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The relationship between the Multiple Sclerosis Functional Composite (MSFC) and Expanded Disability Status Scale (EDSS) scores, ± SD. The Spearman correlation coefficient (–0.80) indicates a strong correlation between the 2 measures. The dash at 4.5 indicates that there were no subjects with this EDSS score.

The relationship between the Multiple Sclerosis Functional Composite (MSFC) and Expanded Disability Status Scale (EDSS) scores, ± SD. The Spearman correlation coefficient (–0.80) indicates a strong correlation between the 2 measures. The dash at 4.5 indicates that there were no subjects with this EDSS score.

Table 1. 
Summary of Measures Used in the Study and Predicted Relationship With MSFC Scores*
Summary of Measures Used in the Study and Predicted Relationship With MSFC Scores*
Table 2. 
Demographic Characteristics of the Study Population
Demographic Characteristics of the Study Population
Table 3. 
Health-Related Qualify-of-Life Scores for the Study Population*
Health-Related Qualify-of-Life Scores for the Study Population*
Table 4. 
Correlations Between HRQoL Measures and MSFC Scores and Between HRQoL Measures and EDSS Scores*
Correlations Between HRQoL Measures and MSFC Scores and Between HRQoL Measures and EDSS Scores*
Table 5. 
Correlations Between HRQoL Measures and MSFC Scores by Disease Severity*
Correlations Between HRQoL Measures and MSFC Scores by Disease Severity*
1.
Rudick  RAntel  JConfavreux  C  et al.  Recommendations from the National Multiple Sclerosis Society Clinical Outcomes Assessment Task Force. Ann Neurol. 1997;42379- 382Article
2.
Whitaker  JNMcFarland  HFRudge  PReingold  S Outcomes assessment in multiple sclerosis clinical trials: a critical analysis. Mult Scler. 1995;137- 47
3.
Cutter  GRBaier  MLRudick  RA  et al.  Development of a Multiple Sclerosis Functional Composite as a clinical trial outcome measure. Brain. 1999;122871- 882Article
4.
Goodkin  DEHertsgard  DSeminary  J Upper extremity function in multiple sclerosis: improving assessment sensitivity with box-and-block and nine-hole peg tests. Arch Phys Med Rehabil. 1988;69850- 854
5.
Gronwall  DM Paced auditory serial-addition task: a measure of recovery from concussion. Percept Mot Skills. 1977;44367- 373Article
6.
Fischer  JSJak  AJKnicker  JERudick  RA Administration and Scoring Manual for the Multiple Sclerosis Functional Composite Measure (MSFC).  New York, NY National Multiple Sclerosis Society1999;
7.
Schipper  HClinch  JJOlweny  CLM Quality of life studies: definitions and conceptual issues. Spilker  Bed.Quality of Life and Pharmacoeconomics in Clinical Trials 2nd ed. Philadelphia, Pa Lippincott-Raven Publishers1996;11- 24
8.
Beitz  JGnecco  CJustice  R Quality-of-life end points in cancer clinical trials: the U.S. Food and Drug Administration perspective. J Natl Cancer Inst Monogr. 1996;207- 9
9.
Wiklund  I Quality of life and regulatory issues. Scand J Gastroenterol Suppl. 1996;22137- 38Article
10.
Turner  S Economic and quality of life outcomes in oncology: the regulatory perspective. Oncology (Huntingt). 1995;9(suppl 11)121- 125
11.
Fischer  JSLaRocca  NGMiller  DMRitvo  PGAndrews  HPaty  D Recent developments in the assessment of quality of life in multiple sclerosis (MS). Mult Scler. 1999;5251- 259Article
12.
Ritvo  PGFischer  JSMiller  DMAndrews  HPaty  DLaRocca  NG Multiple Sclerosis Quality of Life Inventory: A User's Manual.  New York, NY National Multiple Sclerosis Society1997;
13.
Guyatt  GHJaeschke  RFeeny  DHPatrick  DL Measurements in clinical trials: choosing the right approach. Spilker  Bed.Quality of Life and Pharmacoeconomics in Clinical Trials 2nd ed. Philadelphia, Pa Lippincott-Raven Publishers1996;41- 48
14.
Rothwell  PMMcDowell  ZWong  CKDorman  PJ Doctors and patients don't agree: cross sectional study of patients' and doctors' perceptions and assessments of disability in multiple sclerosis. BMJ. 1997;3141580- 1583Article
15.
Vickrey  BGHays  RDHarooni  RMyers  LWEllison  GW A health-related quality of life measure for multiple sclerosis. Qual Life Res. 1995;4187- 206Article
16.
Cella  DFDineen  KArnason  B  et al.  Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology. 1996;47129- 139Article
17.
Bergner  MBobbitt  RAKressel  SPollard  WEGilson  BSMorris  JR The Sickness Impact Profile: conceptual formulation and methodology for the development of a health status measure. Int J Health Serv. 1976;6393- 415Article
18.
Damiano  AM The Sickness Impact Profile User's Manual and Interpretation Guide.  Baltimore, Md Johns Hopkins University Department of Health Policy and Management1996;
19.
Ware  JESnow  KKKosinski  MGandek  B SF-36 Health Survey: Users Manual and Interpretation Guide.  Boston, Mass The Health Institute, New England Medical Center1993;
20.
Fisk  JDRitvo  PGRoss  LHaase  DAMarrie  TJSchlech  WF Measuring the functional impact of fatigue: initial validation of the Fatigue Impact Scale. Clin Infect Dis. 1994;18(suppl 1)S79- S83Article
21.
Sherbourne  CDStewart  AL The MOS Social Support Survey. Soc Sci Med. 1991;32705- 714Article
22.
Fischer  JSRudick  RACutter  GRReingold  SC The Multiple Sclerosis Functional Composite measure (MSFC): an integrated approach to MS clinical outcomes assessment: National MS Society Clinical Outcomes Assessment Task Force. Mult Scler. 1999;5244- 250Article
Original Contribution
September 2000

Clinical Significance of the Multiple Sclerosis Functional CompositeRelationship to Patient-Reported Quality of Life

Author Affiliations

From the Mellen Center, Cleveland Clinic Foundation, Cleveland, Ohio (Drs Miller, Rudick, and Fischer); and AMC Cancer Research Center, Denver, Colo (Dr Cutter and Ms Baier).

Arch Neurol. 2000;57(9):1319-1324. doi:10.1001/archneur.57.9.1319
Abstract

Background  The Multiple Sclerosis Functional Composite (MSFC) was recommended by a task force of the National Multiple Sclerosis Society as a new clinical outcome measure for clinical trials. The task force recommended that the MSFC be validated against other measures of the disease, such as patient-reported quality of life.

Methods  Three hundred patients with multiple sclerosis (MS) representing the spectrum of disease severity were included in this cross-sectional study. The MSFC and Kurtzke Expanded Disability Status Scale (EDSS) were used as measures of disease severity. Clinical relevance of the disease severity scores was analyzed using measures included in the Multiple Sclerosis Quality of Life Inventory. The MSFC and EDSS scores were correlated with self-reported employment status, the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and the Sickness Impact Profile (SIP).

Results  The MSFC and EDSS scores were strongly correlated (r = −0.80, P<.001). The MSFC scores were correlated with patient-reported physical functioning (SIP Physical Summary Scale: r = −0.71, P<.001; SF-36 Physical Component Score: r = −0.41, P<.001). The MSFC scores were significantly but more weakly correlated with emotional functioning (SIP Psychosocial Summary Scale: r = −0.34, P<.001). After controlling for EDSS scores, there were significant residual correlations between the MSFC scores and measures of health-related quality of life, suggesting that the MSFC accounts for the variability in health-related quality of life measures not reflected by the EDSS.

Conclusions  The observed strong correlations between MSFC scores and validated measures of self-reported quality of life indicate that the MSFC scores are clinically relevant. This study supports a recommendation by the National Multiple Sclerosis Society Task Force to use the MSFC as a clinical outcome measure.

THE MULTIPLE Sclerosis Functional Composite (MSFC) is a multidimensional composite measure for multiple sclerosis (MS) clinical trials that was recommended by the National Multiple Sclerosis Society (NMSS) Clinical Outcomes Assessment Task Force.1 The MSFC was developed in response to the following consensus statement issued by participants in an international workshop on outcomes assessment:

There is a clear need for development of new assessment systems, probably based upon the best aspects of the EDSS scales (Kurtzke Expanded Disability Status Scale). Any new system must be multidimensional and quantitative. Preferentially, its scoring should be automated to speed the process and improve consistency from assessment to assessment, between raters and among centers. It should have adequate evaluation of cognition for which there are many validated, though not currently practical, systems.2

The Task Force that recommended the MSFC included 16 members from 5 countries who have expertise in neurology, psychology, neuropsychology, biostatistics, epidemiology, and drug development. They followed an explicit empirical process that was based on the results of an analysis of a pooled data set consisting of placebo groups in controlled clinical trials and selected natural history studies.1,3 Based on the results of these analyses the task force recommended a 3-part composite consisting of the following: (1) timed 25-ft walk; (2) Nine-Hole Peg Test (9-HPT)4; and (3) Paced Auditory Serial Addition test 3-minute version (PASAT-3).5 The task force recommended that the scores from these tests be combined into a single composite score by transforming the results from individual tests to z scores and then averaging the individual z scores to obtain an overall score. In addition, the task force found the following: (1) There was a strong correlation between EDSS and MSFC scores in the pooled data set. (2) Individual component scores from the MSFC (eg, timed 25-ft walk and 9-HPT) were only moderately correlated with each other, suggesting that they represented partially independent dimensions of the MS process. (3) Change in MSFC scores was correlated in the expected direction with change in EDSS scores during a 1-year period. (4) Worsening in MSFC scores during the first year of observation predicted worsening of EDSS scores in the subsequent year for those patients who had stable EDSS scores during the first year.

The MSFC was recommended for use as a clinical outcome measure for MS trials. Observation 4 above was used to support the predictive validity of the MSFC, at least in terms of subsequent changes in EDSS scores. This was considered important in shortening the duration or decreasing the sample size of future MS studies. Because of the potential precision of quantitative measures, feasibility of expressing the results as a single score using a continuous scale, and apparent predictive validity, the MSFC was recommended as an outcome measure for future MS clinical trials. A scoring manual has been published.6 A number of current controlled clinical trials have incorporated the MSFC as a clinical outcome measure.6

For conditions such as MS that do not affect mortality but do produce morbidity, one important goal of treatment is to reduce the impact of the disease on patients' lives and to assure that interventions do not cause more overall harm than good. Health-related quality of life (HRQoL) assessment " . . . in clinical medicine represents the functional effect of an illness and its consequent therapy upon a patient as perceived by the patient."7 Such patient-derived data are gaining increasing acceptance as an important assessment domain, commonly known as HRQoL, and a number of regulatory bodies responsible for approval of new interventions rely on HRQoL in their deliberations.810

From 1994 to 1997, a period overlapping the development of the MSFC (1992-1995), an independent research group sponsored by the Consortium of Multiple Sclerosis Centers and funded by the NMSS developed an MS-specific HRQoL measure, the Multiple Sclerosis Quality of Life Inventory (MSQLI). The battery was developed to assess patient perceptions of response to treatment in clinical trials. Development of the MSQLI involved analysis of generic and disease-specific measures in relation to manifestations of the MS disease process.11 The measure has undergone extensive validity and reliability testing12 and is being used in a variety of clinical studies (R. Philip Kinkel, MD, personal communication, April 2, 2000; and Nicholas G. LaRocca, PhD, personal communication, February 2, 2000).

Although the NMSS Clinical Outcomes Assessment Task Force1 had not yet presented its recommendations, the component measures that were ultimately included in the MSCF were included as measures of disease severity in the MSQLI development work. The inclusion of these measures now permits a secondary analysis of the data to evaluate the relationship between MSFC scores and MSQLI scores.

It is generally accepted that HRQoL data are distinct from objective measures of disease severity.1214 However, significant correlations have been demonstrated between clinical and HRQoL data. The authors of at least 3 MS-specific HRQoL measures12,15,16 established validity of their measures by demonstrating that the instruments correlate in the anticipated manner with objective measures of disability. In the present study, the clinical significance of the MSFC was determined by analyzing its relationship with the MSQLI.

SUBJECTS AND METHODS
STUDY DESIGN

This is a secondary analysis of a prospective, multicenter, cross-sectional study that is intended to assess the relationship of the MSFC with HRQoL. In the original study, 300 subjects from 4 sites in the United States and Canada completed generic and disease-specific HRQoL measures for a cross-sectional study devised to develop a patient-based battery.12 Other data collected for this protocol included measures of physical and cognitive impairment. Among these were the 9-HPT,4 PASAT-3,5 and timed 25-foot walk, which make up the MSFC. Instructions for conducting and scoring the MSFC have been published elsewhere.6 For the MSFC validation described in this article, hypotheses regarding relationships between the MSFC and HRQoL measures were generated before data analysis was initiated.

SUBJECTS

Three hundred patients with clinically definite MS from 4 clinical sites were entered into the study. The sites included the Mellen Center (Cleveland, Ohio), St Agnes Hospital (White Plains, NY), Dalhousie University (Halifax, Nova Scotia), and St Michael's Hospital (Toronto, Ontario). The study used a stratified sampling plan based on disease severity and sex. The sex ratio was 2 females to 1 male to reflect the distribution in the general MS population. Additionally, subjects were selected to provide an even representation of mild (EDSS score, 0-3.0 inclusive), moderate (EDSS score, 3.5-6.5 inclusive), and severe (EDSS score, 7.0-8.5) neurologic impairment.

MEASURES AND RESEARCH QUESTIONS

The EDSS and timed 25-foot walk test were administered by neurologists at each of the 4 clinical sites. Neuropsychology technicians administered the 9-HPT and PASAT-3 and assisted subjects who were unable to independently complete the self-reported HRQoL measures.

The HRQoL measures were selected for this study from a larger set of HRQoL measures included in the original MSQLI project prior to data analysis. Table 1 provides details on these measures and their predicted relation to the MSFC.

The 2 generic HRQoL measures included in the MSQLI were the Sickness Impact Profile (SIP) 17,18 and the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36).19 The Medical Outcomes Study–Social Support Study (MOS-SSS)21 was included as a measure of perceived social support. The Fatigue Impact Scale (FIS)20 was included as a disease-specific measure. Self-reported employment status was included.

The following HRQoL subscales were selected to assess general physical and psychosocial impact of health on well-being: (1) total score of the SIP (SIP Total); (2) Physical Summary Scale of the SIP (SIP Physical); (3) Psychosocial Summary Scale of the SIP (SIP Psychosocial); (4) Physical Component Score of the SF-36 (SF-36 PCS); and (5) Mental Component Score of the SF-36 (SF-36 MCS).

The total score of the FIS (FIS Total) and the physical subscale score of the FIS (FIS Physical) were included because fatigue is one of the most common and most incapacitating symptoms of MS. Because this measure and its subscales assess only the impact of fatigue, which is not assessed by the MSFC, it was anticipated that only weak correlations with the MSFC would be demonstrated. Employment status was included as a surrogate measure of health status impact and was expected to be moderately correlated with the MSFC. The Tangible Support subscale of the MOS-SSS (SSS-Tan) was included as an indicator of the level of personal assistance required to compensate for increasing disability. Because this social support measure would be relevant only for the most severely disabled patients, its correlation with MSFC was expected to be weak.

ANALYSIS PLAN

Using the means and SDs from the baseline visit for all patients in the study (internal standards), MSFC z scores were calculated as recommended in the Administration and Scoring Manual for the Multiple Sclerosis Functional Composite Measure,6 using the following formula3:

Spearman rank correlations between MSFC scores and MSQLI measures were constructed to test the strength of the predicted relationships. Because it was anticipated that the magnitude of the correlations between the MSFC and EDSS measures would be large, a second step in the analysis plan involved calculation of partial correlations between the MSFC and HRQoL measures, controlling for EDSS. Finally, a comparison of MSFC-HRQoL correlations was planned to assess the relative performance of the MSFC and HRQoL measures for the total study group and for 3 subgroups based on severity of neurological impairment.

RESULTS

All 300 subjects entered into the MSQLI project were included in this study. Descriptive statistics on the total sample and the 3 strata are provided in Table 2 and Table 3. The mean EDSS score was 4.61, mean age was 44.7 years, 66% of the patients were female, 93% were white, and 62% were married. One hundred thirteen patients (37.7%) scored 0 to 3.5 on the EDSS, 131 (43.7%) scored 3.5 to 6.5, and 56 (18.7%) scored 7.0 to 8.5.

For patients in this study, the mean SIP Total score was 23.3, the mean SIP Physical score was 22.6, and the mean SIP Psychosocial score was 20.6. Subjects also demonstrated poor quality of life (QoL) on the SF-36. The mean SF-36 PCS score (34.7) was below the 25th percentile of the general population, and the mean SF-36 MCS score (47.9) was between the 25th and 50th percentiles of the general population.

As expected, employment declined with MS severity as measured by the EDSS. Sixty-one percent of those in the total group were unemployed, ranging from 37.5% of the patients in the lowest disability group to 85.7% of the patients in the highest disability group. Mean ± SD age was 44.5 ± 9.3 years (range, 23-65 years). Perceived need for tangible social support increased as expected in the highest disability group. Fatigue severity was significant in the population but did not differ among the 3 EDSS subgroups.

Figure 1 shows the relationship between the MSFC and EDSS scores. The Spearman correlation coefficient (–0.80) indicates a strong correlation between the 2 measures. Thus, EDSS scores accounted for 64% of the variance in MSFC scores. As EDSS scores increased, there was a progressive decrease in the MSFC scores, indicating poorer performance on the functional measures within the MSFC. As indicated in Figure 1, MSFC scores varied somewhat at each EDSS level, indicating that there is information within the MSFC scores that is not accounted for by the EDSS score.

Table 4 shows the correlations between MSFC scores and HRQoL measures as well as correlations between HRQoL measures and EDSS scores. For all but 2 of the HRQoL measures, correlations with MSFC scores were statistically significant in the predicted direction. The MSFC correlations were stronger with the SIP than with the SF-36. This may be explained by a greater number of items in the SIP, which provides a more detailed assessment, or by greater focus on physical functioning in the SIP compared with the SF-36. While the correlations between the MSFC and HRQoL are similar to the correlations between the EDSS and HRQoL, examination of the partial correlations of the MSFC and HRQoL, which control for the correlation between the MSFC and EDSS, indicates that the MSFC is unique in its correlations with the HRQoL.

Correlations between the MSFC and SIP Psychosocial scores and between the MSFC score and employment status were stronger than predicted. Because these scales reflect the impact of disease on ability to perform routine daily activities, it appears that the MSFC measures dimensions of the disease with relevance for daily functioning.

There was not a statistically significant difference between the SF-36 MCS and MSFC scores or between the perceived need for tangible social support and MSFC score (P>.05)—in the latter case, presumably because the SSS-Tan scores increased only in the subgroup with the highest EDSS scores. Also, there were only weak correlations between the MSFC and fatigue scores, probably because fatigue is not measured as a component of the MSFC.

Table 4 also shows a correlation analysis controlling for the EDSS. There were significant residual correlations between the MSFC and 5 of the MS QoL measures included in the study. In particular, there were highly significant residual correlations with the SIP total scores. This indicates that the MSFC explains variance in the SIP total score not accounted for by the EDSS scores.

Table 5 shows correlations between the MSFC and the MS QoL measures for each of the 3 disability severity subgroups. Generally, correlations were larger with smaller P values in the 2 subgroups with less severe disability compared with the subgroup with the most severe disability. As with the whole group analysis, correlations were strongest with SIP scores and employment status. Interestingly, small but statistically significant correlations were observed between the MSFC and fatigue scores in the subgroup with the least disability.

The lack of correlation between MSFC scores and employment status was expected in the subgroup with the most disability, as there were very few employed patients in this subgroup. However, we found that the correlation between MSFC scores and employment status was stronger in the group with midrange disability compared with the group with lower disability. This finding is unexplained.

COMMENT

In its published recommendations, the NMSS task force called for further prospectively designed studies of the MSFC to explore its significance and to quantify its value as a clinical trial outcome measure. The results of this cross-sectional comparison of the MSFC with HRQoL and the EDSS support the clinical relevance of the MSFC.

Significant correlations between MSFC scores and MS QoL measures for the total study population indicate that the MSFC reflects the severity of MS as perceived by patients. The MSFC scores best reflect general physical well-being, as indicated by strong correlations with the SIP physical scores. However, the study also demonstrated significant correlations with measures of psychosocial functioning, suggesting that MSFC scores capture important psychosocial consequences of physical impairments. Finally, the study demonstrated moderate correlations between MSFC scores and employment status, indicating that the MSFC assesses dimensions relevant to everyday functioning.

Results of this study could direct future improvements in the MSFC measurement method in several ways. As an example, lack of correlation with a disease-specific measure of fatigue used in this study suggests that including a fatigue measure in the MSFC might improve overall correlation with disease as perceived by the patients. Furthermore, correlations between MSFC and HRQoL measures were lower in the high-disability subgroup. This may be explained by inability of the patients in this subgroup to perform the walking task that is included in the MSFC. Each patient in this subgroup was assigned a constant severe score for the walking component of the MSFC, which had the effect of truncating biological variability relating to leg function in this subgroup. This may have accounted for the lower correlations with HRQoL measures. Adding an informative measure of lower-extremity function for nonambulatory patients would probably improve correlations with HRQoL measures in patients with severe impairment. Finally, as noted by the task force, it may be useful to include a visual score.1,3

A limitation of this study is the lack of a universally accepted criterion standard for MS disease severity. In this study, the Kurtzke EDSS was used because it is a widely accepted disease severity measure. However, the EDSS has been criticized for being imprecise, using an ordinal scoring system, being insensitive to change over time, and not reflecting important components of the disease, such as arm and cognitive function. However, in the absence of a true criterion standard, the EDSS remains a reasonable comparison measure. Similarly, even though HRQoL measures are useful—their relevance to the patient is self-evident— they do not serve as a criterion standard. Therefore, this study does not resolve the question of the relative value of the MSFC scores compared with other disease measures in actually quantifying disease severity. Rather, it demonstrates significant correlations between MS QoL measures and the MSFC, and it shows significant residual correlations between MS QoL measures and the MSFC after controlling for EDSS scores. These findings demonstrate the clinical relevance of MSFC scores across the range of MS disease severity. In this regard, the study strongly supports the use of the MSFC as a clinical trials outcome measure because of the robust correlations with physician-derived and patient-derived comparison measures. Further validation of the MSFC using longitudinal methods will clarify the significance of MSFC changes and determine its utility as a primary outcome measure in clinical trials.

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Article Information

Accepted for publication January 28, 2000.

Corresponding author: Deborah M. Miller, PhD, Mellen Center for Multiple Sclerosis Treatment and Research, U-10, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195.

References
1.
Rudick  RAntel  JConfavreux  C  et al.  Recommendations from the National Multiple Sclerosis Society Clinical Outcomes Assessment Task Force. Ann Neurol. 1997;42379- 382Article
2.
Whitaker  JNMcFarland  HFRudge  PReingold  S Outcomes assessment in multiple sclerosis clinical trials: a critical analysis. Mult Scler. 1995;137- 47
3.
Cutter  GRBaier  MLRudick  RA  et al.  Development of a Multiple Sclerosis Functional Composite as a clinical trial outcome measure. Brain. 1999;122871- 882Article
4.
Goodkin  DEHertsgard  DSeminary  J Upper extremity function in multiple sclerosis: improving assessment sensitivity with box-and-block and nine-hole peg tests. Arch Phys Med Rehabil. 1988;69850- 854
5.
Gronwall  DM Paced auditory serial-addition task: a measure of recovery from concussion. Percept Mot Skills. 1977;44367- 373Article
6.
Fischer  JSJak  AJKnicker  JERudick  RA Administration and Scoring Manual for the Multiple Sclerosis Functional Composite Measure (MSFC).  New York, NY National Multiple Sclerosis Society1999;
7.
Schipper  HClinch  JJOlweny  CLM Quality of life studies: definitions and conceptual issues. Spilker  Bed.Quality of Life and Pharmacoeconomics in Clinical Trials 2nd ed. Philadelphia, Pa Lippincott-Raven Publishers1996;11- 24
8.
Beitz  JGnecco  CJustice  R Quality-of-life end points in cancer clinical trials: the U.S. Food and Drug Administration perspective. J Natl Cancer Inst Monogr. 1996;207- 9
9.
Wiklund  I Quality of life and regulatory issues. Scand J Gastroenterol Suppl. 1996;22137- 38Article
10.
Turner  S Economic and quality of life outcomes in oncology: the regulatory perspective. Oncology (Huntingt). 1995;9(suppl 11)121- 125
11.
Fischer  JSLaRocca  NGMiller  DMRitvo  PGAndrews  HPaty  D Recent developments in the assessment of quality of life in multiple sclerosis (MS). Mult Scler. 1999;5251- 259Article
12.
Ritvo  PGFischer  JSMiller  DMAndrews  HPaty  DLaRocca  NG Multiple Sclerosis Quality of Life Inventory: A User's Manual.  New York, NY National Multiple Sclerosis Society1997;
13.
Guyatt  GHJaeschke  RFeeny  DHPatrick  DL Measurements in clinical trials: choosing the right approach. Spilker  Bed.Quality of Life and Pharmacoeconomics in Clinical Trials 2nd ed. Philadelphia, Pa Lippincott-Raven Publishers1996;41- 48
14.
Rothwell  PMMcDowell  ZWong  CKDorman  PJ Doctors and patients don't agree: cross sectional study of patients' and doctors' perceptions and assessments of disability in multiple sclerosis. BMJ. 1997;3141580- 1583Article
15.
Vickrey  BGHays  RDHarooni  RMyers  LWEllison  GW A health-related quality of life measure for multiple sclerosis. Qual Life Res. 1995;4187- 206Article
16.
Cella  DFDineen  KArnason  B  et al.  Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology. 1996;47129- 139Article
17.
Bergner  MBobbitt  RAKressel  SPollard  WEGilson  BSMorris  JR The Sickness Impact Profile: conceptual formulation and methodology for the development of a health status measure. Int J Health Serv. 1976;6393- 415Article
18.
Damiano  AM The Sickness Impact Profile User's Manual and Interpretation Guide.  Baltimore, Md Johns Hopkins University Department of Health Policy and Management1996;
19.
Ware  JESnow  KKKosinski  MGandek  B SF-36 Health Survey: Users Manual and Interpretation Guide.  Boston, Mass The Health Institute, New England Medical Center1993;
20.
Fisk  JDRitvo  PGRoss  LHaase  DAMarrie  TJSchlech  WF Measuring the functional impact of fatigue: initial validation of the Fatigue Impact Scale. Clin Infect Dis. 1994;18(suppl 1)S79- S83Article
21.
Sherbourne  CDStewart  AL The MOS Social Support Survey. Soc Sci Med. 1991;32705- 714Article
22.
Fischer  JSRudick  RACutter  GRReingold  SC The Multiple Sclerosis Functional Composite measure (MSFC): an integrated approach to MS clinical outcomes assessment: National MS Society Clinical Outcomes Assessment Task Force. Mult Scler. 1999;5244- 250Article
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