Skip Navigation

JNCI Monographs 2004 2004(33):102-125; doi:10.1093/jncimonographs/lgh002
© 2004 by Oxford University Press
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by Pickard, A. S.
Right arrow Articles by Feeny, D. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Pickard, A. S.
Right arrow Articles by Feeny, D. H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

2004 © Oxford University Press

Article

A Structured Review of Studies on Health-Related Quality of Life and Economic Evaluation in Pediatric Acute Lymphoblastic Leukemia

A. Simon Pickard, Leigh-Ann Topfer, David H. Feeny

A. S. Pickard, Department of Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago; L.-A. Topfer, Institute of Health Economics, University of Alberta; D. H. Feeney, Institute of Health Economics, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, and Health Utilities Inc., Dundas, ON, Canada

Correspondence to: David H. Feeny, Ph.D., Institute of Health Economics, #1200, 10405 Jasper Ave., Edmonton, AB, Canada T5J 3N4 (e-mail: dfeeny{at}pharmacy.ualberta.ca).


   ABSTRACT
Top
 Notes
 Abstract
Background
 Methods
 Results
 Discussion
 Appendix 1. Data Abstraction...
 References
 
Background: A comprehensive review was made of the literature on the health-related quality of life (HRQL) and economic outcomes of children with acute lymphoblastic leukemia (ALL), the most common of all cancers in childhood. Objectives: The primary objectives of the review were to locate and describe measures of HRQL used in pediatrics and in pediatric oncology that might be applicable to ALL, to summarize studies that have applied HRQL measures to ALL, to identify and summarize economic evaluations of the costs and consequences of care for pediatric ALL, and to identify areas requiring further research. Data sources: To identify the HRQL literature in pediatric ALL, searches were run on the major biomedical and social science bibliographic databases. Search terms included a variety of MeSH and other thesaurus terms, text words, names of HRQL instruments, and the names of key authors in the field. The economic literature searches included most of the same databases, with the addition of the National Health Service Economic Evaluation Database and EconLit. Searches on specific authors and instruments and hand searches were also conducted. Study selection: Only English language studies published from 1975 through 2000 were included. Data extraction: Standardized data extraction forms were used to abstract information from HRQL and economic evaluation studies. Two reviewers independently screened the search results, and differences were resolved by consensus. Data synthesis: A number of generic HRQL measures, both adult and pediatric, have been applied in pediatric ALL. In addition, a number of pediatric oncology-specific instruments and pediatric oncology disease-specific instruments have been developed. Most of these instruments have been used to measure the health status of patients undergoing therapy. Despite the limited numbers of patients and resources available to assess HRQL measures in children with cancer, a fairly substantial body of literature has been published. Economic studies of pediatric ALL have only recently been undertaken. Most studies focus on a particular, narrow aspect of costs associated with the disease. There are relatively few cost-effectiveness studies that compare the costs and consequences of two or more treatment options. There are no published, comprehensive economic evaluations of pediatric ALL. Conclusions: HRQL measures provide not only important information on the improvements offered by new therapies but also an outcome measure for economic evaluations. Recently developed HRQL measures and applications that include the direct assessments of children are important contributions. By the age of 7 or 8 years, children can generally provide reliable responses. Furthermore, children often provide information that is not available from parental reports (e.g., in the more subjective areas of pain and emotion). However, the use of multiple viewpoints, such as the patient, parent, and health professional perspectives, can provide valid and important complementary information. Expertise in HRQL measurement should be included in the design of most future trials. Funds for HRQL research should be made available to enhance the scope of HRQL activities by organizations such as the Children's Oncology Group. In the near future, further work to generate evidence of validity for available HRQL measures for use in children with ALL will be a high priority. Continuation of inquiries into the methods for HRQL assessment of younger children (i.e., preschoolers) is also a priority.


   BACKGROUND
Top
 Notes
 Abstract
 Background
Methods
 Results
 Discussion
 Appendix 1. Data Abstraction...
 References
 
Acute lymphoblastic leukemia is the most common of all childhood cancers. Survival rates have improved dramatically over the latter course of the 20th century for most forms of childhood cancers, including ALL. The overall survival rate for children with acute lymphocytic leukemia (ALL accounts for the vast majority of cases in that category) in the United States has improved from 53.8% in 1974-1976 to 86.8% for the period 1992-1997 (3).

The tremendous progress in cure rates is attributable, in part, to the cooperative trial program among centers of excellence in pediatric cancer (4). Children who receive treatment according to well-defined protocols in specialized children's centers have improved survival when compared with that of children who received treatment outside these centers for ALL, lymphoma, Wilms tumor, medulloblastoma, rhabdomyosarcoma, and Ewing's sarcoma (4). Now that the majority of pediatric patients survive their cancer, quality-of-life measurement is increasingly recognized as an important method of evaluating the impact of treatment interventions and understanding the short- and long-term morbidity (5-15). The two key issues are the burden of morbidity during the treatment process and the long-term effects of the disease and its treatment on the health status and health-related quality of life (HRQL) of childhood cancer survivors (16). Given the current focus of many pediatric oncology trials on toxicity and sequelae-sparing therapies, the assessment of HRQL is becoming increasingly important.

A substantial literature has accumulated on the measurement of HRQL in cancer; however, until recently, relatively little of this research focused on pediatric oncology. Health status and HRQL instruments have been developed specifically for use in cancer patients, such as the Functional Living Index—Cancer (FLIC) (17) and the Functional Assessment of Cancer Therapy (FACT) (18), but these instruments do not consider HRQL issues in the context of pediatric oncology.

As for any HRQL measure, an instrument for use in pediatric cancer should possess evidence of content/construct validity and reliability in the patient population of interest. One of the challenges in the development of childhood cancer-specific HRQL measures is to identify the primary HRQL domains of importance. The domains of importance may differ according to the type of cancer and the treatment regimens used. Although many survivors of cancer in childhood enjoy normal health, chronically poor health and specific deficits in cognitive and emotional status have been identified in some children who have had cancer (14). Other aspects of HRQL are cancer-specific concerns such as alopecia, mucositis, and gonadal damage leading to infertility. In some cases, patients experience acute reversible toxic effects; in other cases, the toxic effects persist or are irreversible. The assessment of HRQL provides a mechanism to measure and analyze these complex issues.

Further to the challenge of developing valid and reliable HRQL measures for use in pediatric cancer is the fact that the cognitive capacity of children for self-evaluation changes with the normal process of maturation (16,19). Thus, measures that deal with the problems specific to HRQL measurement in children by age group are needed. Longitudinal assessments of HRQL are complicated by the developmental stages that pediatric subjects undergo during treatment and follow-up (8). Proxy respondents are frequently used, and an understanding of the relationship between parent and child/adolescent assessment of HRQL is important.

One of the first scales developed specifically for use in children with cancer was the Play Performance Scale for Children (PPSC) (20), designed as a measure of a child's ability to perform day-to-day activities as an indication of health status (20). Since the emergence of the PPSC, a number of generic and cancer-specific HRQL measures have been developed. Trudel et al. (21) identified the following quality-of-life measures available for pediatric cancer patients: the PPSC (20), the Quality of WellBeing Scale (QWB) (22), the Perceived Illness Experience (PIE) Scale (23), the Pediatric Oncology Quality of Life Scale (POQOLS) (24), and the Health Utilities Index (HUI) Mark 2 system (25).

This review includes the following three types of published studies (available up to April 2001): 1) HRQL measures that potentially could be used in pediatric cancer, 2) studies that have applied HRQL measures in populations that include children with ALL, and 3) economic studies involving treatments for children with ALL. The first objective of this review was to identify and describe generic, specific, and disease-specific HRQL measures developed or applied in childhood ALL. The second objective was to identify and describe economic evaluation studies dealing with pediatric ALL.


   METHODS
Top
 Notes
 Abstract
 Background
 Methods
Results
 Discussion
 Appendix 1. Data Abstraction...
 References
 
Literature Search

HRQL and health status in pediatric ALL. Literature searches were run on several databases by using various search strategies as shown below. More than 1800 references were identified. A review of the titles and abstracts revealed that most studies were not specific to HRQL in children with leukemia. For example, many studies focused on the quality of life of the parents and on the neurological/cognitive impacts of cancer therapies on adult survivors of leukemia in childhood. The search was restricted to studies published since 1975. Only studies published in English were selected for review. An appendix providing details on the search strategies, keywords, and databases consulted is available from the authors. Search strategies will be described briefly here. Standard databases including PubMed (MEDLINE), EMBASE, Web of Science, and CancerLit were consulted. Given previous knowledge of important studies and measures, specific searches for papers by the authors or for particular HRQL measures were also conducted. Key journal supplements and relevant sources from the personal collection of one of the authors (D. H. Feeny) were also used.

Economic evaluations and costing studies. For the economic component of the review, literature searches were run on biomedical databases used in the HRQL searches and also on economic literature databases, such as EconLit and the National Health Service Economic Evaluation Database. (More details on the search strategies are recorded in an appendix, available upon request.) More than 250 references were identified. However, upon examination of the abstracts, most were not specifically relevant to the costs of ALL in children. This review was also restricted to English language studies published since 1975.

Data Extraction and Presentation

Citations identified by the literature search were independently screened by two reviewers (A. S. Pickard and L.-A. Topfer). Papers that appeared to be relevant were retrieved and were included or rejected on the basis of a review of the entire paper. Disagreements were resolved by consensus. Information was abstracted from each study by using standardized data collection forms (see "Appendix 1"). Three data collection forms were developed, and the information from these forms is summarized in Tables 1, 2, 3.


View this table:
[in this window]
[in a new window]
  		Table 1. Pediatric oncology health-related quality-of-life (HRQL) measures
 

View this table:
[in this window]
[in a new window]
  		Table 2. Applications of health-related quality-of-life (HRQL) measures to acute lymphoblastic leukemia (ALL)*
 

View this table:
[in this window]
[in a new window]
  		Table 3. Costing studies and economic evaluations in pediatric acute lymphoblastic leukemia (ALL)*
 

Form I was structured to describe HRQL instruments that have been used or may find application in pediatric ALL in North America in terms of the following: domains (dimensions of health status) included on each measure, intended respondent and age group, number of items, application format, country of origin, and availability of translations in other languages.

Form II was used to extract evidence from studies that have applied HRQL instruments to pediatric ALL. This form included aspects such as the following: sample characteristics, data collection, respondents, study design, phase of care, medication regimen, and additional evidence on the measurement properties of each HRQL instrument generated by the study. This information is summarized in Table 2, which reports the actual activity or applications of the HRQL measures identified as having potential for application in Table 1.

A taxonomy of HRQL measures and explanations of psychometric properties and issues related to HRQL measurement are available elsewhere (16,26,27). Evidence on reliability and validity reported in Table 2 pertains only to studies involving one or more children with ALL. The characteristics in column 13 of Table 2 (i.e., confidence intervals and power calculations) were added in a recent update for this article and were not available (had not been abstracted) for studies identified in an earlier phase of this project. Only results related to the HRQL measure of interest are reported in Table 2. A number of studies aggregated ALL patients with other types of cancer patients and did not report disaggregated results based on type of cancer. The same patient cohort may have been studied in more than one paper. Papers that explored single domains of HRQL, such as anxiety, were excluded. Studies investigating the late effects of childhood cancer on the HRQL of adults were also excluded.

Form III was used for the economic evaluation and costing studies. The results from economic evaluations and costing studies on patient samples or subsamples that include children with ALL (Table 3) are reported on the basis of criteria that constitute guidelines for the performance of economic evaluations in health care (28,29). Our tables are intended to convey a brief summary of the contents of each study or instrument and are not a substitute for reading the papers.


   RESULTS
Top
 Notes
 Abstract
 Background
 Methods
 Results
Discussion
 Appendix 1. Data Abstraction...
 References
 
The HRQL measures were subdivided into health profiles, preference-based measures, and childhood cancer-specific pediatric HRQL instruments (Table 1). A number of pediatric oncology-specific instruments have been developed, such as the Miami Pediatric Quality of Life Questionnaire, the Pediatric Cancer Quality of Life Inventory (PCQL-32), and the POQOLS. Finally, several pediatric oncology disease-specific instruments have been developed. For instance, the Disease Specific Impairment Inventories—Bone Marrow Transplantation (DSII-BMT) focuses on HRQL in patients undergoing bone marrow transplantation. These instruments have been used in studies of several childhood cancers, including ALL. No HRQL measure specific to ALL was identified.

A number of generic HRQL measures (e.g., the QWB and the HUI Mark 2 [HUI2] and Mark 3 [HUI3] systems) and generic pediatric HRQL measures (e.g., the Child Health Questionnaire [CHQ]) have been applied in pediatric ALL (Table 2). These instruments have been used both to assess health status and HRQL during treatment and to assess outcomes.

Most of the literature on the cancer-specific pediatric measures focused on evidence of validity and reliability. Studies that describe a specific treatment regimen were more likely to have a primary objective of evaluation of therapy, with information on the psychometric properties more likely to have been a secondary objective.

Sawyer et al. (30) provided evidence on interrater agreement (generally moderate to high but seldom perfect) for the CHQ (Table 2). This study also provides similar evidence for functional status measures. Comparable results for interrater reliability are reported by Parsons et al. (31) for the Child Health Rating Inventories (Table 2). Evidence on interrater reliability is also reported by Barr et al. (32). Test-retest results for HUI2 are reported by Trudel et al. (21).

Internal consistency results for the QWB are reported by Bradlyn et al. (22). Phipps et al. (33) reported estimates of internal consistency for the Behavioral, Affective, and Somatic Experiences Scale (BASES) (Table 2). Eiser et al. (23) reported results on internal consistency and test-retest reliability (Table 2). Varni et al. (34,35) reported on internal consistency and interrater reliability for the PCQL-32 (Table 2). Goodwin et al. (24) reported high levels of internal consistency and interrater reliability for POQOLS (Table 2). In general, internal consistency and test-retest reliability have been acceptable for most instruments.

There are a number of areas of application for cancer outcome measures. These include the following categories: 1) studies to provide evidence on the efficacy or effectiveness of an intervention, 2) studies to provide evidence on the cost-effectiveness of an intervention, 3) studies to describe the patterns of care, 4) studies to monitor or measure quality of care, and 5) cost of illness studies. With respect to the HRQL literature on pediatric ALL summarized in Table 2, approximately 9.4% of the studies fell into category 1 (establish effectiveness), 46.9% fell into category 3 (patterns of care), and 43.8% fell into category 4 (monitor quality of care). Most of the latter group were late-effects studies that have played an important role in documenting the sequelae of therapy and disease in childhood cancer. Results from late-effects studies have been important in generating interest in new treatments that are toxicity and sequelae sparing.

Generic profile measures, such as the CHQ, and generic preference-based measures, such as the HUI, have been applied in a number of late-effects studies that monitor the quality of care in terms of the health status and HRQL in a cohort of survivors. The use of broad comprehensive measures to examine late effects is appropriate.

Many of the studies of HRQL in pediatric ALL have been done in the context of assessing late effects: the quality of survivorship. The overwhelming majority of these studies are cross-sectional. These studies have provided important evidence on construct validity (and in some cases internal consistency reliability); however, given their cross-sectional nature, they have not provided information on responsiveness or sensitivity to change.

In pediatric ALL, there are a number of standard phases of treatment specified within widely used treatment protocols. These phases typically involve diagnosis, induction of remission, consolidation of remission, maintenance therapy, and discharge to long-term follow-up. In less successful cases, additional phases, such as reinduction of remission, intensification of therapy, salvage therapy, or palliative care, may be relevant. The bulk of available evidence on HRQL is related to the quality of survivorship.

Speechley et al. (36) provided evidence on the construct validity of the CHQ and HUI2 (Table 2). Both measure captured burdens expected on the basis of known groups. Correlations among similar constructs included in both measures were moderate to high.

Barr et al. (37) provided evidence on the construct validity of HUI2 (Table 2). The burden of morbidity among survivors of high-risk ALL exceeded that among survivors of standard-risk ALL. Feeny et al. (38) provided evidence on the construct validity of HUI2 in a cohort of survivors of high-risk ALL. Burdens were found in cognition and emotion. Bradlyn et al. (22) found that QWB scores and performance status ratings were significantly correlated. Trudel et al. (21) reported on results of expected correlations between POQOLS and HUI2.

There is less evidence on the responsiveness (sensitivity to change) of these generic instruments to monitor changes over time during therapy. In some cases these generic measures have been responsive. For instance, Barr et al. (32) and Furlong et al. (39) provided evidence on the responsiveness of HUI2 and HUI3 in detecting changes in HRQL during 3-week cycles of high-dose corticosteroids during "maintenance" therapy for ALL (Table 2). As predicted a priori, patients experienced difficulties with mobility/ambulation, pain, and emotion during the week that they were on high-dose corticosteroids and demonstrated few problems during the week off all therapy. Nonetheless, given the coarseness of the generic measures, there is reason to suspect that these measures may not capture all of the important HRQL trends experienced during therapy. The complementary use of specific, targeted measures is therefore desirable.

Kazak et al. (40) provided evidence on the responsiveness of the POQOLS. Statistically significant changes in POQOLS scores were detected over time in the context of a randomized controlled clinical trial. Within the context of a prospective study, Parsons et al. (31) provided evidence of responsiveness for the Child Health Rating Inventories.

Studies involving the costing of health care services in pediatric ALL have started to emerge only recently (Table 3). There are a number of narrowly focused studies reporting on estimates of the cost of particular components of treatment, such as the cost of treating febrile neutropenia or using granulocyte colony-stimulating factor. For instance, Charnas et al. (41) examined alternatives for the treatment of febrile neutropenia in children with cancer. Luce et al. (42) retrospectively analyzed data from a randomized controlled clinical trial of patients undergoing bone marrow transplantation. The mean charges were lower for patients receiving recombinant human granulocyte-macrophage colony-stimulating factor than for those receiving placebo. The availability of these studies may well reflect sponsorship by pharmaceutical firms. There are relatively few cost-effectiveness studies in which the costs and consequences of two (or more) relevant treatment options (e.g., inpatient versus home care) are compared. Only one study focuses on costs borne by families (43). There are no published, comprehensive economic analyses of pediatric ALL.

With respect to the areas of application, approximately 73.1% of the economic studies listed in Table 3 focus on establishing cost-effectiveness, whereas 26.9% provide descriptive evidence on the cost of illness.

During the 1990s, there was a trend toward an increase in the annual number of publications (Fig. 1). Few HRQL or costing studies in ALL were published prior to 1990. Although numerous generic and pediatric cancer-specific measures were identified that could potentially be used to study pediatric ALL, most have only been applied in a handful of studies (Table 4). Among the generic measures, the HUI2 and HUI3 were the most widely applied. Overall, the POQOLS (24) pediatric cancer-specific HRQL measure was the most widely used instrument in studies including children with ALL.



View larger version (31K):
[in this window]
[in a new window]
  		Fig. 1. Trend in the number of studies of acute lymphoblastic leukemia, 1990-2000. HRQL = Health-related quality of life.

 

View this table:
[in this window]
[in a new window]
  		Table 4. HRQL instruments applied in studies of ALL*
 


   DISCUSSION
Top
 Notes
 Abstract
 Background
 Methods
 Results
 Discussion
Appendix 1. Data Abstraction...
 References
 
Historically, HRQL measures have not often been used to supplement traditional end points of treatment effectiveness in pediatric oncology (8). HRQL measurement in pediatric oncology is particularly challenging. The development of measures requires access to a sufficient number of patients (and families). Adequate instrument development requires the accumulation of evidence on the measurement properties of instruments. Relevant properties include test-retest reliability, interrater reliability, sensitivity to change (or responsiveness), and validity. Given the absence of a "gold standard" for assessing HRQL, the investigation of validity is largely accomplished through construct validation. Although commentators tend to declare that instruments have been shown to be reliable and valid, in reality such evidence tends to be context specific (27). In particular, construct validity involves the accumulation of evidence over time.

Given that the incidence of pediatric ALL, the most common of all cancers in childhood, is (fortunately) low and that even large tertiary care centers typically have at most only a modest number of patients, the extent of evidence on HRQL in pediatric ALL gathered during the 1990s is impressive. It is also remarkable that investigators have been able to develop a number of promising measures. In general, the pediatric oncology-specific and pediatric oncology disease-specific instruments focus on health status and HRQL during therapy. Thus, it may be wise to use these measures along with generic measures that are also relevant for the assessment of long-term outcomes. Despite the limited patient numbers and resources available, there has been substantial progress on HRQL assessment in childhood cancer.

To date, investigators from a wide variety of disciplines have contributed to the development of HRQL measures for use in pediatric oncology. The disciplines included pediatric oncology, epidemiology, economics, decision science, psychology, health-services research, nursing, and statistics. Experience in instrument development in pediatric oncology is consistent with experience in adult HRQL in which three major measurement traditions are represented: psychometrics, clinimetrics, and economics. Although the involvement of a number of disciplines may raise the cost of studies, it has enriched the variety and quality of the measures developed. In general, it is important to involve a range of disciplines in work on HRQL in pediatric oncology.

Toward the objective of enriching the evidence on HRQL measurement in pediatric oncology, several themes emerge from this review. [These themes build upon earlier publications by Feeny et al. (16,44).]

Theme 1: HRQL Tools Address Important Questions

Survival rates for most cancers in childhood have risen dramatically over the last five decades. Pediatric ALL was almost universally fatal in the early 1940s; today the survival rate is greater than 85%. An important reaction among clinical researchers has been a shift in focus to finding therapies that lessen the toxicity burden during treatment and reduce sequelae, without sacrificing survival. HRQL measures provide crucial information on whether or not the new therapies really are toxicity and sequelae sparing. HRQL tools provide a solid evidential basis for assessing quality-adjusted survival and can serve as inputs into economic evaluations. Greater use needs to be made of the measures that have been developed, especially in longitudinal studies. Surrogate clinical measures are often incomplete and can be misleading. The formal assessment of HRQL during treatment and the assessment of the HRQL associated with long-term outcomes are essential in answering many of the key clinical questions.

Theme 2: Measures Are Available

As this review demonstrates, there are a number of generic and specific measures that have been used productively in pediatric ALL. There is substantial evidence on the reliability, responsiveness, and validity of some measures; in other cases, the evidence is still emerging. Twenty years ago an investigator interested in assessing HRQL in pediatric ALL would have had little choice of measures. Today there are a number of measures from which to choose; the availability of useful measures is no longer a barrier. Efforts focused on accelerating the accumulation of evidence are needed (see theme 5 below); nonetheless, suitable measures already exist. The lack of availability of a credible instrument is seldom a valid reason for not measuring HRQL in pediatric ALL studies.

Theme 3: Children Can Respond on Their Own Behalf

Early studies on HRQL in pediatric ALL tended to rely on reports by parents and health care professionals. More recently in pediatric studies in general and pediatric ALL in particular, a number of studies have also asked children to complete questionnaires. In general, results indicate that by the age of 7 or 8 years, children generally provide reliable responses. Furthermore, a number of studies [e.g., Parsons et al. (31)] indicate that children can often provide information that is unavailable from parental reports. Thus, children should be engaged as respondents whenever possible.

Theme 4: There Are Multiple Valid Viewpoints

Information provided by patients, parents, and healthcare professionals is often complementary. Each type of respondent has a valid and important perspective. Children can provide insights on their HRQL, particularly the intrinsically subjective elements such as pain and emotion. Parents observe their children in a variety of contexts and social settings, and their responses can incorporate that information. Healthcare professionals are often more aware of the full range of experiences and are well informed about norms both for children with pediatric ALL and for the general pediatric population. Information from each of these three types of respondents is potentially valuable. Thus, rather than rely on only one source of information, it is important to develop strategies to share information. Furthermore, evidence on interrater reliability (or agreement) indicates that, in general, the responses of children, parents, and health care professionals are not interchangeable. This is especially germane for subjective dimensions (domains) of health status.

Theme 5: Implications—Harness the Cooperative Groups

The overwhelming majority of children with ALL in the United States and Canada are treated on established protocols for which there is rigorous evidence of clinical effectiveness based on results from randomized controlled clinical trials (4). Many children being treated for ALL are enrolled in clinical trials and related studies. This pediatric experience (which, in general, contrasts with the situation in adult oncology) is a reflection of the considerable accomplishments of the two major cooperative groups: the Pediatric Oncology Group and the Childhood Cancer Study Group. Recently, these two groups have merged into the Children's Oncology Group (COG). The use of the cooperative group will help to overcome the limited sample sizes available at even the largest treatment centers. It is now time to add HRQL to the scope of activities of the cooperative group. First, modest funds should be made available for methodological assessments of HRQL measures. Second, expertise on HRQL measurement (including specific, generic, and preference-based measures) should be added to at least some of the teams that design future trials. Third, the cooperative group needs to start to include existing measures in their studies. In particular, there are no examples in the published literature of studies using HRQL throughout the process of treatment from diagnosis and induction of remission, to discharge and to long-term follow-up. More information on the natural history of HRQL during all phases of treatment and survival is needed. Currently, we have a number of useful snapshots of portions of that experience, but we lack comprehensive longitudinal evidence. Given the recent inclusion of HRQL measures in a few trials, preliminary evidence on the patterns of HRQL over time will be emerging soon. Fourth, when the assessment of existing tools or experience in studies identifies a major gap, the cooperative group could be used to expedite the development of new instruments to ameliorate those gaps. Of course, the inclusion of HRQL measures in any study must be linked explicitly to specific study objectives or hypotheses to be tested.

If the methodological work on HRQL in pediatric oncology and the application of existing HRQL measures in the COG trials become reasonably common, if not routine, the rate of progress on HRQL in pediatric oncology will be increased substantially. The spectrum of morbidities in many other illnesses of children is encompassed by the morbidities among children with cancer (16). Therefore, a focus on HRQL in pediatric oncology affords a context within which to build a prototype strategy for the assessment of HRQL among all children. In fact, several approaches originally developed in the context of childhood cancer have been applied already in other areas in pediatrics, such as the use of the HUI in follow-up studies among survivors of extremely low birth weight and population health surveys of children. HRQL studies in pediatric oncology can serve as a foundation for HRQL studies in other areas of pediatrics. Tools developed in pediatric HRQL may be directly transferable to other areas or may serve as a useful starting point for the development of other specific pediatric HRQL instruments.

Theme 6: Implications—Identification of Topics for the Research Agenda

The accumulation of additional evidence on the existing measures is a key item for the research agenda. The generation of such evidence will help investigators to learn which measures are suitable for which purposes and how to interpret HRQL scores. Evidence on the lack of adequate measures in particular situations will help to identify priorities for the development of new measures. Only by subjecting existing instruments to appropriate assessments will we know if new measures need to be developed. In particular, additional evidence on responsiveness is needed. In addition, evidence on the entire path of HRQL experience from diagnosis through adulthood is needed.

The measures that have been used to date have primarily been used for discriminative (distinguish among persons or groups at a point in time; cross-sectional study) or evaluative (assessment of within-person change or time; prospective studies such as clinical trials) purposes. Another set of issues for the research agenda concerns the use of these measures in managing individual patients and their families. Can HRQL scores be used to identify patients with elevated treatment morbidity for whom additional interventions may be indicated? Can simple HRQL measures be used as screening tools to identify subjects for more extensive evaluation?

A related issue that has received little attention is the prognostic value of baseline observations of health status and HRQL. In a number of (but not all) areas in adult oncology, baseline HRQL measures have been shown to have independent prognostic value for survival, controlling for standard clinical prognostic indicators [see for instance Clinch (17), Coates et al. (45), Dancey et al. (46), and Ganz et al. (47)]. It will be interesting to see if the HRQL measures have predictive value in pediatric oncology settings.

Most work to date involving self-report by children has been in school-aged children, yet many of the children with ALL are preschoolers. A number of investigators are exploring innovative approaches to obtaining HRQL information from younger children; such approaches include cartoon and touch-screen computer formats. Clearly, additional work on developing and assessing these and other approaches is needed. In general, HRQL measures for children need to make allowances for the development stages of childhood; this will be particularly important for measures that are used for preschool children.

Finally, the accumulation of evidence on interpretability and usefulness of HRQL in pediatric oncology are important research priorities (16,48-50). Interpretability refers to the extent to which one can assign meaning, both qualitatively and quantitatively, to HRQL scores (51). Usefulness refers to the impact that assessing HRQL has on decisions that are made, including the management of specific patients and families, the development of clinical policy and guidelines, the conduct of clinical trials, resource allocation, and the identification of research priorities. There is relatively little evidence on interpretability in the context of pediatric oncology and even less on usefulness. These gaps should be filled.

In summary, HRQL measures have the potential to assist in the identification of the morbidity burdens of treatment, assessment of the quality of survival, evaluation of new treatments and interventions in pediatric oncology, and the management of patients and families. There is a small but impressive collection of measures with proven track records. A variety of disciplines have contributed to the existing body of knowledge. The instruments and expertise to support vigorous application of HRQL tools to clinically important questions in cancer care in pediatric settings exist. Investments in the application of these tools will likely pay handsome returns.


   APPENDIX 1. DATA ABSTRACTION FORMS
Top
 Notes
 Abstract
 Background
 Methods
 Results
 Discussion
 Appendix 1. Data Abstraction...
References
 
Data Abstraction Form I: Description of HRQL Instruments in Pediatric ALL

  1. Type of instrument (profile, preference-based, cancer-specific, cancer subtype specific)
  2. Instrument name (relevant references) ([ref.]—author, journal, volume, number, month, pages)
  3. Source of items (health professionals, general public, parents—specific type, family members, literature review)
  4. Criteria for selection of items (factor analysis, ratings of importance/clinical impact)
  5. Response options (dichotomous, 5-point Likert, 7-point Likert, categorical)
  6. Recall period
  7. Domains
  8. Scoring
  9. Intended respondent
  10. Target age group
  11. Number of items
  12. Format (interviewer, self-report)
  13. Country (language)
  14. Translations
  15. Validity (content, construct, discriminant, concurrent, face)
  16. Reliability (internal consistency, test-retest, interrater agreement)

Data Abstraction Form II: Applications of HRQL Instruments in ALL

  1. Instrument
  2. Reference (paper reporting application of instrument: author, year)
  3. Data collection period (year[s])
  4. Site of data collection
  5. Demographics (sex, age range)
  6. Sample size (total and by demographic group)
  7. Description of ALL sample
  8. Respondents (patient, parent, patient/parent, doctor, nurse, other ____).
  9. Study design (phase I, II, or III randomized trial; nonrandomized intervention trial; prospective or retrospective cohort study; case-control study; cross-sectional study)
  10. Phase of care (remission, induction, relapse, maintenance, bone marrow transplantation [BMT], intensification)
  11. Medication regimen
  12. Evidence on reliability (internal consistency, test-retest, interrater agreement)
  13. Validity (content, construct, convergent, discriminant, concurrent, face)
  14. Responsiveness (longitudinal construct validity)
  15. Results
  16. Statistical power of design mentioned? (yes/no)
  17. Confidence intervals provided for point estimates? (yes/no)
  18. Clinical importance of outcomes discussed? (yes/no)

Data Abstraction Form III: Studies of Economic Resource Use in Pediatric ALL

  1. Brief description
  2. Reference (author, year)
  3. Study design (prospective, retrospective, decision modeling, meta-analysis, case-control)
  4. Analytic design (cost-identification, cost minimization analysis, cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis)
  5. Description of patient population
  6. Sample size (n of each group; ALL sample n = ____)
  7. Intervention/treatment comparators (if applicable)
  8. Perspective of study (e.g., hospital, health maintenance organization [HMO], societal)
  9. Analytic horizon (and discount rate, if applicable)
  10. Care setting (single-institution, multi-institutional, HMO, citywide, nation-wide)
  11. Outcome measures (e.g., quality-adjusted life years [QALY], natural units)
  12. Source of preferences (if applicable, e.g., HUI, direct utilities)
  13. Resources included—formal health care
  14. Resources included—patient resources
  15. Sources of resource information (standard cost list, hospital, etc.)
  16. Results
  17. Sensitivity analyses (no/yes [univariate or multivariate])


   NOTES
 
Editor's note: D. H. Feeny is an owner of Health Utilities, Inc., which owns the copyright to the Health Utilities Index and related materials discussed in this review.

Supported by the National Cancer Institute, National Institutes of Health, Department of Health and Human Services (contract 263-MQ-112556).

We are grateful to Melinda Connolly and Jeffrey Johnson for permission to use excerpts from Table 1 in their article (1). We thank Tamara Davis, Karen Nelson, Kris Schindel, Janice Varney, Tania Stafinski, Wanda Draginda, and Joseph Gebran of the Institute of Health Economics for their assistance. We also acknowledge the contributions of Ronald D. Barr, William Furlong, Melissa Hudson, and Raymond K. Mulhern to issues discussed in this article.

An earlier version of this article appeared as a Working Paper for the Institute of Health Economics, Edmonton, AB, Canada.


   REFERENCES
Top
 Notes
 Abstract
 Background
 Methods
 Results
 Discussion
 Appendix 1. Data Abstraction...
 References
 

1 Connolly M, Johnson J. Measuring quality of life in pediatric patients. Edmonton (AB): Institute of Health Economics; 1998 . Working Paper 98-1.

2 Pickard AS, Topfer LA, Feeny DH. A rapid structured review of studies on health-related quality of life and economic evaluations of pediatric acute lymphoblastic leukemia. JNCI Monog 2004; 33: 102 -25.

3 SEER (Surveillance, Epidemiology and End Results), SEER Cancer Statistics Review 1973-1998. Bethesda (MD): National Cancer Institute; 1998.

4 Bleyer WA. The U.S. pediatric cancer clinical trials programmes: international implications and the way forward. Eur J Cancer 1997; 33: 1439 -47.[CrossRef][ISI][Medline]

5 Barr RD, Feeny D, Furlong W, Weitzman S, Torrance GW. A preference-based approach to health-related quality of life for children with cancer. Int J Ped Hematol Oncol 1995; 2: 305 -15.

6 Boggs SR, Graham-Pole J, Miller EM. Life-threatening illness and invasive treatment. The future of quality of life assessment and research in pediatric oncology. In: Johnson JH, Johnson SB, editors. Advances in child health psychology. Gainsville (FL): University of Florida Press; 1991 . p. 353-61.

7 Bradlyn AS, Harris CV, Spieth LE. Quality of life assessment in pediatric oncology: a retrospective review of Phase III reports. Soc Sci Med 1995;41: 1463 -5.[CrossRef][ISI][Medline]

8 Bradlyn AS, Ritchey AK, Harris CV, Moore IM, O'Brien RT, Parsons SK, et al. Quality of life research in pediatric oncology. Research methods and barriers. Cancer 1996; 78: 1333 -9.[CrossRef][ISI][Medline]

9 Calaminus G, Kiebert G. Studies on health related quality of life (HRQL) in childhood cancer in the European setting—an overview. Int J Cancer Suppl 1999;12: 83 -6.[Medline]

10 Feeny D, Furlong W, Barr RD. Multiattribute approach to the assessment of health-related quality of life: Health Utilities Index. Med Pediatr Oncol 1998;Suppl 1: 54 -9.

11 Jenney ME. Theoretical issues pertinent to measurement of quality of life. Med Pediatr Oncol 1998;Suppl 1: 41 -5.

12 Jenney ME, Kane RL, Lurie N. Developing a measure of health outcomes in survivors of childhood cancer: a review of the issues. Med Pediatr Oncol 1995;24: 145 -53.[ISI][Medline]

13 Monaco GA. Commentary on assessing health-related quality of life in children with cancer. Int J Cancer Suppl 1999; 12: 10 .[Medline]

14 Parsons SK, Brown AP. Evaluation of quality of life of childhood cancer survivors: a methodological conundrum. Med Pediatr Oncol 1998; Suppl 1: 46-53.

15 Reaman GH, Haase GM. Quality of life research n childhood cancer. The time is now. Cancer 1996; 78: 1330 -1332.[CrossRef][ISI][Medline]

16 Feeny D, Furlong W, Mulhern RK, Barr RD, Hudson M. A framework for assessing health-related quality of life among children with cancer. Suppl Int J Cancer 1999;12: 2 -9.

17 Clinch JJ. The Functional Living Index—Cancer: ten years later. In: Spilker BF, editor. Quality of life and pharmacoeconomics in clinical trials. Philadelphia (PA): Lippincott-Raven; 1996 . p. 215-25.

18 Cella DF, Bonomi AE. The Functional Assessment of Cancer Therapy (FACT) and Functional Assessment of HIV Infection (FAHI) quality of life measurement system. In: Spilker BF, editor. Quality of life and pharmacoeconomics in clinical trials. Philadelphia (PA): Lippincott-Raven; 1996. p. 203 -14.

19 Eiser C, Morse R. The measurement of quality of life in children: past and future perspectives. J Dev Behav Pediatr 2001; 22: 248 -56.[ISI][Medline]

20 Lansky SB, List MA, Lansky LL, Ritter-Sterr C, Miller DR. The measurement of performance in childhood cancer patients. Cancer 1987; 60: 1651 -6.[CrossRef][ISI][Medline]

21 Trudel JG, Rivard M, Dobkin PL, Leclerc JM, Robaey P. Psychometric properties of the Health Utilities Index Mark 2 system in paediatric oncology patients. Qual Life Res 1998; 7: 421 -32.[CrossRef][ISI][Medline]

22 Bradlyn AS, Harris CV, Warner JE, Ritchey AK, Zaboy K. An investigation of the validity of the quality of Well-Being Scale with pediatric oncology patients. Health Psychol 1993; 12: 246 -50.[CrossRef][ISI][Medline]

23 Eiser C, Havermans T, Craft A, Kernahan J. Development of a measure to assess the perceived illness experience after treatment for cancer. Arch Dis Child 1995;72: 302 -7.[Abstract]

24 Goodwin DA, Boggs SR, Graham-Pole J. Development and validation of the Pediatric Oncology Quality of Life Scale. Psychol Assess 1994; 6: 321 -8.[CrossRef]

25 Feeny D, Furlong W, Barr RD, Torrance GW, Rosenbaum P, Weitzman S. A comprehensive multiattribute system for classifying the health status of survivors of childhood cancer. J Clin Oncol 1992; 10: 923 -8.[Abstract]

26 Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Intern Med 1993; 118: 622 -9.[Abstract/Free Full Text]

27 McDowell I, Newell C. Measuring health: a guide to rating scales and questionnaires. New York (NY): Oxford University Press; 1996 .

28 Drummond M, Rutten F, Brenna A, Pinto CG, Horisberger B, Jonsson B, et al. Economic evaluation of pharmaceuticals: a European perspective. Pharmacoeconomics 1993;4: 173 -86.[ISI][Medline]

29 Canadian Coordinating Office for Health Technology Assessment. Guidelines for economic evaluation of pharmaceuticals . 2nd ed. Ottawa (Canada): Canadian Coordinating Office for Health Technology Assessment (CCOHTA); 1997.

30 Sawyer M, Antoniou G, Toogood I, Rice M. A comparison of parent and adolescent reports describing the health-related quality of life of adolescents treated for cancer. Int J Cancer Suppl 1999; 12: 39 -45.[Medline]

31 Parsons SK, Barlow SE, Levy SL, Supran SE, Kaplan SH. Health-related quality of life in pediatric bone marrow transplant survivors: according to whom? Int J Cancer Suppl 1999; 12: 46 -51.[CrossRef][Medline]

32 Barr RD, Petrie C, Furlong W, Rothney M, Feeny D. Health-related quality of life during post-induction chemotherapy in children with acute lymphoblastic leukemia in remission: an influence of corticosteroid therapy. Int J Oncol 1997;11: 333 -9.

33 Phipps S, Dunavant M, Jayawardene D, Srivastiva DK. Assessment of health-related quality of life in acute in-patient settings: use of the BASES instrument in children undergoing bone marrow transplantation. Int J Cancer Suppl 1999;12: 18 -24.[Medline]

34 Varni JW, Katz ER, Seid M, Quiggins DJ, Friedman-Bender A. The Pediatric Cancer Quality of Life Inventory-32 (PCQL-32): I. Reliability and validity. Cancer 1998;82: 1184 -96.[CrossRef][ISI][Medline]

35 Varni JW, Katz ER, Seid M, Quiggins DJ, Friedman-Bender A, Castro CM. The Pediatric Cancer Quality of Life Inventory (PCQL). I. Instrument development, descriptive statistics, and cross-informant variance. J Behav Med 1998;21: 179 -204.[CrossRef][ISI][Medline]

36 Speechley KN, Maunsell E, Desmeules M, Schanzer D, Landgraf JM, Feeny DH, et al. Mutual concurrent validity of the Child Health Questionnaire and the Health Utilities Index: an exploratory analysis using survivors of childhood cancer. Int J Cancer Suppl 1999; 12: 95 -105.[Medline]

37 Barr RD, Furlong W, Dawson S, Whitton AC, Strautmanis I, Pai M, et al. An assessment of global health status in survivors of acute lymphoblastic leukemia in childhood. Am J Ped Hematol Oncol 1993; 15: 284 -90.[ISI][Medline]

38 Feeny D, Leiper A, Barr RD, Furlong W, Torrance GW, Rosenbaum P, et al. The comprehensive assessment of health status in survivors of childhood cancer: application to high-risk acute lymphoblastic leukaemia. Br J Cancer 1993;67: 1047 -52.[ISI][Medline]

39 Furlong WJ, Feeny DH, Torrance GW, Barr RD. The Health Utilities Index (HUI) system for assessing health-related quality of life in clinical studies. Ann Med 2001;33: 375 -84.[ISI][Medline]

40 Kazak AE, Penati B, Boyer BA, Himelstein B, Brophy P, Waibel MK, et al. A randomized controlled prospective outcome study of a psychological and pharmacological intervention protocol for procedural distress in pediatric leukemia. J Pediatr Psychol 1996; 21: 615 -31.[Abstract/Free Full Text]

41 Charnas R, Luthi AR, Ruch W. Once daily ceftriaxone plus amikacin vs. three times daily ceftazidime plus amikacin for treatment of febrile neutropenic children with cancer. Pediatr Inf Dis J 1997; 16: 346 -53.[CrossRef][ISI][Medline]

42 Luce BR, Singer JW, Weschler JM, Buckner CD, Sheingold SH, Shannon-Dorcy K, et al. Recombinant human granulocyte-macrophage colony-stimulating factor after autologous bone marrow transplantation for lymphoid cancer: an economic analysis of a randomised, double-blind, placebo-controlled trial. Pharmacoeconomics 1994; 6: 42 -8.[Medline]

43 Barr RD, Furlong W, Horsman JR, Feeny D, Torrance GW, Weitzman S. The monetary costs of childhood cancer to the families of patients. Int J Oncol 1996;8: 933 -40.

44 Feeny D. The assessment of health-related quality of life. What roles should it play in pediatric oncology? Med Pediatr Oncol 1999; 33: 184 .

45 Coates A, Gebski V, Signorini D, Murray P, McNeil D, Byrne M, et al. Prognostic value of quality-of-life scores during chemotherapy for advanced breast cancer. Australian New Zealand Breast Cancer Trials Group. J Clin Oncol 1992;10: 1833 -8.[Abstract]

46 Dancey J, Zee B, Osoba D, Whitehead M, Lu F, Kaizer L, et al. Quality of life scores: an independent prognostic variable in a general population of cancer patients receiving chemotherapy. The National Cancer Institute of Canada Clinical Trials Group. Qual Life Res 1997; 6: 151 -8.[ISI][Medline]

47 Ganz PA, Lee JJ, Siau J. Quality of life assessment. An independent prognostic variable for survival in lung cancer. Cancer 1991; 67: 3131 -5.[CrossRef][ISI][Medline]

48 Guyatt GH. Measuring health-related quality of life in childhood cancer: lessons from the workshop. Int J Cancer Suppl 1999; 12: 143 -6.[CrossRef][Medline]

49 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 1999; 12: 147 -50.[CrossRef][Medline]

50 Pollock BH. Obstacles and opportunities for the use of health-related quality-of-life assessment in pediatric cancer clinical trials. Int J Cancer Suppl 1999;12: 151 -3.[Medline]

51 Osoba D. Interpreting the meaningfulness of changes in health-related quality of life scores: lessons from studies in adults. Int J Cancer Suppl 1999;12: 132 -7.[CrossRef][Medline]

52 Starfield B, Riley AW, Green BF, Ensminger ME, Ryan SA, Kelleher K, et al. The adolescent child health and illness profile. A population-based measure of health. Med Care 1995; 33: 553 -66.[ISI][Medline]

53 Landgraf JM, Maunsell E, Speechley KN, Bullinger M, Campbell S, Abetz L, et al. Canadian-French, German and UK versions of the Child Health Questionnaire: methodology and preliminary item scaling results. Qual Life Res 1998;7: 433 -45.[CrossRef][ISI][Medline]

54 Koopman HM, vanWeel MH, vanDiest W, Hoogerbrugge PM. Health-related quality of life of children with acute leukemia. Blood 1997; 90: 3771 .

55 Stein RE, Jessop DJ. Functional status II®. A measure of child health status. Med Care 1990; 28: 1041 -55.[ISI][Medline]

56 Lewis CC, Pantell RH, Kieckhefer GM. Assessment of children's health status. Field test of new approaches. Med Care 1989; 27: S54 -S65.[CrossRef][ISI][Medline]

57 Bruil J, Maes S, le Coq L, Boeke J. The development of the How Are You? (HAY), a quality of life questionnaire for children with a chronic illness. Qual Life Newsl 1997; 18: 9 .

58 Ravens-Sieberer U, Bullinger M. Assessing health-related quality of life in chronically ill children with the German KINDL: first psychometric and content analytical results. Qual Life Res 1998; 7: 399 -407.[CrossRef][ISI][Medline]

59 Apajasalo M, Sintonen H, Holmberg C, Sinkkonen J, Aalberg V, Pihko H, et al. Quality of life in early adolescence: a sixteen-dimensional health-related measure (16D). Qual Life Res 1996; 5: 205 -11.[CrossRef][ISI][Medline]

60 Apajasalo M, Rautonen J, Holmberg C, Sinkkonen J, Aalberg V, Pihko H, et al. Quality of life in pre-adolescence: a 17-dimensional health-related measure (17D). Qual Life Res 1996; 5: 532 -8.[CrossRef][ISI][Medline]

61 Feeny DH, Torrance GW, Furlong WJ. Health Utilities Index. In: Spilker B, editor. Quality of life and pharmacoeconomics in clinical trials. Philadelphia (PA): Lippincott-Raven; 1996. p. 239 -52.

62 Barr RD, Pai MK, Weitzman S, Feeny D, Furlong W, Rosenbaum P, et al. A multi-attribute approach to health status measurement and clinical management—illustrated by an application to brain tumors in childhood. Int Oncol 1994;4: 639 -48.

63 Phipps S, Hinds PS, Channell S, Bell GL. Measurement of behavioral, affective, and somatic responses to pediatric bone marrow transplantation: development of the BASES scale. J Pediatr Oncol Nurs 1994; 11: 109 -17.[Medline]

64 Seid M, Varni JW, Rode CA, Katz ER. The Pediatric Cancer Quality of Life Inventory: a modular approach to measuring health-related quality of life in children with cancer. Int J Cancer Suppl 1999; 12: 71 -6.[CrossRef][Medline]

65 Varni JW, Rode CA, Seid M, Katz ER, Friedman-Bender A, Quiggins DJ. The Pediatric Cancer Quality of Life Inventory-32 (PCQL-32). II. Feasibility and range of measurement. J Behav Med 1999; 22: 397 -406.[CrossRef][ISI][Medline]

66 Calaminus G, Weinspach S, Teske C, Gobel U. Quality of life in children and adolescents with cancer. First results of an evaluation of 49 patients with the PEDQOL questionnaire. Klin Padiatr 2000; 212: 211 -5.[CrossRef][ISI][Medline]

67 Varni JW, Seid M, Rode CA. The PedsQL: Measurement Model for the Pediatric Quality of Life Inventory. Med Care 1999; 37: 126 -39.[CrossRef][ISI][Medline]

68 Armstrong FD, Toledano SR, Miloslavich K, Lackman-Zeman L, Levy JD, Gay CL, et al. The Miami Pediatric Quality of Life Questionnaire: parent scale. Int J Cancer Suppl 1999; 12: 11 -7.[CrossRef][Medline]

69 Watson M, Edwards L, von Essen L, Davidson J, Day R, Pinkerton R. Development of the Royal Marsden Hospital Paediatric Oncology Quality of Life Questionnaire. Int J Cancer Suppl 1999; 12: 65 -70.[CrossRef][Medline]

70 Levi RB, Drotar D. Health related quality of life in childhood cancer: discrepancy in parent-child reports. Int J Cancer Suppl 1999; 12: 58 -64.[CrossRef][Medline]

71 Apajasalo M, Sintonen H, Siimes MA, Hovi L, Holmberg C, Boyd H, et al. Health-related quality of life of adults surviving malignancies in childhood. Eur J Cancer 1996; 32A: 1354 -8.[CrossRef][ISI][Medline]

72 Billson AL, Walker DA. Assessment of health status in survivors of cancer. Arch Dis Child 1994; 70: 200 -4.[Abstract]

73 Kanabar DJ, Attard-Montalto S, Saha V, Kingston JE, Malpas JE, Eden OB. Quality of life in survivors of childhood cancer after megatherapy with autologous bone marrow rescue. Pediatr Hematol Oncol 1995; 12: 29 -36.[ISI][Medline]

74 Torrance GW, Feeny D, Furlong W, Barr RD, DePauw S. Health Utilities Index: further evidence of responsiveness. Value Health 1998;1: 41 .

75 Feeny D, Barr RD, Furlong W, Torrance GW, Weitzman S. Quality of life of the treatment process in pediatric oncology: an approach to measurement. In: Osoba D, editor. Effect of cancer on quality of life. Boca Raton (FL): CRC; 1991. p. 73 -88.

76 Eiser C, Havermans T, Craft A, Kernahan J. Validity of the Rotterdam Symptom Checklist in paediatric oncology. Med Pediatr Oncol 1997;28: 451 -4.[CrossRef][ISI][Medline]

77 Kazak AE, Boyer BA, Brophy P, Johnson K, Scher CD, Covelman K, et al. Parental perceptions of procedure-related distress and family adaptation in childhood leukemia. Child Health Care 1995; 24: 143 -58.

78 Kazak AE, Penati B, Waibel MK, Blackall GF. The Perception of Procedures Questionnaire: psychometric properties of a brief parent report measure of procedural distress. J Pediatr Psychol 1996; 21: 195 -207.[Abstract/Free Full Text]

79 Kazak AE, Barakat LP. Brief report: parenting stress and quality of life during treatment for childhood leukemia predicts child and parent adjustment after treatment ends. J Pediatr Psychol 1997; 22: 749 -58.[Abstract/Free Full Text]

80 Matthes-Martin S, Lamche M, Ladenstein R, Emminger W, Felsberger C, Topf R, et al. Organ toxicity and quality of life after allogeneic bone marrow transplantation in pediatric patients: a single centre retrospective analysis. Bone Marrow Transplant 1999; 23: 1049 -53.[CrossRef][ISI][Medline]

81 Baranko PV. A cost comparison of laboratory charges in treating childhood leukemia. A children's cancer group analysis. Am J Pediatr Hematol Oncol 1994;16: 102 -3.[ISI][Medline]

82 Bash RO, Katz JA, Cash JV, Buchanan GR. Safety and cost effectiveness of early hospital discharge of lower risk children with cancer admitted for fever and neutropenia. Cancer 1994; 74: 189 -96.[CrossRef][ISI][Medline]

83 Bodkin CM, Pigott TJ, Mann JR. Financial burden of childhood cancer. Br Med J (Clin Res Ed) 1982; 284: 1542 -4.

84 Buda K, Tubergen DG, Levin MJ. The frequency and consequences of varicella exposure and varicella infection in children receiving maintenance therapy for acute lymphoblastic leukemia. J Pediatr Hematol Oncol 1996;18: 106 -12.[CrossRef][ISI][Medline]

85 Castagnola E, Lanino E, Giacchino R, Viscoli C, Dini G. Strategies for cost-containment: once-daily ceftriaxone plus amikacin as empiric therapy for febrile granulocytopenic children with cancer. J Chemother 1999; 11: 54 -60.[ISI][Medline]

86 Cimino MA, Rotstein CM, Moser JE. Assessment of cost-effective antibiotic therapy in the management of infections in cancer patients. Ann Pharmacother 1994;28: 105 -11.[Abstract]

87 Dagher R, Robertson KA, Lucas KG, Emanuel D, Smith FO. Outpatient total body irradiation for pediatric patients undergoing stem cell transplantation. Bone Marrow Transplantation 1997; 19: 1065 -7.[CrossRef][ISI][Medline]

88 Duncan N, Hewetson M, Atra A, Dick G, Pinkerton R. An economic evaluation of the use of granulocyte colony-stimulating factor after bone marrow transplantation in children. Pharmacoeconomics 1997; 11: 169 -74.[ISI][Medline]

89 Farah RA, Aquino VM, Munoz LL, Sandler ES. Safety and cost-effectiveness of outpatient total body irradiation in pediatric patients undergoing stem cell transplantation. J Pediatr Hematol Oncol 1998; 20: 319 -21.[CrossRef][ISI][Medline]

90 Jayabose S, Escobedo V, Tugal O, Nahaczewski A, Donohue P, Fuentes V, et al. Home chemotherapy for children with cancer. Cancer 1992; 69: 574 -9.[CrossRef][ISI][Medline]

91 Lansky SB, Cairns NU, Clark GM, Lowman J, Miller L, Trueworthy R. Childhood cancer: nonmedical costs of the illness. Cancer 1979; 43: 403 -8.[CrossRef][ISI][Medline]

92 Law B, Fitzsimon C, Ford-Jones L, McCormick J, Riviere M. Cost of chickenpox in Canada: part II. Cost of complicated cases and total economic impact. The Immunization Monitoring Program-Active (IMPACT). Pediatrics 1999;104(1 Pt 1): 7-14.[Abstract/Free Full Text]

93 Madero L, Gonzalez-Vicent M, Molina J, Madero R, Quintero V, Diaz MA. Use of concurrent G-CSF + GM-CSF vs G-CSF alone for mobilization of peripheral blood stem cells in children with malignant disease. Bone Marrow Transplant 2000;26: 365 -9.[CrossRef][ISI][Medline]<