Assessment of Patient-Reported Clinical Outcome in Pancreatic and Other Hepatobiliary Cancers: The FACT Hepatobiliary Symptom Index

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1 32 Journal of Pain and Symptom Management Vol. 24 No. 1 July 2002 Original Article Assessment of Patient-Reported Clinical Outcome in Pancreatic and Other Hepatobiliary Cancers: The FACT Hepatobiliary Symptom Index Susan Yount, PhD, David Cella, PhD, Kimberly Webster, MA, Nancy Heffernan, RN, Chih-Hung Chang, PhD, Linda Odom, MA, and Renilt van Gool, MD, MHA Center on Outcomes, Research and Education (S.Y., D.C., K.W., C.-H.C, L.O.), Evanston Northwestern Healthcare, Evanston, Illinois; Northwestern University (S.Y., D.C., C.-H.C.), Evanston, Illinois; Memorial Sloan-Kettering Cancer Center (N.H.), New York, New York; and Janssen Research Foundation (R.V.G.), Beerse, Belgium Abstract This study s aim was to develop and validate a symptom index derived from the Functional Assessment of Cancer Therapy-Hepatobiliary, a questionnaire measuring general and hepatobiliary disease specific aspects of quality of life. The item pool was narrowed to 26 questions that assess symptoms and function. Each of 95 hepatobiliary cancer experts narrowed the list to 5 of the most important to attend to when treating advanced hepatobiliary disease. Eight symptoms were endorsed by more than 20% of the experts (3 pain, 2 fatigue, nausea, weight loss, jaundice) and were named the FACT-Hepatobiliary Symptom Index-8 (FHSI-8). Among 51 hepatobiliary cancer patients, the FHSI-8 showed good internal consistency (0.79), test-retest reliability (r 0.86), strong association with mood (r 0.56), and patient differentiation by ECOG Performance Status Rating ( P ) and treatment status ( P 0.057). Symptom scaling in diseases such as hepatobiliary cancer is feasible and may provide an efficient, clinically-relevant endpoint for following groups over time. J Pain Symptom Manage 2002;24: U.S. Cancer Pain Relief Committee, Key Words Quality of life, cancer, symptoms, hepatobiliary cancer Introduction Hepatobiliary cancers occur with limited frequency in the United States but with increasing prevalence worldwide. 1 In Asia and Africa, liver Address reprint requests to: Susan E. Yount, PhD, Center on Outcomes, Research and Education, Evanston Northwestern Healthcare, 1001 University Place, Suite 100, Evanston, IL 60201, USA. Accepted for publication: October 5, cancer is one of the most common malignancies, and pancreatic cancer has increased fourfold in Japan and three-fold in Asia over the last 50 years. 1,2 Although the incidence of many hepatobiliary cancers in the United States is relatively low, they often carry a grave prognosis. This is due, in part, to the systemic and non-specific symptoms associated with these cancers, which often lead to diagnosis in an advanced stage. 3,4 Thus, symptom palliation is often the most beneficial therapy that can be offered to these patients. 5 U.S. Cancer Pain Relief Committee, /02/$ see front matter Published by Elsevier, New York, New York PII S (02)

2 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer 33 The limited possibility of curative treatments has implications not only for survival but also quality of life (QOL). 6 The QOL of people with advanced hepatobiliary cancer is adversely affected by symptoms of disease and treatment. 3,5 7 Both surgical and chemotherapeutic interventions have been shown to provide symptomatic relief and improved QOL in hepatobiliary cancer patients. 8,9 Experts in the field of oncology have recently identified a need for adapted, specially tailored QOL assessment in diseases such as hepatobiliary cancers, which are highly symptomatic and all too often involve rapid deterioration of health status. 4 Clinical and regulatory interest in a symptom-focused approach to QOL assessment, whereby the disease symptoms measured by multi-dimensional QOL questionnaires are aggregated in a clinically relevant and psychometrically acceptable manner, has increased. Despite a proliferation of well-established, reliable, and valid instruments to measure QOL in oncology, health care experts have voiced concerns about the ease of interpreting scores on these multi-item, multidimensional instruments. 14 Clinical researchers and practicing oncologists report uncertainty about how to interpret and derive clinical meaning from scores and how to translate the QOL information into treatment decisions. 18,20,21 Some physicians have also been resistant to incorporating QOL assessment into clinical trials Practical barriers include time and resource constraints and the perceived lack of a suitable questionnaire. 14,17,25 A brief, clinically meaningful set of symptoms related to hepatobiliary cancer could improve the willingness of researchers and clinicians to obtain these patient-reported outcomes. Regulatory agencies likewise are interested in valid patient-reported outcomes that bear some relationship to QOL. For example, since 1985, there has been an explicit recognition from the U.S. Food and Drug Administration (FDA) that, along with survival, benefit to QOL is one of two primary endpoints that could be considered for approval of new anti-cancer drugs. 26 Since then, it has been challenging to review data submitted in support of QOL claims. 27 The FDA has stated that strict standards for QOL claims may be needed to guard against claim expansiveness in which a promotional claim goes beyond the data supporting the claim. 28 Terms such as clinical benefit and patient reported outcomes have surfaced recently, reflecting a preference among clinical and regulatory reviewers of QOL data for tumor-specific symptom assessment with a focus on a therapeutic palliative target. Some early work, focused on a single-item symptom assessment, has contributed to regulatory approval in oncology. 29,30 However, these approaches fail to take advantage of recent measurement advances in symptom and QOL assessment. Most recently-validated measures of cancer-specific QOL incorporate an assessment of certain prevalent symptoms (e.g., pain and fatigue) within the larger context of multidimensional assessment. 10,11 Cancer-specific QOL questionnaires such as the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 10 and the Functional Assessment of Cancer Therapy-General (FACT-G) 11 assess a few common cancer symptoms and add more detailed, site-specific symptom assessment to the core general questionnaire. For example, in hepatobiliary cancer, 18 questions are added to the FACT-G to assess specific concerns related to hepatobiliary disease and is known as the FACT-Hepatobiliary (FACT- Hep). 31 Thus, while there are many questionnaires that have been developed and tested to assess cancer-specific symptoms, many of them have been nested within larger multidimensional QOL questionnaires. This creates an opportunity to derive clinically appropriate and precise evaluation of symptomatology in specific cancer populations. This study is an attempt to begin to address the concerns of the clinical and regulatory communities about the interpretability and relevance of multi-item, multi-dimensional QOL instruments and the more recent requests for a more targeted, symptom-focused approach to QOL assessment. In this article, we describe the development of a symptom index derived from a well-established multidimensional QOL measurement system, the Functional Assessment of Cancer Therapy. 11 Methods Study Design and Overview The development and validation of an index of hepatobiliary cancer symptoms was part of a larger study involving the development and vali-

3 34 Yount et al. Vol. 24 No. 1 July 2002 dation of the FACT-Hep QOL questionnaire. 31 The goal of the study was to determine if a subset of symptoms and concerns derived from the FACT-Hep could be demonstrated to have adequate psychometric properties to serve as a standalone brief index of hepatobiliary symptoms. This FACT Hepatobiliary Symptom Index (FHSI) was developed and validated in three phases. First, the FACT-Hep was developed and administered along with other measures to a sample of hepatobiliary cancer patients to allow for collection of data for reliability and validity of the candidate FHSI items. 31 Second, the FHSI items were selected by presentation of a list of symptoms and concerns from the FACT-Hep to an international sample of experts in treating hepatobiliary cancer patients who were asked to select the 5 most important symptoms/concerns to address in treating patients with these types of cancer. Third, FHSI psychometric performance was compared to FACT-Hep performance. Participants Patient Sample. The validation sample consisted of an initial pool of 59 patients recruited through the outpatient clinics at Memorial Sloan-Kettering Cancer Center. Inclusion criteria were: 1) 18 years of age or older; 2) diagnosis of cancer of the liver, pancreas, gallbladder, or bile duct; and 3) ability to speak, read, and write English. Four patients refused, and one patient was excluded due to a psychiatric history. Reasons for refusal included fatigue and concern about participating in research. Of the 54 patients enrolled, 51 patients (94%) completed the study. A complete description of the demographic and clinical characteristics of the validation sample is presented in Table 1. Physician Sample. The item selection phase of the study involved administering a survey of pre-selected symptoms to an international sample of 180 physicians who are experts in treating hepatobiliary cancers. The experts practice in 11 different countries, including the United States, Canada, Western Europe, India, and Russia. Ninety-five experts responded, yielding a response rate of 53%. Table 1 Descriptive Statistics of Patient Validation Sample (n 51) Characteristic n (%) Age Mean (SD) 61.5 (9.6) Range Sex Male 26 (51%) Female 25 (49%) Marital Status Married 39 (76%) Single 4 (8%) Separated/Divorced 5 (10%) Widowed 3 (6%) Ethnicity White, non-hispanic 45 (88%) Black, non-hispanic 1 (2%) Hispanic 2 (4%) Asian 3 (6%) Education 8 years or less 3 (6%) 9 12 years 28 (39%) years (35%) years (20%) Employment Status Employed 15 (29%) Homemaker 2 (4%) Disabled/unemployed 9 (18%) Retired 25 (49%) Disease Site Colon w/liver metastases 19 (38%) Hepatocellular 10 (20%) Pancreatic 5 (10%) Gallbladder 5 (10%) Other 9 (22%) Extent of disease NED 29 (56%) Local disease 6 (12%) Liver disease 8 (16%) Nodal metastases 1 (2%) Other 7 (14%) ECOG Performance Status 0 16 (31%) 1 12 (24%) 2/3 23 (45%) 4/5 0 (0%) Measures As the present study was part of a larger study on the QOL of hepatobiliary cancer patients, only a subset of the instruments in the battery of questionnaires administered to patients will be reported on here, including the FACT-G plus Hepatobiliary subscale (FACT- Hep), the Profile of Mood States (POMS), 32 and the Eastern Cooperative Oncology Group (ECOG) Performance Status Rating (PSR). 33 The FACT QOL measurement system began with the creation of a generic core questionnaire called the FACT-G. 11 The FACT-G is a 27- item compilation of general questions divided into four primary QOL domains: Physical Well- Being (PWB; 7 items), Social/Family Well- Being (SFWB; 7 items), Emotional Well-Being

4 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer 35 (EWB; 6 items), and Functional Well-Being (FWB; 7 items) using a five-point Likert-type scale ranging from 0 ( not at all ) to 4 ( very much so ). Scores are obtained for each of the specific domains as well as a total QOL score. An additional score obtained from the FACT-G, the Trial Outcome Index (TOI), 34,35 is created by summing the PWB, FWB, and Hepatobiliary subscales and was used in this study as a means of determining known-groups validity for the FHSI. The FACT-G has good test-retest reliability (r ranging from 0.82 to 0.92), is sensitive to change over time, and has been shown to possess good convergent and discriminant validity. 11 A number of subscales have since evolved that complement the FACT-G and address relevant disease-, treatment-, or condition-related issues not already covered in the general questionnaire, including the FACT-Hep. The FACT- Hep comprises the 27 core items plus an additional 18 hepatobiliary-specific items. This subscale has likewise been shown to have good internal consistency, test-retest reliability, and convergent and discriminant validity. 31 The Profile of Mood States (POMS) is a widely utilized 65-item self-report measure of 37 subjective mood states. 32 Adjectives are rated on a 5-point rating scale ranging from 0 ( not at all ) to 4 ( extremely ), and responses are summed to yield both subscale scores and a total mood disturbance score. The Eastern Cooperative Oncology Group (ECOG) PSR 33 is a single-item rating of the degree to which patients are able to participate in typical activities without the need for rest. This index is widely used in cancer clinical trials to assess functional capability of patients as they undergo treatment. The PSR score ranges from 0 ( I have normal activity without symptoms ) to 4 ( I am unable to get out of bed ). In this study, the PSR was obtained from patients themselves and served as a means of classifying patients for known-groups validation. Procedure Collection of Symptom Index Validation Data. Validation of the FHSI involved presentation of a packet of questionnaires, including the FACT- Hep, to a sample of hepatobiliary cancer patients recruited through the outpatient clinics at Memorial Sloan-Kettering Hospital. Patients were asked to complete a retest of the FACT- Hep at home within 3 7 days of the initial battery for purposes of test retest reliability. Construction of the Symptom Index. To narrow the item pool down to a clinically relevant subset of symptoms, eight items from the FACT-G that, based on face validity, were deemed to assess cancer-related symptoms or function and all 18 items from the Hepatobiliary subscale were included in the 26-item symptom survey administered to clinical experts. The survey instructions contained a two-step request: 1) select up to 10 of the most important symptoms to track when assessing the value of treatment for cancer of the pancreas ; 2) select up to 5 of the 10 that are the most clinically important symptoms to palliate in pancreatic cancer patients. While the ultimate goal is to develop a symptom index applicable to patients with all hepatobiliary cancers, this survey used pancreatic cancer as the reference point because it represents a large percentage of patients with hepatobiliary cancer, and because of extensive recent clinical trial activity in this disease. It was also believed that respondents would have an easier time endorsing priority symptoms if a specific diagnosis (such as pancreatic cancer) was given. Analysis Plan. Surveys returned by the experts were tabulated by frequency with which respondents selected a particular symptom/concern as one of the 5 most important. The most commonly endorsed items, as judged by examination of the frequency distribution, were retained for psychometric analysis as the FHSI. Two criteria for item retention were considered: 1) probability of chance endorsement as one of the top 5 symptoms/concerns; and 2) the 95% confidence interval around this probability of chance endorsement. The probability of an item being one of the 5 items selected by chance (19%) was calculated without adjusting for multiple comparisons by dividing 5 (the allowable number of very most important symptoms ) by the total number of items on the survey (26). Patient responses to these surviving items were subjected to analysis for determination of internal consistency (Cronbach s alpha), test retest reliability, and convergent and discriminant validity. To evaluate the unidimensionality and construct validity of the FHSI candidate items in

5 36 Yount et al. Vol. 24 No. 1 July 2002 greater detail, we also applied an item response theory (IRT) based approach. 36 For items meeting the more liberal of the criteria described above (i.e., meeting or exceeding chance probability of endorsement), Andrich s 37,38 rating scale extension of the Rasch measurement model was used to determine whether FHSI candidate items measure the same underlying construct. The WINSTEPS computer program 39 was used for Rasch analyses. Unweighted item fit mean square (MNSQ) values (expected value 1.0) were also calculated to identify potential misfitting items or those that indicate a lack of construct homogeneity with other items in a scale to assure scale unidimensionality. By convention, MNSQ 1.3 was set as the critical value for a misfitting item. The MNSQ value indicates the amount of error associated with the item estimate with respect to its fit with other items in the dimension being measured. For example, a MNSQ of 1.98 indicates 98% excess noise in the data, suggesting the item is measuring a different dimension than the one it is intended to measure. Table 2 Frequency of Endorsement of Checklist Symptoms/Concerns Symptoms/Concerns % Endorsed ( top 5 ) Pain 68 Weight loss 60 Feel fatigued 44 Nausea 30 Jaundice 26 Back pain 25 Stomach pain/discomfort 22 Lack of energy 20 (19% chance probability of endorsement) Itching 16 Feel ill 13 Stomach swelling/cramps 12 Diarrhea 10 Spend time in bed 9 Able to do usual activities 8 Side effects 7 Sadness 5 Digestion 3 Appetite 3 Constipation 3 Fevers 3 Change in taste 2 Nervous 0 Bowel control 0 Change in appearance 0 Chills 0 Dry mouth 0 Results FHSI Item Endorsement by Experts Ninety-five medical experts were presented with 26 symptoms and concerns associated with hepatobiliary cancer from which they were requested to select five of the most important symptoms to assess in treating these cancers. The frequency with which these items were endorsed is displayed in Table 2. Among the 8 most commonly selected items, pain is represented by 3 items, and fatigue/lack of energy is represented by 2 items. Of the 26 items, 5 (nervous, bowel control, change in appearance, chills, dry mouth) did not receive any expert endorsements as important symptoms to assess. To quantify the selection of items for the FHSI, two criteria were initially applied: 1) meeting or exceeding the probability of item endorsement by chance, and 2) meeting or exceeding the 95% confidence interval threshold around this chance probability. Using the more liberal criterion, 8 symptoms or concerns exceeded the threshold: pain, weight loss, nausea, jaundice, back pain, stomach pain/discomfort, and two fatigue items (lack of energy, feeling fatigued). Application of the more conservative criterion resulted in a scale comprised of 4 symptoms/concerns. For purposes of validation analyses, described below, the initial symptom index was considered to be composed of 8 items. (See Appendix for FHSI-8.) FHSI Validation Analyses The scores for the FACT-G, FACT-Hep, and FHSI are reported in Table 3. The scale raw scores were transformed to a scale for ease of comparison across scales. FACT-G and FACT-Hep. The FACT-G and FACT- Hep had good internal consistency (alpha 0.90 and 0.94, respectively). The PWB, SFWB, EWB, and FWB subscales as well as the Hepatobiliary subscale also demonstrated acceptable internal consistency (alpha 0.72 to 0.84). These measures were stable over a 3 7 day interval (test retest r 0.86 to 0.91). The FACT-G total score, FACT-Hep total score, well-being subscales and Hepatobiliary subscale were all significantly and negatively correlated with the POMS (r 0.59 to 0.85) with the excep-

6 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer 37 Table 3 Descriptive Statistics of Scales Scale/Subscale Raw Scores M (SD) (n 51) Transformed Scores (0 100) M (SD) (n 51) Cronbach s Alpha (n 51) Test Retest Correlation (n 50) Correlation with POMS (n 50) FACT-G Total 84.5 (13.1) 78.2 (12.1) a 0.85 a Physical well-being 22.6 (4.6) 80.6 (16.3) a 0.63 a Social/family well-being 24.2 (3.8) 86.3 (13.7) a 0.25 Emotional well-being 18.7 (4.1) 77.9 (17.3) a 0.76 a Functional well-being 19.1 (5.9) 68.1 (21.1) a 0.71 a FACT-Hep Total (20.6) 79.4 (11.5) a 0.80 a Hepatobiliary subscale 58.5 (9.0) 81.2 (12.5) a 0.59 a FHSI-3 (pain, weight loss, fatigue) 8.9 (2.5) 74.2 (20.8) a 0.60 a FHSI-4 (pain, weight loss, fatigue, nausea) 12.4 (2.9) 77.7 (18.3) a 0.61 a FHSI-6 (excluding jaundice, back pain) 17.7 (4.7) 73.9 (19.7) a 0.61 a FHSI-7a (excluding jaundice) 20.9 (5.2) 74.6 (18.5) a 0.56 a FHSI-7b (excluding back pain) 21.4 (5.0) 76.5 (17.9) a 0.61 a FHSI (5.38) 76.9 (16.8) a 0.56 a a p < tion of the SFWB subscale, which was not significantly associated with the POMS. FHSI Reliability Analyses. Analyses of the FHSI were conducted on various numbers and combinations of items comprising the scale: FHSI-3 (pain, weight loss, fatigue); FHSI-4 (pain, weight loss, fatigue, nausea); FHSI-6 (pain, weight loss, nausea, stomach pain/discomfort, 2 fatigue items); FHSI-7a (jaundice excluded); FHSI-7b (back pain excluded); and FHSI-8 (pain, two fatigue items, weight loss, nausea, stomach pain/discomfort, jaundice, back pain). Internal consistency of the various FHSI permutations ranged from 0.69 to Test retest correlations, while slightly lower than that of the FACT-Hep total score, were still within an acceptable range (r 0.77 to 0.86). FHSI Convergent Validity Analyses. All of the various FHSI versions were significantly and negatively associated with the POMS (r 0.56 to 0.61). Further, the FSHI-8 was significantly correlated with the FACT-G (r 0.66, P ), PWB (r 0.89, P ), EWB (r 0.33, P 0.05), FWB (r 0.64, P ), FACT-Hep total score (r 0.81, P ), and Hepatobiliary subscale (r 0.90, P ). Discriminant (Known-Groups) validity: Performance Status. The sample was divided into three groups by PSR (PSR 0 versus 1 versus 2/3). It was expected that better performance status (i.e., lower PSR) would be associated with higher QOL scores and better symptom status (lower scores on FHSI). As displayed in Figure 1, PSR was indeed associated with QOL as measured by PWB (F(2,48) 15.77, P ), EWB (F(2,48) 3.22, P 0.05), FWB (F(2,48) 17.08, P ), FACT-G (F(2,48) 19.70, P ), Hepatobiliary subscale (F(2,48) 18.22, P ), TOI (F(2,48) 23.64, P ), and FHSI-8 (F(2,48) 19.59, P ). Scores on SFWB were not significantly different across groups. Post hoc review of subgroup differences using Tukey s HSD indicated that the TOI and Hepatobiliary subscale scores differentiated all three PSR levels, whereas the FHSI-8, FACT-G, PWB, and FWB scores significantly differentiated two of the three levels. Specifically, the FACT-G, PWB, and FWB subscales differentiated the PSR 0 group from the PSR 1 and PSR 2/3 groups, while the FHSI-8 differentiated the PSR 2/3 group from the PSR 0 and PSR 1 groups. To provide an indication of the clinical significance of group differences, effect sizes for group comparisons were also calculated for all subscales, as displayed in Table 4. By convention, effect sizes of 0.20 are considered to be small in magnitude, those of 0.50 to be moderate, and effects sizes larger than 0.80 to be high in magnitude. 40 Discriminant (Known-Groups) validity: Treatment Status. The sample was dichotomized into groups of patients based on treatment status (on/off treatment). It was expected that offtreatment status would be associated with bet-

7 38 Yount et al. Vol. 24 No. 1 July 2002 Fig. 1. Mean FACT scale responses ( one standard error of the mean) by Patient ECOG Performance Status Rating (PSR). PSR groups were trichotomized into PSR 0, PSR 1, and PSR 2/3. [1] indicates discrimination between (PSR 0) v (PSR 1 or 2/3); [2] indicates discrimination between (PSR 0) v (PSR 1) v (PSR 2/3); [3] indicates discrimination between (PSR 0 or 1) v (PSR 2/3). *P 0.05, **P ter QOL (higher scores) and better symptoms status (lower FHSI scores). As displayed in Figure 2, treatment status was associated with improved PWB (t(50) 2.8, P 0.01), FWB (t(50) 2.4, P 0.05), Hepatobiliary subscale (t(51) 2.1, P 0.05), and the TOI (t(50) 2.6, P 0.05). The group differences were marginally significant for the FHSI-8 (t(50) 1.9, P 0.057). The SFWB, EWB, and FACT-G scores did not differ significantly by treatment status. Again, effect sizes were calculated for group differences in treatment status for each subscale, as shown in Table 5. Item-Level Analysis of FHSI. The results of the item analysis are displayed in Table 6. Items with MNSQ values outside the range are typically identified as possible misfitting items meriting more careful examination. 41 Misfit below MNSQ 0.7 suggest overfit to the concept being measured, and therefore do not perturb the measurement so much as introduce possible redundancy. Because this is already a brief index, the two items showing modest overfit were retained. MNSQ above 1.3 suggests misfit to the dimension being measured by the collection of questions. Data are presented (Table 3) on internal consistency variation as a function of including or excluding each of these two misfitting questions. In all cases excluding both (FHSI-6), including both (FHSI-8), including one or the other (FHSI-7a and FHSI-7b) internal consistency remained high. Discussion The goal of this study was to develop and evaluate an index assessing high priority concerns associated with advanced hepatobiliary

8 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer 39 Table 4 Effect Sizes for Performance Status Rating (PSR) Group Comparisons Effect Sizes for Group Comparisons PSR n Baseline Mean SD /3 0 2/3 Physical well-being / Social/family well-being / Emotional well-being / Functional well-being / FACT-G / FACT-Hep additional concerns / Trial Outcome Index (TOI) / FHSI / cancer by drawing items from the FACT-G 11 and the 18-item Hepatobiliary Subscale. 31 To this end, an 8-item symptom index, the FHSI-8, was constructed based on the clinical importance ratings of an international sample of hepatobiliary cancer specialists. Initial patient validation of the 8 items demonstrated that these items have adequate reliability and validity to assess the most important symptoms in this population. The 8-item version of the FHSI demonstrated good internal consistency, test retest reliability, and convergent validity. The FHSI-8 was significantly correlated with the FACT-G and its PWB, EWB, and FWB subscales as well as with the FACT-Hep and the Hepatobiliary Subscale subscale. The 8-item symptom index was also significantly associated with mood, such that better symptom status was associated with better mood. The FHSI-8 also successfully discriminated patients based on differences in performance and treatment status, and these differences were clinically significant, as evidenced by its moderate to high effect sizes. Patients with better performance status reported better symptom status than those with poorer performance status. Similarly, patients off treatment reported better symptom status than those who were on treatment. Although the FHSI-8 performed well and met or exceeded standards for acceptability in clinical research applications, it did not outperform existing FACT-Hep subscales. Usually, it performed comparably to the FACT PWB, FWB, Hepatobiliary subscale, and the aggregation of these, the TOI. In differentiating patients by PSR, the Hepatobiliary Subscale (18-items) and the TOI (32 items) were actually superior, separating not only patients with PSR 1 from those with PSR 2/3, but also from patients with PSR 0. The only advantage of the FHSI-8 in this context, then, is its shorter length. It becomes the discretion of the investigator as to whether to select the briefer assessment, assuming some degree of loss to precision, or the longer, more burdensome but accurate assessment. One consideration in the selection might be sample size projection. As projected sample size increases, acceptability of the shorter FHSI-8 would also increase. Another consideration is the relative importance of precision in individual diagnosis. However, as desire for accurate individual assessment in-

9 40 Yount et al. Vol. 24 No. 1 July 2002 Fig. 2. Mean FACT scale responses ( one standard error of the mean) by treatment status (off/on). *P 0.05, **P creases, one would be increasingly inclined to use the FACT-Hep (45 items) instead of the FHSI-8 due to its more favorable internal consistency. In addition to the 8-item version of the FHSI, results suggest that still-shorter versions may also have utility. The selection of the optimal number of items at 8 was somewhat arbitrary. An argument can be made in favor of the 6-item or one of the two 7-item indices, excluding one or both of the questions on jaundice and back pain, as the internal consistency of these alternatives was comparable. Even the 4-item alternative, retaining only those questions that exceeded the 95% confidence interval for chance probability of expert selection, represents a viable choice that results in a scale with items on pain, weight loss, fatigue, and nausea. One advantage of this version is the fact that each symptom category is weighted equally, avoiding the greater weighting given to pain and fatigue in the 8-item version. However, while the 4-item scale had an acceptable 42 internal consistency (alpha 0.70), the probability of lower internal consistency in future use is high. This was a factor in our recommending a slightly longer index. The data depicting item analysis for fit to a unidimensional model, an IRT-analogue to classical internal consistency, suggested that perhaps 6- or 7-item scales might be optimal, if the only purpose was to create a reproducible, unidimensional index. However, symptoms of hepatobiliary disease are diverse by nature, and although they tend to coexist as disease-related clusters, have a competing tendency to emerge

10 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer 41 Table 5 Effect Sizes for Treatment Status Group Comparisons Treatment Status a n Baseline Mean SD Effect Size Physical well-being Social/family well-being Emotional well-being Functional well-being FACT-G FACT-Hep additional concerns Trial Outcome Index (TOI) FHSI a Treatment Status Groups: 0 off-treatment, 1 on-treatment. somewhat independent of one another. This may be more true of jaundice and back pain than the others, and emerge as an indication of possible misfit. One then must struggle with the dilemma of retaining the symptom in the index because it captures a clinically important problem, risking a compromise in what is ultimately measured, or deleting the potentially misfitting symptom, creating a more unidimensional scale but also eliminating an important clinical issue. In this case, we opted to retain the 2 potentially misfitting items for their clinical relevance because their effect on internal consistency in this sample was negligible. Internal consistency remained high in cases where both jaundice and back pain were excluded (FHSI-6), both were included (FHSI-8), and one or the other was included (FHSI-7a and FHSI-7b). Retention or exclusion of these Item Content Table 6 Summary of Item Statistics for FHSI-8 a Item Difficulty Outfit Mean Square Lack energy Fatigue Stomach pain/discomfort Pain Back pain Weight loss Nausea Jaundice a Based on Andrich s extension of the Rasch rating scale model. 37,38 items can be a clinical investigational decision based upon the scale s future application. The importance of symptom control in the cancer population has been widely recognized due to the extraordinarily high prevalence of physical and psychological symptoms as well as the impact of these symptoms on patient QOL. 43,44 Symptom assessment and management are integral components in the practice of medicine 45 and, as such, are closely related to areas routinely analyzed by clinicians, which may increase clinicians comfort level with conducting such an assessment. 16 In contrast, the application of multidimensional QOL instruments in oncology practices and clinical trials has met with resistance In addition, despite a longstanding recommendation to include QOL evaluation in the drug approval process, 26 the FDA is confronted with the challenge of addressing the multidimensional nature and assessment of QOL and the consequent implications for claims of drug effectiveness. The FDA Oncology Drug Advisory Committee (ODAC) subcommittee on QOL has advanced the position that overall claims of QOL benefit cannot be made from one or two domain measurements and that claims made about QOL need to be specific to the domain that was measured. 46 Although an abbreviated, symptom-focused assessment, albeit clinically appealing and possibly maximally responsive, would not be considered sufficient for a broadly worded QOL regulatory claim, it would lend support to the use of more appro-

11 42 Yount et al. Vol. 24 No. 1 July 2002 priate claims, such as symptomatic relief or delay of onset of tumor-related symptoms. Routine comprehensive symptom assessment offers potential benefits to patients as well. For patients with advanced disease, where life expectancy is reduced and there is no cure, relief of physical symptoms and maintenance of function become primary objectives of medical intervention. 25,47,48 Routine assessment of symptoms may identify a significant proportion of patients who require and would benefit from intensive symptom palliation. 49 The FHSI-8 offers the additional advantage of being a very brief instrument. In studies requiring repeated assessments, it is particularly important to minimize respondent (patient) burden. To the extent that symptomatology is related to objective disease states, routine symptom assessment may yield additional benefits. Symptom improvement has been associated with objective tumor regression in metastatic breast cancer patients. 50 The assessment of physical symptoms is believed to yield prognostic information related to survival in cancer patients, possibly even accounting for the predictive value of QOL scores. 51 In addition to those with advanced cancer, symptom assessment may also be important to those believed to be free of disease. A significant proportion of patients with no existing disease continue to experience severe symptoms. 49 Further, as the oncology field moves to a unified set of response criteria for measurable tumors (RE- CIST), it is likely that many tumors previously considered measurable will no longer be so. 52 Yet it will remain important to know whether these unmeasurable tumors are treatable. The clinical bottom line is that symptomatic response, or the delay of symptom progression, has meaning to people with cancer and their health care providers. Several limitations of this study should be acknowledged. First, clinician input was used to select the target symptoms, and no patient input was obtained. Although patients did not participate in the choice of target symptoms, they did participate, in a 3:1 ratio, in the selection of the original 26 items during development of the FACT-Hep. It remains to be seen, however, if patients would select similar or the same 8 symptoms. Second, clinicians were asked to focus on pancreatic cancer when responding to the survey. It is yet to be determined the extent to which this symptom index, which has been tested on hepatobiliary cancer patients with variety of diagnoses, will work in a narrower group of any one of the subtypes of this population. Third, this sample, presenting to an outpatient ambulatory clinic and disproportionately (half) free of active disease, is likely to be on the healthy side relative to the population of hepatobiliary cancer patients. Unfortunately, laboratory data (e.g., bilirubin) were not available on this sample to provide additional insight into their health status. However, for clinical trial purposes, where good performance status is a prerequisite for admission, this sample may be representative of the patients entering trials where this outcome measure would be employed. The validity of the FHSI in palliative and end-of-life care remains to be evaluated. This study has provided evidence that items pertaining to symptoms and concerns of advanced hepatobiliary cancer patients can be derived from a well-established multidimensional QOL questionnaire and aggregated in a clinically relevant and psychometrically acceptable manner. Although we continue to advocate for the continued assessment of QOL across a range of biopsychosocial domains, such as those assessed with the FACT measurement system, we believe the FHSI may provide an acceptable alternative when the clinical or research interest is symptom-focused. To date, the clinical relevance of the symptom index has been validated only by experts in treating these cancers. It is yet to be determined the extent to which FHSI is responsive to changes in symptom status over time. Future work will validate the FHSI-8 in a patient population as a stand-alone symptom index and determine the extent to which changes translate into meaningful improvement to the patient. Acknowledgments Supported in part by a grant from Janssen Research Foundation. References 1. Niederhuber JE. Tumors of the liver. In: Murphy GP, Lawrence W, Lenhard RE, eds. Clinical Oncology, 2nd ed. Atlanta, GA: The American Cancer Society, Inc., 1995:

12 Vol. 24 No. 1 July 2002 Symptom Assessment in Hepatobiliary Cancer Miller BA, Kolonel LN, Bernstein L, et al, eds. Racial/Ethnic Patterns of Cancer in the United States , National Cancer Institute. NIH Pub. No Bethesda, MD, Beazley RM, Cohn I. Tumors of the pancreas, gallbladder, and extrahepatic bile ducts. In: Murphy GP, Lawrence W, Lenhard RE, eds. Clinical Oncology, 2nd ed. Atlanta, GA: The American Cancer Society, Inc., 1995: Bernhard J, Hurny C. Gastrointestinal cancer. In: Holland JC, ed. Psycho-oncology. New York: Oxford University Press, 1998: Rice D, Geller A, Bender CE, et al. Surgical and interventional palliative treatment of upper gastrointestinal malignancies. Eur J Gastroenterol Hepatol 2000;1294: Chao T, Greager JA. Primary carcinoma of the gallbladder. J Surg Oncol 1991;46: Modolell I, Guarner L, Malagelada JR. Vagaries of clinical presentation of pancreatic and biliary tract cancer. Ann Oncol 1999;10: Burris HA, Moore MJ, Andersen J, et al. Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 1997;15: Glimelius B, Hoffman K, Sjoken PO, et al. Chemotherapy improves survival and quality of life in advanced pancreatic and biliary cancer. Ann Oncol 1996;7: Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: A Quality-of-Life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 1993;85: Cella DF, Tulsky DS, Gray G, et al. The Functional Assessment of Cancer Therapy (FACT) Scale: Development and validation of the general measure. J Clin Oncol 1993;11: Schag CA, Heinrich RL. Development of a comprehensive quality of life measurement tool: CARES. Oncology (Huntingt) 1990;4: Schipper H, Clinch J, McMurray A, Levitt M. Measuring the quality of life of cancer patients: the Functional Living Index Cancer: development and validation. J Clin Oncol 1984;2: Taylor KM, Macdonald KG, Bezak A, et al. Physicians perspective on quality of life: an exploratory study of oncologists. Qual Life Res 1996;5: Browman GP. Science, language, intuition, and the many meanings of quality of life. J Clin Oncol 1999;17: Guyatt G, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Int Med 1993;118: Morris J, Perez D, McNoe B. The use of quality of life data in clinical practice. Qual Life Res 1998;7: Skeel R. Quality of life dimensions that are most important to cancer patients. Oncology 1993;7: Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life: a conceptual model of patient outcomes. JAMA 1995;273: Gill TM, Feinstein AR. A critical appraisal of the quality of quality-of-life measurements. JAMA 1994; 272: Hopwood P. Progress, problems, and priorities in quality of life research. Eur J Cancer 1992; 28A: Bezjak A, Taylor KM, Ng P, et al. Quality-of-life information and clinical practice: the oncologist s perspective. Cancer Prev Control 1998;2: Deyo RA, Patrick DL. Barriers to the use of health status measures in clinical investigation, patient care, and policy research. Med Care 1989;27: S254 S Nelson EC, Berwick DM. The measurement of health status in clinical practice. Med Care 1989;27: S77 S Fish LS, Lewis BE. Quality of life issues in the management of ovarian cancer. Semin Oncol 1999; 26:S32 S Johnson JR, Temple R. Food and Drug Administration requirements for approval of new anticancer drugs. Cancer Treat Rep 1985;69: Rothermich EA, Pathak DS. References for health-related quality of life claims in prescription drug advertisements. Am J Health Syst Pharm 1997; 43: Quality of life ad claims should be specific, FDAsponsored physician focus groups say; ad division direction cautions against QOL claim expansiveness. FDC Rep. 1994;56(4): Moore MJ, Osoba D, Murphy K, et al. Use of palliative end points to evaluate the effects of mitoxantrone and low-dose prednisone in patients with hormonally resistant prostate cancer. J Clin Oncol 1994;12: Burris HA, Moore MJ, Andersen J, et al. Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 1997;15: Heffernan N, Fong Y, Jarnagin W, et al. Measuring health-related quality of life with the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT- Hep) Questionnaire. J Clin Oncol 2002;20: McNair DM, Lorr M, Droppleman LF, eds. Manual for the Profile of Mood States. San Diego, CA: Educational and Industrial Testing Service, Zubrod CG, Schneiderman M, Frei E, et al. Appraisal of methods for the study of chemotherapy of cancer in man: comparative therapeutic trial of nitrogen mustard and triethylene thiophosphoramide. J Chronic Dis 1960;11:7 33.

13 44 Yount et al. Vol. 24 No. 1 July Cella DF, Bonomi AE, Lloyd SR, et al. Reliability and validity of the Functional Assessment of Cancer Therapy Lung (FACT-L) quality of life instrument. Lung Cancer 1995;2: Brady MJ, Cella DF, Mo F, et al. Reliability and validity of the Functional Assessment of Cancer Therapy-Breast (FACT-B) quality of life instrument. J Clin Oncol 1997;15: Wright BD, Masters GN. Rating Scale Analysis. Chicago: MESA Press, Andrich D. A rating formulation for ordered response categories. Psychometrika 1978;43: Andrich D. Application of a psychometric rating model to ordered categories which are scored with successive integers. App Psychol Measurement 1978; 2: Linacre JM, Wright BD. WINSTEPS Rasch model computer program. Chicago: MESA Press; Cohen J. Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum, Linacre JM, Wright BD. A user s guide to BIG- STEPS. Chicago: MESA Press; 1993: Nunnally JC. Psychometric Theory, 2nd ed. New York: McGraw-Hill, Portenoy RK, Thaler HT, Kornblith AB, et al. Symptom prevalence, characteristics and distress in a cancer population. Qual Life Res 1994;3: Portenoy RK, Thaler HT, Kornblith AB, et al. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer 1994;30A: Ingham JM, Portenoy RK. Symptom assessment. Hematol Oncol Clin North Am 1996;10: Food And Drug Administration Oncologic Drugs Advisory Committee Quality of Life Subcommittee Meeting Transcript, 10 February Coons SJ, Kaplan RM. Assessing health-related quality of life: application to drug therapy. Clin Ther 1992;14: Sutherland HJ, Lockwood GA, Boyd NF. Ratings of the importance of quality of life variables: therapeutic implications for patients with metastatic breast cancer. J Clin Epidemiol 1990;43: Chang VT, Hwang SS, Feuerman M, Kasimis BS. Symptom and quality of life surveys of medical oncology patients at a Veterans Affairs Medical Center: a role for symptom assessment. Cancer 2000;88: Geels P, Eisenhauer E, Bezjak A, et al. Palliative effect of chemotherapy: objective tumor response is associated with symptom improvement in patients with metastatic breast cancer. J Clin Oncol 2000;18: Chang VT, Thaler HT, Polyak TA, et al. Quality of life and survival. Cancer 1998;83: Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst 2000;92: Appendix FACT Hepatobiliary Symptom Index (FHSI-8) Below is a list of statements that other people with your illness have said are important. By circling one (1) number per line, please indicate how true each statement has been for you during the past 7 days. Not at all A little bit Somewhat Quite a bit Very much GP1 I have a lack of energy GP2 I have nausea GP4 I have pain C2 I am losing weight CNS7 I have pain in my back HI7 I am fatigued Hep2 I am bothered by jaundice or yellow color to my skin Hep8 I have discomfort or pain in my stomach