | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Stage I | Stage II | Stage III | Stage IV | Stage unknown | Total | |||||||
| n | % | n | % | n | % | n | % | n | % | n | % | |
| Chemotherapy | 7 | 11 | 22 | 34 | 25 | 38 | 3 | 5 | 8 | 12 | 65 | 3 |
| Follow-up of melanoma | 66 | 85 | 8 | 10 | 2 | 3 | – | – | – | – | 76 | 3 |
| Immunotherapy | 26 | 27 | 24 | 25 | 41 | 42 | 2 | 2 | 3 | 3 | 96 | 4 |
| Pathology/tumor bank | 216 | 42 | 147 | 28 | 90 | 17 | 11 | 2 | 26 | 5 | 490 | 21 |
| Psychosocial outcomes | 38 | 60 | 14 | 22 | 9 | 14 | – | – | 1 | 2 | 62 | 3 |
| Radiotherapy | 12 | 23 | 16 | 30 | 20 | 38 | 4 | 8 | 1 | 2 | 53 | 2 |
| Surgery | 638 | 44 | 561 | 39 | 238 | 16 | 3 | 0 | 13 | 1 | 1453 | 63 |
| Surgery/chemotherapy | – | – | 1 | 25 | 2 | 50 | 1 | 25 | – | – | 4 | <1 |
| Unknown | – | – | 1 | 20 | 3 | 60 | 1 | 20 | – | – | 5 | <1 |
| Total | 1003 | 43 | 794 | 34 | 430 | 18 | 25 | 1 | 52 | 2 | 2304 | 100 |
Univariate logistic regression analyses for the cohort showed that patient age, sex, SES, remoteness index and AJCC stage of disease at initial presentation were significant predictors of clinical trial participation. Notably, people from areas of higher SES were slightly less likely to participate than people from areas of low SES. Multivariate analyses showed that increasing age was associated with a lower likelihood of trial participation (OR: 0.99; 95% CI: 0.98–0.99) and males were more likely to participate than females (OR: 1.18; 95% CI: 1.07–1.30). Patients with AJCC stage II or III disease at initial presentation were much more likely to participate than patients with stage I disease (OR: 2.81 [95% CI: 2.50–3.16] and OR: 4.55 [95% CI 3.91–5.30], respectively; Table 3). Patients with stage IV disease were not significantly more likely to participate in trials than patients with stage I disease (OR: 1.39; 95% CI: 0.88–2.18). While there was a trend for patients from more geographically remote areas compared with less remote areas of Australia to participate in trials, this was not statistically significant (p = 0.07). Distance from home to the MIA was not a significant predictor (p = 0.06) and SES status was no longer a significant predictor of clinical trial participation (p = 0.78).
Table 3. . Multivariate logistic regression for factors predicting clinical trial participation in patients with invasive melanoma (n = 9,074) .
| Factor | Odds ratio | 95% CI | p-value | |
|---|---|---|---|---|
| Lower | Upper | |||
| Age at primary diagnosis (per year increase) | 0.99 | 0.98 | 0.99 | <0.01 |
| Sex (males vs females) | 1.18 | 1.07 | 1.30 | <0.01 |
| Socioeconomic status (per decile increase) | 1.00 | 0.98 | 1.02 | 0.78 |
| ARIA (per quintile increase) | 1.10 | 0.99 | 1.22 | 0.07 |
| Distance (per km increase) | 1.00 | 1.00 | 1.00 | 0.06 |
| AJCC stage (referent group: stage I): overall | – | – | – | <0.01 |
| AJCC stage II (referent group: stage I) | 2.81 | 2.50 | 3.16 | – |
| AJCC stage III (referent group: stage I) | 4.55 | 3.91 | 5.30 | – |
| AJCC stage IV (referent group: stage I) | 1.39 | 0.88 | 2.18 | – |
| AJCC stage not classified | 1.10 | 0.81 | 1.51 | – |
| Constant | 0.40 | – | – | <0.01 |
Goodness of fit χ2 = 82.64.
AJCC: American Joint Cancer Committee; ARIA: Accessibility Remoteness Index of Australia.
Discussion
This is the largest study regarding melanoma clinical trial participation reported to date, as far as we are aware. Our data suggest that a quarter of all patients attending the MIA participate in clinical trials. We found that AJCC stage of disease at initial presentation was the strongest predictor of melanoma clinical trial participation and neither SES, remoteness nor proximity to the MIA were significant predictors when adjusted for relevant covariates. Interestingly, men were more likely to participate than women, and older people were slightly less likely to participate than younger people.
Our high rate of trial participation is very different from the findings of other cancer centers in the USA, where one in 20 patients are typically enrolled in trials [23]. In the UK, the National Cancer Research Institute has reported that, over the last decade, participation in clinical studies has increased from one in 26 to one in six diagnosed patients [23]. In Australia, the national cancer agency (Cancer Australia) has provided substantial funding in order to increase cancer trial participation rates, with a specific focus on culturally and linguistically diverse groups and those from rural or remote areas [24]. The high rate of recruitment at the MIA may be due to a relatively healthy patient population, who are predominantly English speakers, which makes discussions of trials and the process of informed consent relatively straightforward. It may also be due to a ‘trials culture’ within the MIA, along with the necessary personnel and expertise to accommodate clinical research. It was no surprise that AJCC stage of disease was a significant predictor of trial participation, as the majority of adjuvant intervention trials target patients who are at high risk of recurrence. However, the MIA does offer many other noninterventional trials that investigate, for example, histopathological tumor response and psychosocial outcomes, and these studies are not restricted to a particular AJCC stage of disease. Overall, our findings are similar to those reported in the COMS ocular melanoma study [18], in that low SES was not a barrier to clinical trial participation.
• Limitations
Our data set was restricted to routinely collected data from clinical practice and, consequently, we did not have complete information on individual-level measures of SES, such as the patient's highest education level achieved, patient income level or occupational group. Instead, we relied on a government index of area-level SES, which classifies small areas based on a comprehensive number of household socioeconomic indicators, such as number of bedrooms, equivalized household income, proportion of people in professional or managerial occupations, proportion of adults who have attended university, marital status, car ownership and broadband internet access. One potential issue with the use of this variable is the misclassification of individuals. For example, people of relative social advantage who choose to reside in an area of disadvantage may be classified as being of low SES. Our strategy to minimize misclassification was to use the smallest area for SES – the census collection district with approximately 200 households – rather than the larger area of postal code, which can include up to 4000 households. As an aside, the census collection district boundaries in Australia changed between 2001 and 2006 [20], resulting in a few residential addresses changing districts and therefore SES levels, but these few cases did not have a significant effect on the results.
Our study included patients treated at a single center in Australia (the MIA), which has a substantial clinical trials infrastructure and acts as a tertiary referral center. Some patients in this cohort may have been specifically referred to the MIA for consideration of a clinical trial. It is therefore likely that the high trial participation rate and the distance travelled by patients to attend the center may only be generalizable to other specialist melanoma treatment centers with a similar infrastructure and referral process. However, the factors affecting trial participation (severity of disease, age and sex) are likely to be similar in centers elsewhere. A further limitation was the unknown follow-up status of 10% of our cohort that could be improved with linkage to the National Death Index or state cancer registry.
• Interpretation
Our findings contain important messages for melanoma clinicians and research staff. First, when screening potential clinical trial candidates and discussing trial eligibility, it is important to consider people of all socioeconomic backgrounds, as well as those who live some distance from the trials center, as they may see great value in participation and be willing to be enrolled in an experimental protocol, even if it requires frequent visits to the center. Second, we must consider that upper age limits may exclude older people who may otherwise have adequate performance status and are willing to be involved [25]. As the median age of patients with melanoma is steadily increasing, it is becoming more important to ensure that new treatments are fully evaluated in the older population. Third, we must ensure that clinical trials for all stages of melanoma, including stage IV disease, are open and available for patients. This may require the referral of patients to colleagues at other melanoma treatment centers, as it may not be efficient to have all trials open at all sites. In Australia, web-based initiatives hosted by the Australia and New Zealand Clinical Trials Registry, Cancer Australia [26] and the Cancer Council NSW provide a useful list of trials by center, including those that are open to recruitment, and these initiatives are accessible to the clinician or the lay person.
• Implications for policy
Participation in clinical trials has been reported to improve patient outcomes in chronic diseases other than cancer, regardless of whether the patient receives a new drug or treatment [27]. For cancer patients, the evidence is less clear [28]; however, all patients should be informed about trials for which they may be eligible and given the opportunity to participate in them, as there is little doubt that trials improve the treatment of future patients. The identification of ‘low-participation’ groups can help tailor recruitment strategies in order to ensure the representative participation of those who are affected by melanoma. We encourage the reporting of the SES of trial participants in order to enable judgments to be made regarding the generalizability of the trial findings.
Further research is needed in order to investigate why women were significantly less likely to participate in trials than men, even when adjusted for disease severity. One explanation may be the exclusion criteria for women who are pregnant or planning to become pregnant. Other explanations may include financial barriers or perceived out-of-pocket costs associated with participation or, alternatively, insufficient time to participate as a consequence of family care-giving responsibilities. Future studies should assess clinical trial participation for all melanoma patients on a population (i.e., statewide or national) basis.
Conclusion
Our data show that a very large proportion of patients attending the MIA participate in clinical trials. We found that AJCC stage of disease at initial presentation was the strongest predictor of melanoma clinical trial participation, and neither SES, remoteness nor proximity to the MIA were significant factors when adjusted for relevant covariates. Reporting of the SES of trial participants enables judgments to be made regarding the generalizability of trial findings.
Future perspective
In order to ensure the generalizability of the results from melanoma clinical trials, there will be greater recognition of the importance of recruiting a wide range of subjects with differing sociodemographic and clinical characteristics. If this is not adequately achieved, then extensive modeling based on data from unselected cohorts will be required. More trials will be offered to older people and those with stage IV melanoma than is currently the case.
Footnotes
Financial & competing interests disclosure
JL Lee is supported with a University of Sydney, Summer Research Scholarship 2012. RL Morton is supported by an Australian National Health and Medical Research Council Early Career Fellowship #1054216. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
References
- 1.Australian Institute of Health and Welfare (AIHW) Australian Cancer Incidence and Mortality (ACIM). Melanoma of the Skin for Australia. Australian Institute of Health and Welfare of the Australian Government; Australia: 2012. [Google Scholar]
- 2.Forsea AM, Del Marmol V, de Vries E, Bailey EE, Geller AC. Melanoma incidence and mortality in Europe: new estimates, persistent disparities. Br. J. Dermatol. 2012;167(5):1124–1130. doi: 10.1111/j.1365-2133.2012.11125.x. [DOI] [PubMed] [Google Scholar]
- 3.Geller AC, Miller DR, Lew RA, et al. Cutaneous melanoma mortality among the socioeconomically disadvantaged in Massachusetts. Am. J. Public Health. 1996;86(4):538–543. doi: 10.2105/ajph.86.4.538. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Morton DL, Thompson JF, Cochran AJ, et al. Sentinel-node biopsy or nodal observation in melanoma. N. Engl. J. Med. 2006;355:1307–1317. doi: 10.1056/NEJMoa060992. [DOI] [PubMed] [Google Scholar]
- 5.Burmeister BH, Henderson MA, Ainslie J, et al. Adjuvant radiotherapy versus observation alone for patients at risk of lymph-node field relapse after therapeutic lymphadenectomy for melanoma: a randomised trial. Lancet Oncol. 2012;13(6):589–597. doi: 10.1016/S1470-2045(12)70138-9. [DOI] [PubMed] [Google Scholar]
- 6.Flaherty KT, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 2010;363(9):809–819. doi: 10.1056/NEJMoa1002011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ascierto PA, Kirkwood JM. Adjuvant therapy of melanoma with interferon: lessons of the past decade. J. Transl. Med. 2008;6:62. doi: 10.1186/1479-5876-6-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Elting LS, Cooksley C, Bekele BN, et al. Generalizability of cancer clinical trial results: prognostic differences between participants and nonparticipants. Cancer. 2006;106:2452–2458. doi: 10.1002/cncr.21907. [DOI] [PubMed] [Google Scholar]
- 9.Ford JG, Howerton MW, Lai GY, et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer. 2008;112(2):228–242. doi: 10.1002/cncr.23157. [DOI] [PubMed] [Google Scholar]
- 10.Wallingford SC, Alston RD, Birch JM, Green AC. Regional melanoma incidence in England, 1996–2006: reversal of north–south latitude trends among the young female population. Br. J. Dermatol. 2013;169(4):880–888. doi: 10.1111/bjd.12460. [DOI] [PubMed] [Google Scholar]
- 11.Cancer Institute NSW. Melanoma Red Zones 2004–2008. New South Wales Central Cancer Registry. www.cancerinstitute.org.au/media/148332/melanoma-red-zones-nsw.pdf
- 12.Gross CP, Filardo G, Mayne ST, Krumholz HM. The impact of socioeconomic status and race on trial participation for older women with breast cancer. Cancer. 2005;103(3):483–491. doi: 10.1002/cncr.20792. [DOI] [PubMed] [Google Scholar]
- 13.Grann VR, Jacobson JS, Troxel AB, et al. Barriers to minority participation in breast carcinoma prevention trials. Cancer. 2005;104(2):374–379. doi: 10.1002/cncr.21164. [DOI] [PubMed] [Google Scholar]
- 14.Mohd Noor A, Sarker D, Vizor S, et al. Effect of patient socioeconomic status on access to early-phase cancer trials. J. Clin. Oncol. 2013;31(2):224–230. doi: 10.1200/JCO.2012.45.0999. [DOI] [PubMed] [Google Scholar]
- 15.Melisko ME. Patient and physician attitudes toward breast cancer clinical trials: developing interventions based on understanding barriers. Clin. Breast Cancer. 2005;6(1):45–54. doi: 10.3816/CBC.2005.n.008. [DOI] [PubMed] [Google Scholar]
- 16.Byrne MM, Tannenbaum SL, Glück S, Hurley J, Antoni M. Participation in cancer clinical trials: why are patients not participating? Med. Decis. Making. 2014;34(1):116–126. doi: 10.1177/0272989X13497264. [DOI] [PubMed] [Google Scholar]
- 17.Lara PN. Evaluation of factors affecting awareness of and willingness to participate in cancer clinical trials. J. Clin. Oncol. 2005;23(36):9282–9289. doi: 10.1200/JCO.2005.02.6245. [DOI] [PubMed] [Google Scholar]
- 18.Diener-West M. The COMS randomized trial of iodine 125 brachytherapy for choroidal melanoma, II: characteristics of patients enrolled and not enrolled. COMS Report No. 17. Arch. Ophthalmol. 2001;119(7):951–965. doi: 10.1001/archopht.119.7.951. [DOI] [PubMed] [Google Scholar]
- 19.von Elm E, Douglas GA, Matthias E, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806–808. doi: 10.1136/bmj.39335.541782.AD. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Pink B. Socio-Economic Index for Areas (SEIFA) 2011. Technical Paper. Australian Bureau of Statistics Commonwealth of Australia. 2013 www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/22CEDA8038AF7A0DCA257B3B00116E34/$File/2033.0.55.001%20seifa%202011%20technical%20paper.pdf [Google Scholar]
- 21.Visualizer GPS. Quick Geocoder. www.gpsvisualizer.com/geocode
- 22.Esri ArcGIS platform. www.esri.com/software/arcgis
- 23.National Cancer Research Institute. Number of cancer patients taking part in clinical studies quadruples in a decade. www.ncri.org.uk/wp-content/uploads/2013/09/NCRI-Press-Release-2010-7Nov.pdf
- 24.Australian Government – Cancer Australia. Supporting Cancer Clinical Trials in Australia: Principles Document. www.canceraustralia.gov.au/sites/default/files/publications/principles_policy_504af04b39bfa.pdf
- 25.Lewis JH. Participation of patients 65 years of age or older in cancer clinical trials. J. Clin. Oncol. 2003;21(7):1383–1389. doi: 10.1200/JCO.2003.08.010. [DOI] [PubMed] [Google Scholar]
- 26.Australian Government – Cancer Australia Australian Cancer Trials. www.australiancancertrials.gov.au
- 27.Baker JR, Vandal AC, Yeoh J, Zeng I, Wong S, Ryan SN. Clinical trial participation improves outcome: a matched historical cohort study. Clin. Trials. 2013;10(5):735–743. doi: 10.1177/1740774513496915. [DOI] [PubMed] [Google Scholar]
- 28.Peppercorn JM, Weeks JC, Cook EF, Cook EF, Joffe S. Comparison of outcomes in cancer patients treated within and outside clinical trials: conceptual framework and structured review. Lancet. 2004;363(9405):263–270. doi: 10.1016/S0140-6736(03)15383-4. [DOI] [PubMed] [Google Scholar]