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Journal of Clinical Oncology, Vol 26, No 22 (August 1), 2008: pp. 3763-3769
© 2008 American Society of Clinical Oncology.
DOI: 10.1200/JCO.2007.13.5145
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Quality of Life in Patients With Metastatic Renal Cell Carcinoma Treated With Sunitinib or Interferon Alfa: Results From a Phase III Randomized Trial

David Cella, Jim Z. Li, Joseph C. Cappelleri, Andrew Bushmakin, Claudie Charbonneau, Sindy T. Kim, Isan Chen, Robert J. Motzer

From the Evanston Northwestern Healthcare and Northwestern University Feinberg School of Medicine, Evanston, IL; Pfizer Global Research and Development, San Diego, CA; Pfizer Global Research and Development, New London, CT; Pfizer Global Research and Development; and Memorial Sloan-Kettering Cancer Center, New York, NY

Corresponding author: David Cella, PhD, Center on Outcomes, Research and Education (CORE), Evanston Northwestern Healthcare and Northwestern University Feinberg School of Medicine, 1001 University Place, Evanston, IL 60201; e-mail: d-cella{at}northwestern.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
Purpose In an international, randomized phase III trial, sunitinib demonstrated statistically significant efficacy over interferon alfa (IFN-{alpha}) as first-line therapy in patients with metastatic renal cell carcinoma (mRCC) (progression-free survival time, 11 v 5 months, respectively; P < .001; objective response rate, 31% v 6%, respectively; P < .001). We report health-related quality-of-life (QOL) results from this trial.

Patients and Methods Seven hundred fifty mRCC patients were randomly assigned to sunitinib (6-week cycles: 50 mg orally once daily for 4 weeks, followed by 2 weeks off) or IFN-{alpha} (9 million units subcutaneous injections, three times weekly). QOL measures included the Functional Assessment of Cancer Therapy–General (FACT-G), the FACT-Kidney Symptom Index–15 item (FKSI-15), and the EuroQoL-5D's utility score (EQ-5D Index) and its visual analog scale (EQ-VAS). The primary QOL end point was the FKSI Disease-Related Symptoms (FKSI-DRS) subscale. Higher scores indicated better outcomes (better QOL or fewer symptoms). Data were analyzed for the intent-to-treat population using mixed-effects models, supplemented with pattern-mixture models.

Results Patients receiving sunitinib reported higher FKSI-15 and FKSI-DRS scores at each cycle than those receiving IFN-{alpha}, with a significant difference in the overall least squares means (3.27 and 1.98, respectively; P < .0001). Similarly, differences in least squares means for FACT-G (and all subscales), EQ-5D Index, and EQ-VAS were all significantly favorable for sunitinib (P < .01). Per pre-established thresholds, between-treatment differences in the mean scores were clinically meaningful after cycle 4 for FKSI-DRS and at all assessments for FKSI-15, FACT-G, and the FACT-G functional well-being subscale.

Conclusion Sunitinib provides superior QOL compared with IFN-{alpha} in mRCC patients.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
Renal cell carcinoma (RCC) is the most common form of kidney cancer.1,2 At initial presentation, 25% to 30% of RCC patients have overt metastases.1 Five-year survival rates range from 0% to 20%.1-4

Metastatic RCC (mRCC) is difficult to treat and generally unresponsive to conventional chemotherapy. In the absence of effective treatment, many patients die within 6 to 10 months of diagnosis.5,6 Until recently, cytokines were widely used as first-line mRCC therapy.2,7 Treatment with cytokines, such as interferon alfa (IFN-{alpha}), is associated with influenza-like symptoms, including neurocognitive effects, and may have a negative effect on quality of life (QOL).2,7 Although 5% to 10% of patients experience long-term remission, most gain no clinical benefit or cannot tolerate the adverse effects.8

The introduction of targeted therapies, such as sunitinib malate (Sutent; Pfizer Inc, New York, NY), represents a significant shift in mRCC treatment. In a prospectively planned interim analysis of an international, phase III, randomized trial, treatment with sunitinib was well tolerated and resulted in significantly longer progression-free survival and higher objective response rate than IFN-{alpha} (P < .001)9; these benefits are likely to be reflected in patients’ QOL. The objective of this study was to compare the QOL between patients receiving sunitinib and those receiving IFN-{alpha}.


   PATIENTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
Eligibility and Treatments
The study population included patients aged ≥ 18 years with mRCC with a component of clear cell histology. Key eligibility criteria included the following: no previous treatment with systemic RCC therapy; measurable disease; Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1; and adequate hepatic, renal, and cardiac function. Patients were excluded if they had brain metastases, uncontrolled hypertension, or clinically significant cardiovascular events or disease during the preceding 12 months.

Eligible patients were randomly assigned to sunitinib or IFN-{alpha} in repeated 6-week cycles. Sunitinib was administered orally at a starting dose of 50 mg/d for 4 weeks followed by 2 weeks off treatment. IFN-{alpha} was administered as a subcutaneous injection on 3 nonconsecutive days per week. Patients treated with IFN-{alpha} received doses of 3 million units (MU) the first week, 6 MU the second week, and 9 MU thereafter. Dose modifications were allowed for toxicity management on both treatments. The institutional review board/ethics committee of each center approved the study. All patients gave written informed consent. Additional trial details and results have been presented elsewhere.9

QOL Assessments
QOL was measured using the following three validated patient self-reported questionnaires: the Functional Assessment of Cancer Therapy–Kidney Symptom Index–15 item (FKSI-15), the Functional Assessment of Cancer Therapy–General (FACT-G), and the EuroQol Group's EQ-5D self-report questionnaire. Patients were asked to complete the questionnaires before any clinical activities during visits to the study clinics at screening, on days 1 and 28 of each 42-day treatment cycle, and at the end of treatment or study withdrawal. Questionnaires were provided in the patient's preferred language. From these three questionnaires, nine QOL end points were derived, and the FKSI Disease-Related Symptoms (FKSI-DRS) subscale was prospectively specified as the primary QOL end point.

FKSI-15
The FKSI-15 is a validated symptom index for kidney cancer patients10 containing 15 questions, each scored on a 5-point scale (0 = not at all; 4 = very much). For this study, the following two measures were derived from this questionnaire: the FKSI-15 score and FKSI-DRS score. The FKSI-15 score ranges from 0 (most severe symptoms and concerns) to 60 (no symptoms or concerns). The FKSI-DRS is a subscale of FKSI-15 that contains nine item-measuring symptoms predominantly related to kidney cancer per se, including lack of energy, fatigue, pain, bone pain, weight loss, shortness of breath, cough, fever, and hematuria. The FKSI-DRS was validated in a separate study.11 The FKSI-DRS score ranges from 0 (all most severe symptoms) to 36 (no symptoms).

FACT-G
The validated FACT-G scale is widely used for assessing health-related QOL for different cancers.12-14 The most recent version (4.0) consists of 28 items in four domains (physical well-being [PWB], social/family well-being, emotional well-being, and functional well-being [FWB]). Test-retest reliability for the four subscales ranges from 0.82 to 0.88.14 The FACT-G is moderately to strongly correlated with mood distress (r = 0.57 to 0.69) and activity level (r = –0.56) and weakly correlated with social desirability (r = 0.22).14 The FACT-G is able to differentiate patients by performance status and disease stage (P < .0001).14

EuroQoL Instrument
The EuroQol Group's self-reported health status measure (EQ-5D) was used to assess patients’ overall health status. The following two measures from EQ-5D are used for this study: a population preference-based health state utility score (EQ-5D Index) and a patient's overall health state on a visual analog scale (EQ-VAS). Both the EQ-5D Index and EQ-VAS have been shown to be reliable and valid for assessing health-related QOL in cancer patients.15-17 The EQ-5D Index is derived by matching a patient's self-reported problems (no, moderate, or severe) in five health dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) with the population's preference for the problems. The EQ-5D Index score ranges from –0.594 to 1.000, with scores of 1, 0, or less than 0 denoting that the corresponding health state is valued by the population as equivalent to full health, death, or worse than death, respectively. The EQ-VAS is a 100-point VAS (0 = worst imaginable health state; 100 = best imaginable health state) that expresses the patient's self-perceived value for his/her health state.18

Statistical Analysis
All analyses were conducted on the intent-to-treat population. Completion was defined as a patient completing more than 80% of items in the overall FACT-G and more than 50% of items in the FKSI-DRS, FKSI-15, and FACT-G subscales. No patients with a baseline assessment and at least one postbaseline measurement were excluded from the analysis. Patients without any postbaseline assessment were excluded (ie, 24 patients [6.4%] and 37 patients [10.4%] in the sunitinib and IFN-{alpha} groups, respectively, at cycle 1, day 28).

Frequency distributions, means, and standard deviation were used to describe patient demographics and baseline characteristics. Frequencies and percentages of missing data for each instrument were compared between treatment groups. Between-group differences in the proportion of missing data (withdrawal from treatment for reasons other than documented disease progression or death) were tested using the {chi}2 test.

Repeated-measures mixed-effects models with random intercepts and slopes were used to assess treatment differences in QOL measures over time. The outcomes were QOL postbaseline scores, and the predictors were baseline QOL score, treatment, time (which was treated as a continuous variable and measured at the planned time [cycle] of assessment), and treatment x time interaction.19-21 The changes in QOL scores over time were considered to be linear over the entire range of assessments (an assumption that was retrospectively tested and satisfied). All available data for each patient were used in the analysis; thus, patient end-of-treatment assessments were included, and missing responses as a result of withdrawal were not (although patients’ previous completed responses were included). All parameter estimates were obtained using restricted maximum likelihood estimation. Estimated mean scores were calculated for all QOL measures and compared between treatments over time. Sensitivity analyses using pattern-mixture models22,23 were performed to test the robustness of results from the mixed-effects models; for these data, all parameters were identifiable and estimable. In this model, each pattern/stratum is defined by the time of last assessment. The first two cycles are combined as the first stratum, and each subsequent stratum is based on a cycle whose patients had data up until that cycle. Subsequently, in this analysis, there is a separate stratum for each potential dropout time by cycle.

Pattern-mixture models allow assumptions that missing data are missing at random to be relaxed by permitting group comparisons on available data, within subgroups of patients who drop out early. Similar results between mixed-effects and pattern-mixture models would suggest that the results are not overly dependent on the nature of the missing data. All the data were analyzed using SAS 8.2 (SAS Institute, Cary, NC), and P < .05 was considered nominally statistically significant without multiplicity adjustment. No adjustments were made for multiple comparison testing.

Average QOL scores during the study for all instruments were calculated and compared between the treatment groups. To estimate the magnitude of the difference between treatment groups, the standardized effect size (SES) and minimally important difference (MID) were used. SES is calculated as the mean difference divided by the baseline standard deviation and provides a standardized value for the size of the treatment differences. Cohen's definitions were used to characterize SES (0.20 for a small but tangible effect, 0.50 for a moderate effect, and 0.80 for a large effect).24 The MIDs in the FKSI-15, FACT-G, and their subscale scores have been established with the following lower boundaries: 2 points for FKSI-DRS, 3 points for FKSI-15, 5 points for FACT-G, and 2 points for each of the four FACT-G subscales.25,26


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
Baseline Characteristics
Seven hundred fifty patients were randomly assigned and included in the intent-to-treat analysis (Fig 1). Median age was 62 years for the sunitinib arm and 59 years for the IFN-{alpha} arm; 71% of patients were male, and more than 90% were white. There were no significant between-treatment differences in baseline characteristics (Table 1).


Figure 1
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  		Fig 1. Patient enrollment and outcomes in the intent-to-treat population. Baseline quality-of-life (QOL) data were available for 373 patients in the sunitinib group and 356 patients in the interferon alfa (IFN-{alpha}) group, of whom 349 and 319 patients, respectively, had at least one postbaseline assessment and were, therefore, included in the analysis (see Patients and Methods).

 

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  		Table 1. Patient Demographics and Baseline Characteristics

 
Questionnaire Completion Rates
The questionnaire completion rates were based on the number of patients who entered the cycles; there were fewer patients in later cycles because many had not reached them yet. The completion rates (the proportion of expected evaluations received) for the FKSI-15 and FACT-G questionnaires, through up to 10 cycles for which QOL data were available, were 95.4% and 95.2% for the sunitinib and IFN-{alpha} groups, respectively. Patient refusal was the most common reason for noncompletion.

FKSI Assessments
Patients in the sunitinib group reported higher (more favorable) FKSI-15 and FKSI-DRS scores at each cycle than those in the IFN-{alpha} group (Fig 2). Across all cycles, the overall mean difference in scores was 1.98 points for FKSI-DRS (95% CI, 1.46 to 2.51 points) and 3.27 points for FKSI-15 (95% CI, 2.36 to 4.18 points), both favoring sunitinib (Table 2). These differences were statistically significant (P < .0001). The overall SES, across all cycles, was 0.39 for the FKSI-DRS subscale (ranging from 0.34 at cycle 1, day 28 to 0.54 at cycle 11, day 28) and 0.38 for the FKSI-15 scale (ranging from 0.36 at cycle 1, day 28 to 0.46 at cycle 11, day 28), suggesting a small to moderate benefit for sunitinib compared with IFN-{alpha}.24,26 Compared with the pre-established MIDs, the treatment differences were considered clinically meaningful after cycle 4, day 1 for FKSI-DRS and at all postbaseline time points for FKSI-15.


Figure 2
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  		Fig 2. Estimated mean (A) Functional Assessment of Cancer Therapy–Kidney Symptom Index–15 item (FKSI-15) and (B) Functional Assessment of Cancer Therapy–Kidney Symptom Index Disease Related Symptoms (FKSI-DRS) scores by treatment arm. P values are for trends of treatment means within the sunitinib or interferon alfa (IFN-{alpha}) groups over the postbaseline period (ie, cycle 1, day 28 to cycle 11, day 28). The units for the estimates of the slopes are the changes in scores associated with an increase in one cycle (ie, points/cycle).

 

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  		Table 2. Average Treatment Differences for Quality-of-Life Instruments

 
Within-group changes over time for the postbaseline scores, as shown in Figure 2, indicate that there were sharp decreases in both FKSI-15 and FKSI-DRS scores after the first cycle of treatment; the initial decreases were more pronounced for the IFN-{alpha} group, exceeding the pre-established MIDs for both scores, perhaps reflecting the more toxic nature of IFN-{alpha}. After the initial decreases, the FKSI-15 and FKSI-DRS scores remained lower than baseline, except for the FKSI-DRS scores in the sunitinib group, which increased slowly but significantly over time (slope = 0.138, P = .003), suggesting that sunitinib-treated patients’ disease-related symptoms lessened over time; however, this change was only a slight or moderate improvement over baseline.

When examining the nine items in the FKSI-DRS, patients treated with sunitinib experienced significantly less severe symptoms of lack of energy, weight loss, bone pain, fatigue, breathlessness, coughing, and fever symptoms than those receiving IFN-{alpha} (Table 3).


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  		Table 3. Average Treatment Differences for FKSI-DRS Items

 
FACT-G Assessments
The mean treatment difference over all cycles was estimated to be 5.58 points significantly higher for the sunitinib group (95% CI, 3.91 to 7.24 points; P < .0001), with a small to moderate overall treatment effect (SES = 0.36, ranging from 0.35 at cycle 1, day 28 to 0.40 at cycle 11, day 28); mean treatment differences also favored sunitinib for each of the four FACT-G subscales (P ≤ .0009; Table 2). Between-treatment differences in FACT-G scores exceeded the pre-established MID at all assessment points. However, of the FACT-G subscales, only the difference in FWB subscale scores exceeded the MID after cycle 4, day 1. Small treatment effects were observed for PWB (SES = 0.27), social/family well-being (SES = 0.26), emotional well-being (SES = 0.17), and FWB (SES = 0.32) subscales.

Comparisons of the postbaseline scores with their respective baseline values indicate that there were sharp decreases in the FACT-G total score (Fig 3A) and the PWB and FWB subscale scores for the IFN-{alpha} group (decreases of 4.4, 3.0, and 1.5 points, respectively) after the first cycle; there were no initial decreases in the FACT-G total or subscale scores for the sunitinib group, except for the PWB subscale (a decrease of 2.0 points). None of the slopes for the within-group changes over time for the postbaseline FACT-G total and subscale scores were statistically significant. As a result, the between-group difference that favored sunitinib was evident at the first postbaseline assessment and persisted throughout the study, again suggesting that sunitinib was less toxic than IFN-{alpha}.


Figure 3
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  		Fig 3. Estimated mean (A) Functional Assessment of Cancer Therapy–General (FACT-G), (B) EuroQol 5-Dimension (EQ-5D), and (C) EuroQol Visual Analog Scale (EQ-VAS) scores by treatment arm. P values are for trends of treatment means within the sunitinib or interferon alfa (IFN-{alpha}) groups over the postbaseline period (ie, cycle 1, day 28 to cycle 11, day 28). The units for the estimates of the slopes are the changes in scores associated with an increase in one cycle (ie, points/cycle).

 
EuroQol Assessments
The overall postbaseline mean treatment difference for the EQ-5D Index was estimated to be 0.0364 points in favor of sunitinib (95% CI, 0.0109 to 0.0620 points; P = .0052; Table 2), although the mean difference was not statistically significant after cycle 5, day 1 (Fig 3). The overall mean treatment difference for EQ-VAS was estimated to be 4.74 points in favor of sunitinib (95% CI, 2.60 to 6.87 points; P < .0001), and mean treatment differences were found to be statistically significant at all time points (P < .01; Fig 3). The overall SES values were close to zero (0.05 for the EQ-5D and 0.07 for the EQ-VAS). Comparisons of postbaseline EQ-5D Index and EQ-VAS scores with their respective baseline values (Figs 3B and 3C, respectively) yielded similar patterns as were shown for the FACT-G total scores.

Sensitivity Analyses
Results of the pattern-mixture models were generally consistent with those of the mixed-effect models (Table 4). The estimated treatment difference for FKSI-DRS from the pattern-mixture models provided stronger evidence than mixed-effects models that sunitinib treatment resulted in milder kidney cancer–related symptoms over time (2.96, P < .0001 v 1.98, P < .0001, respectively) compared with IFN-{alpha}; similar evidence was found for FKSI-15. Although the two models showed similar trends for overall cancer-specific QOL (FACT-G), the pattern-mixture models indicated that the IFN-{alpha} group experienced greater declines in physical and emotional functioning, as well as in functional well-being, compared with mixed-effects models.


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  		Table 4. Comparison of Results from Mixed-Effects Models and Pattern Mixture Models

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
To our knowledge, this is the first head-to-head comparison of patients’ QOL between an oral tyrosine kinase inhibitor and a systemic cytokine in the first-line treatment of mRCC. Previous studies of QOL among mRCC patients were case series,27 evaluations of other therapeutic options such as surgery,28 or comparisons of immunotherapy.8,29,30 Complementing previously published interim results from this trial in which sunitinib demonstrated statistically superior efficacy over IFN-{alpha},9 these results indicate that patients on the sunitinib arm also had significantly better QOL compared with patients on the IFN-{alpha} arm. These results predominantly reflected between-group differences rather than within-group improvement from baseline with sunitinib.

Validated generic and disease-specific QOL instruments were used to measure kidney cancer–related symptoms (FKSI-15 and FKSI-DRS), cancer-specific health-related QOL (FACT-G and its subscales), and overall health status (EQ-5D Index and EQ-VAS). Longitudinal data allowed assessment of the impact of treatments over the course of the trial.

Use of FKSI-DRS (the primary QOL end point) was intended to examine the impact of sunitinib on disease-related symptom experience rather than on treatment-related adverse effects. In the context of a drug trial, symptoms are often the most proximal result of treatment and an important component of QOL for people with advanced kidney cancer; therefore, it is important to isolate the symptom domain from other domains within the QOL concept.11 Patients receiving sunitinib reported better scores on FKSI-DRS, indicating that they experienced fewer severe disease-specific symptoms than patients treated with IFN-{alpha}.

However, despite use of the FKSI-DRS, it remains difficult to clearly distinguish treatment effects related to a drug's adverse effect profile from those effects related to its capacity to relieve disease-related symptoms; thus, further analysis via use of a mediation model is warranted. The development of such a model to better clarify these findings is currently being explored by the authors.

The advantage of sunitinib was also reflected in the results for the secondary QOL end points (FKSI-15, FACT-G, EQ-5D Index, and EQ-VAS). FKSI-15 and the FACT-G both exhibited clinically meaningful differences at all assessments. Consistent with the other findings, EQ-5D and EQ-VAS scores were superior for patients on sunitinib compared with patients on IFN-{alpha}. These findings indicate that not only does society place a higher value on the health states experienced during treatment with sunitinib, compared with IFN-{alpha}, but also that patients perceive that their health states are superior with sunitinib treatment.

Patients were aware of their assigned therapy, opening the possibility that the QOL benefit of sunitinib (the active treatment) may have been overestimated. However, this potential concern is mitigated by having patients assess their current status noncomparatively, and doing so on many questions, rather than retrospectively, compared with baseline. Therefore, the instruments used here are harder to answer in a biased way. Moreover, the consistency of the postbaseline responses across time within treatment group provides evidence for the validity of the QOL benefit of sunitinib over IFN-{alpha}.

This study enrolled patients from different countries, and we have not adjusted for potential cultural differences.31 Other factors, such as social support, may affect QOL in cancer patients as well.32 Although these factors merit exploration, they are unlikely to have had a significant impact because of the randomized study design.

As a result of the efficacy difference,9 there was an increasing between-treatment difference in the proportions of patients remaining in the study over time. Use of the repeated-measures mixed-effects models reduced the potential for bias resulting from the uneven dropouts by using all available assessments. Results from the pattern-mixture models supported and enhanced the validity of these findings. However, this model has a limitation because its assumption, that trajectories for patient outcome within each stratum can be extrapolated past the point of dropout, cannot be tested because of missing data; also, if a stratum is comprised of censored patients and dropouts, the latter's true impact will be unclear. In summary, this large, international, randomized trial showed that, in addition to providing significantly superior efficacy compared with IFN-{alpha}, sunitinib offers patients with advanced RCC a superior QOL, including better kidney cancer disease-related symptoms.


   AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
AUTHOR CONTRIBUTIONS
 REFERENCES
 
Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "U" are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: Jim Z. Li, Pfizer Inc (C); Joseph C. Cappelleri, Pfizer Inc (C); Andrew Bushmakin, Pfizer Inc (C); Claudie Charbonneau, Pfizer Inc (C); Sindy T. Kim, Pfizer Inc (C); Isan Chen, Pfizer Inc (C) Consultant or Advisory Role: David Cella, Pfizer Inc (C) Stock Ownership: Jim Z. Li, Pfizer Inc; Joseph C. Cappelleri, Pfizer Inc; Andrew Bushmakin, Pfizer Inc; Sindy T. Kim, Pfizer Inc; Isan Chen, Pfizer Inc Honoraria: None Research Funding: David Cella, Pfizer Inc; Robert J. Motzer, Pfizer Inc Expert Testimony: None Other Remuneration: None


   AUTHOR CONTRIBUTIONS
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
REFERENCES
 
Conception and design: Jim Z. Li, Sindy T. Kim, Isan Chen, Robert J. Motzer

Administrative support: Sindy T. Kim

Provision of study materials or patients: Sindy T. Kim, Robert J. Motzer

Collection and assembly of data: Sindy T. Kim, Isan Chen

Data analysis and interpretation: David Cella, Jim Z. Li, Joseph C. Cappelleri, Andrew Bushmakin, Claudie Charbonneau, Sindy T. Kim, Isan Chen

Manuscript writing: David Cella, Jim Z. Li, Joseph C. Cappelleri, Andrew Bushmakin, Claudie Charbonneau, Isan Chen

Final approval of manuscript: David Cella, Joseph C. Cappelleri, Sindy T. Kim, Isan Chen, Robert J. Motzer


   ACKNOWLEDGMENTS
 
We thank the patients and their families for participation in this study. Editorial assistance was provided by ACUMED (Tytherington, United Kingdom) with funding by Pfizer Inc.


   NOTES
 
Supported by Pfizer Inc.

Presented in part at the 42nd Annual Meeting of the American Society of Clinical Oncology, June 2-6, 2006, Atlanta, GA, and the 5th International Kidney Cancer Symposium, September 22-23, 2006, Chicago, IL.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Clinical Trials repository link available on www.JCO.org.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 REFERENCES
 
1. Mickisch G, Carballido J, Hellsten S, et al: Guidelines on renal cell cancer. Eur Urol 40:252-255, 2001[CrossRef][Medline]

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9. Motzer RJ, Hutson TE, Tomczak P, et al: Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med 356:115-124, 2007[Abstract/Free Full Text]

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13. Cella D, Hernandez L, Bonomi AE, et al: Spanish language translation and initial validation of the functional assessment of cancer therapy quality-of-life instrument. Med Care 36:1407-1418, 1998[CrossRef][Medline]

14. Cella DF, Tulsky DS, Gray G, et al: The Functional Assessment of Cancer Therapy scale: Development and validation of the general measure. J Clin Oncol 11:570-579, 1993[Abstract/Free Full Text]

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Submitted July 21, 2007; accepted April 15, 2008.


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