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Journal of Clinical Oncology, Vol 23, No 24 (August 20), 2005: pp. 5424-5426 © 2005 American Society of Clinical Oncology. DOI: 10.1200/JCO.2005.05.906
Quality-of-Life Assessment in Pediatric Brain Tumor Patients and Survivors: Lessons Learned and Challenges to FaceDepartment of Radiation Oncology, St John's Health Center, Santa Monica; Valley Radiation Associates, El Segundo; and University of California, Los Angeles, Department of Radiation Oncology, Los Angeles, CA
Institute for Clinical Research and Health Policy Studies, Tufts—New England Medical Center and Tufts School of Medicine, Boston, MA Much has been written about the health-related quality of life (HRQOL) of adults with cancer based on a number of generic and cancer-specific instruments that have been applied with increasing sophistication in clinical studies and practice. Unfortunately, such work in pediatric cancer has lagged far behind. There are good reasons why the volume and quality of reporting of HRQOL outcomes in pediatrics has been slower to progress. Aside from the difficult conceptual and methodologic issues of determining the capacity of children to provide valid and reliable self-report based on age and threats to cognitive functioning, researchers in this area face additional struggles with regards to data collection, given small sample sizes and heterogeneity relative to tumor types, location, and treatment alternatives. In this issue, Bhat et al1 make an important foray into this area of research by providing us with a report on the use of the PedsQL 4.0 generic scale and cancer module in the pediatric brain tumor population at their center. Their experience raises for discussion many of the difficulties and challenges faced in this field. Although the focus of the article and this editorial is on the evaluation of HRQOL in the pediatric brain tumor population, the principles hold true for other pediatric populations. Researchers and clinicians alike must recognize that this is a time-consuming and iterative process. Articles such as that by Bhat et al represent important first steps but not the final answer. When selecting an HRQOL instrument for a study, a number of factors are considered, including relevancy to the target population, study outcome(s) of interest (eg, overall functioning, symptom relief, cognitive functioning, and so on), and practicality of instrument (eg, length of instrument; availability of language and age-appropriate versions; ease of administration, particularly in a busy, clinical setting). In addition, the instrument ideally should possess some demonstration of validity and reliability in the target population. The latter is often overlooked or insufficiently addressed. The use of the PedsQL in the pediatric brain tumor population has been limited, with previous studies including only a handful of children and adolescents on or off treatment for a brain tumor. This is despite years of psychometric testing in healthy children, children with various chronic medical conditions, and pediatric cancer survivors (which was one of the original populations in which earlier versions of this instrument was tested). Specifically, the original validation article on an earlier version of the PedsQL (PCQL-32) included nine pediatric brain tumor patients off treatment, and the most recent validation report of the current PedsQL generic core scales, fatigue scale, and cancer module included only 24 brain tumor patients on or off treatment.2,3 Another study using this instrument in pediatric cancer survivors reports an additional 23 pediatric brain tumor patients off treatment.4 Therefore, we applaud the efforts of Bhat et al in recruiting their study sample of 134 children and their parents. This is an important first step in the process of robustly characterizing HRQOL in this complex population. Specific demonstration of reliability and validity in this target population is particularly germane given the higher risk of cognitive impairment and developmental delay in this population, making self-report potentially more challenging. It is important to note that Bhat et al examined reliability and validity in their study population by reporting the internal consistency reliability of the PedsQL scales in different age groups and by cognitive ability and described basic validity checks, including the correlation of scores on PedsQL cancer-module symptom-specific items, with report of symptoms by parents in a separate study questionnaire, as well as difference in scores between groups expected to be different by treatment and tumor pathology. Their results of the relatively low reliability of both the PedsQL generic scale and cancer module for younger children (5 to 7 years old) indicate that this instrument is still exploratory for this age group. Although parent-proxy reports demonstrate higher reliability, the complexity of proxy reporting has been well described elsewhere. In particular, proxy and self-report are not interchangeable.5,6 The authors' main finding is that healthy controls have higher functioning compared with children with brain tumors on and off treatment, as demonstrated by the PedsQL generic scale total score and individual domain scores for both parent proxy and child report. However, there is no report of whether the PedsQL total score or individual domain scores distinguish within the brain tumor study population by treatment status (on and off treatment). Furthermore, the finding that the PedsQL cancer module was unable to distinguish between those patients on and off cancer treatment in the Bhat et al study is both surprising and at odds with what Varni et al3 demonstrated in a large mixed sample of pediatric cancer study populations. Although this may be caused in part by small sample size, it certainly raises concern about the use of the PedsQL cancer module in distinguishing between treatment arms or study groups in clinical trials. Such an instrument should be able to differentiate groups based on adverse effects or toxicity. The presumed added value of a disease- or condition-specific measure is that a more honed and detailed measure would distinguish important clinical differences between groups or over time that may not be discerned with a generic measure either by its domain scores or a summary total score. This has been demonstrated repeatedly in the adult cancer literature.7,8 In our ongoing work with the newly developed Quality of Life Assessment instrument designed for the pediatric brain tumor population, we have also demonstrated how a condition-specific measure differentiates by treatment status, treatment intensity, and in comparison with other populations (ie, children with acute lymphoblastic leukemia).9 Data collection in the pediatric brain tumor population, given the expected prevalence of cognitive impairment, requires trained personnel, careful attention to administration of a questionnaire in a consistent and unbiased manner, and a relatively quiet setting to minimize distractions. In reality, the worse-functioning patients are typically screened out of HRQOL studies because of their inability to get through study tasks. In our work with this population, we have attempted to formalize the screening process by testing for minimal reading and comprehension ability to complete a pen-and-paper base instrument. In addition, we conducted formal structured interviews with a small number of pediatric brain tumor patients and not only found variation in how patients interpreted questions but also noted the susceptibility of a subject to an interviewer's influence, further emphasizing the importance of the interviewer's skill in eliciting an unbiased response.10 Although collection of HRQOL data can and needs to be done in the context of a regular clinic, if one is to obtain high-quality data in terms of both completeness and validity, the importance of careful training of personnel and dedication of resources to study monitoring should not be overlooked. Bhat et al note the use of research associates, presumably devoted to the task of identifying and screening patients and administering the PedsQL in the context of an outpatient clinic, although the specifics of their training and study monitoring were not discussed. Mode of HRQOL instrument administration (such as self-administration or research personnel assisted in the clinic or mailed to home with instructions for completion or telephone administration) is another factor that can introduce variability and bias in results. Use of more than one mode of administration within a study is often used for practical reasons and may be less problematic in adults who may be better able to think and respond independently without the influence of another. Mailed questionnaires to home with the intent of a child independently completing the questionnaire is always fraught with the uncertainty that this in fact is being done. Telephone administration allows more control over the delivery of the questions but requires more research resources and may be more challenging for the child rater. Use of multiple language versions of a questionnaire also requires more skilled research personnel in that the administration process (ie, explanation and guidance) needs to be provided in more than one language. Proxy reporting certainly has value in reporting of HRQOL, particularly if attempting to access a sickly, cognitively impaired, or young population; however, it is important to understand its true value and its meaning. Many studies have shown modest correlations (Pearson's correlation, 0.3 to 0.5) between parent (often maternal) proxy and child self-report. For certain domains (eg, physical functioning), results tend to be more highly correlated between the two reporting sources, although for other domains such as emotional and self-perception, results can differ more substantially. Furthermore, moderate correlations that are statistically significant may not necessarily demonstrate similar conceptualization of HRQOL. In fact, some may argue that given the developmental discord between parents and children, one would not necessarily expect them to agree.11 Study of the pediatric brain tumor population is also fraught with the difficulty of small sample size and inherent heterogeneity of the population given variety of histopathologies, tumor locations, treatment types and combinations, effect of age of diagnosis and treatment, sex, and so on. As noted, Bhat et al put forth a significant recruitment effort; they have reported the largest series on HRQOL outcomes in a pediatric brain tumor population. However, substantially greater numbers would be needed to properly address the impact of the numerous potential confounders within this population. Ongoing evaluation of the measurement properties of the PedsQL or any other HRQOL instrument in this population, particularly the ability to detect change over time, is also desirable, because these instruments are incorporated into clinical trials and, ultimately, into clinical practice. One should accept with caution the preliminary results of this report, recognizing that work is needed to more completely understand HRQOL in this population. The results to date will hopefully spur additional hypothesis-driven studies to better understand the effects of various sociodemographic, disease, and treatment factors on the HRQOL of these patients and how clinically distinct groups within this population differ. We support the recent move within cooperative groups and other research consortia to identify and incorporate a consistent core battery of HRQOL instruments suited for this population. This will yield a rich and meaningful database and surmount the limitations of small patient numbers faced by any individual investigator or clinical study. This united effort will also inform us about necessary potential modifications to existing instruments and the need to supplement the instrument battery based on emerging study questions or designs. Authors' Disclosures of Potential Conflicts of Interest Although all authors have completed the disclosure declaration, the following author or their immediate family members has indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.
Dollar Amount Codes (A) < $10,000 (B) $10,000–99,999 (C)
REFERENCES
1. Bhat SR, Goodwin TL, Burwinkle TM, et al: Profile of daily life in children with brain tumors: An assessment of health-related quality of life. J Clin Oncol 23:5493-5500, 2005 2. Varni JW, Katz ER, Seid M, et al: The pediatric cancer quality of life inventory-32 (PCQL-32): I. Reliability and validity. Cancer 82:1184-1196, 1998[CrossRef][Medline] 3. Varni JW, Burwinkle TM, Katz ER, et al: The PedsQL in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory generic core scales, multidimensional fatigue scale, and cancer module. Cancer 94:2090-2106, 2002[CrossRef][Medline] 4. Eiser C, Vance YH, Horne B, et al: The value of the PEdsQLTM in assessing quality of life in survivors of childhood cancer. Child Care Health Dev 29:95-102, 2003[CrossRef][Medline] 5. Grootendorst PV, Feeny DH, Furlong W: Does it matter whom and how you ask? Inter- and intra-rater agreement in the Ontario Health Survey. J Clin Epidemiol 50:127-135, 1997[CrossRef][Medline] 6. Parsons SJ, Mayer DK: Health-related quality of life in hematologic disease. Hematol Oncol Clin North Am 18:1235-1248, 2004[CrossRef][Medline] 7. Litwin MS, Hays RD, Fink A, et al: Quality-of-life outcomes in men treated for localized prostate cancer. JAMA 273:129-135, 1995[Abstract] 8. Ganz PA, Rowland JH, Desmond K, et al: Life after breast cancer: Understanding women's health-related quality of life and sexual functioning. J Clin Oncol 16:501-514, 1998[Abstract] 9. Tao ML, Masterman-Smith M, Garvie PA, et al: Quality of life assessment (QUOLA) in pediatric brain tumor population: Feasibility and measurement properties of a new brain-specific instrument. Presentation at the 11th International Symposium on Pediatric Neuro-oncology, Boston. Neuro-oncology 6:448, 2004 10. Garvie PA, O'Leary TE, Tao ML, et al. The role of cognitive interviewing in pediatric questionnaire development: Are we asking what we think we are asking? Presented at the 23rd Annual Meeting for the Society of Behavioral Medicine, April 2002, Washington, DC 11. Landgraf JM. Measuring health-related quality of life in pediatric oncology patients: A brief commentary on the state of the art of measurement and application. Int J Cancer Suppl 12:147-150, 1999[CrossRef][Medline] Related Article
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Copyright © 2005 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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