Reliability, validity, and responsiveness of general and disease-specific quality of life measures in a clinical trial for cytomegalovirus retinitis1

Journal of Clinical Epidemiology 54 (2001) 376–386

Reliability, validity, and responsiveness of general and disease-specific quality of life measures in a clinical trial for cytomegalovirus retinitis Barbara K. Martina, Adele M. Kaplan Gilpina, Douglas A. Jabsb,c,d,†, Albert W. Wua,b,c,* for the Studies of Ocular Complications of AIDS Research Group1 a Department of Epidemiology, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, MD, 21205, USA Department of Health Policy and Management, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, MD, 21205, USA c Department of Medicine, School of Medicine, The Johns Hopkins University, Baltimore, MD, 21205, USA d Department of Ophthalmology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA Received 7 September 1999; received in revised form 16 May 2000; accepted 15 June 2000

b

Abstract The objective of this study was to evaluate a questionnaire for assessing general and disease-specific quality of life among people with cytomegalovirus (CMV) retinitis. Cross-sectional and longitudinal analyses of data from 279 people enrolled in the CMV Retinitis Retreatment Trial were used. At baseline, Cronbach’s alpha and multitrait analysis were used to assess internal consistency and discriminant construct validity for scales of general health, vision, and treatment impact. Associations of scales with clinical measures of health and vision were assessed at baseline with Pearson correlations and t tests, and over time with generalized estimating equations regression. Internal consistency coefficients ranged from .68 to .88. Criteria for discriminant validity were fulfilled for most scales; however, the general health perceptions and energy scales were highly correlated. Scales were moderately correlated with clinical measures at baseline. Over time, scale scores were associated with Karnofsky scores and clinical measures of CMV retinitis and vision. General and CMV retinitisspecific quality of life measures appear reliable, valid, and responsive. © 2001 Elsevier Science Inc. All rights reserved. Keywords: Acquired immune deficiency syndrome; Cytomegalovirus retinitis; Quality of life; Questionnaires; Randomized clinical trial; Vision

1. Introduction Cytomegalovirus (CMV) retinitis is a late-stage complication of the acquired immune deficiency syndrome (AIDS). CMV retinitis results in progressive loss of vision leading to blindness if untreated. CMV is the most common ocular opportunistic infection in AIDS, estimated in the mid-1990s to affect about 30% of patients [1,2]. Although the incidence of CMV retinitis has decreased dramatically since then [3] due to the use of combinations of highly active antiretroviral drugs, this disease may remain a common complication for people who do not respond well to treat-

* Corresponding author. Health Services Research and Development Center, The Johns Hopkins University School of Hygiene and Public Health, 624 North Broadway, Room 633, Baltimore, MD 21205. Tel.: 410955-6567; fax: 410-955-0470. E-mail address: [email protected] (A.W. Wu) † Source of reprints. The Wilmer Ophthalmological Institute, The Johns Hopkins University School of Medicine, 550 North Broadway, Suite 700, Baltimore, MD 21205. Tel.: 410-955-1966; fax: 410-955-0629. 1 Members of the Studies of Ocular Complications of AIDS Research Group are listed in Appendix A.

ment due to viral drug resistance or to difficulties with tolerating or adhering to the treatment regimen. Although clinical measures of CMV retinitis and vision generally are the primary outcomes in trials of treatment efficacy, quality of life measures also may be important to evaluate the impact of treatment toxicities and the extent of loss of visual functioning. Treatment for active CMV retinitis requires either surgery to place an intraocular implant that delivers drug to the retina or systemic administration of drugs that potentially can cause serious toxicities, and treatment efficacy may need to be weighed against the adverse effects of treatment. Clinical measures of CMV retinitis and vision are objective measures of a biological phenomenon, but they do not fully capture patients’ experience of their vision and their ability to function. First, the clinical measures of CMV retinitis typically used to measure efficacy in treatment trials are based on the movement of lesion borders and the increase in amount of retina covered by retinitis lesions. Although clinicians generally record the location of the lesions, because this greatly affects the extent of functional impairment, the above clinical measures of efficacy do not themselves take location into account. Second, patients’ vi-

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B.K. Martin et al. / Journal of Clinical Epidemiology 54 (2001) 376–386

sual functioning may be affected by many factors not captured when visual acuity and visual field are measured in the clinic under specific conditions; such factors may include energy level, ability to concentrate, environmental conditions (lighting, contrast, movement in the visual field), and the extent to which a person’s usual activities (work, self-care, hobbies) require central or peripheral vision [4,5]. A questionnaire to measure both general health-related quality of life and CMV retinitis-specific domains related to treatment impact and vision was developed and pilot tested in a small cross-sectional sample (N ⫽ 26) of patients in a clinical trial [6]. It required 5 min to complete and was wellaccepted by patients. Presented here is the performance of the general and CMV retinitis-specific measures as further evaluated with longitudinal data from a much larger population of 279 patients, who were enrolled in the Cytomegalovirus Retinitis Retreatment Trial (CRRT). This trial provides a larger body of data for evaluating the reliability, validity, and responsiveness of the questionnaire, and therefore its general usefulness in CMV retinitis clinical research. 2. Methods 2.1. Design summary The CRRT was a multicenter, randomized controlled trial conducted by the Studies of Ocular Complications of AIDS (SOCA) Research Group to compare intravenous foscarnet, intravenous ganciclovir, and a combination of intravenous foscarnet and ganciclovir for the treatment of patients with relapsed CMV retinitis [7]. The study sample consisted of 279 patients enrolled at 12 sites between December 1992 and March 1995, who completed a baseline interview at the time of their initial CRRT study visit. Subsequent interviews were conducted at follow-up visits scheduled for 3 months and 6 months after randomization and every 6 months thereafter. 2.2. Measures 2.2.1. General health-related quality of life measures General health-related quality of life was assessed with a 34-item instrument adapted from existing instruments. Items representing nine dimensions of health (general health perceptions, pain, physical functioning, role functioning, social functioning, energy/fatigue, mental health, cognitive function, and quality of life) were taken from the MOS-HIV, an instrument that has been well characterized and tested [8–10]. To assess general health perceptions, a health rating item from the MOS-HIV instrument (adapted from the RAND Health Insurance Experiment) [11], was supplemented with four general health items developed for use in an AIDS clinical trial [12,13]. Also included were two questions about employment, one about change in health status (health transition), and two items from the National Health Interview Survey [14] regarding the number of days spent in bed and the number of days of reduced activity.

377

2.2.2. CMV retinitis-specific quality of life measures A 21-item retinitis-specific component [6] was added to the questionnaire to potentially provide for a more sensitive and responsive assessment of treatment impact and vision. It was thought that patients’ responses to general healthrelated quality of life questions could be heavily influenced by the impact of late-stage AIDS and its treatment and therefore might be affected less by treatment for CMV retinitis. Items to assess visual symptoms (five), visual function (seven), and global vision (two) were taken from the VF-14 [15] and adapted, after semistructured questioning of a sample of patients, to include visual function difficulties and symptoms relevant to CMV retinitis. Symptom questions asked how bothered patients were by common sequelae of CMV retinitis. Visual function questions asked patients about their ability to perform common activities. Global vision questions included a rating of the amount of trouble patients had with their eyesight and a rating of eyesight on an excellent to poor scale. The impact of treatment administration was assessed with four questions, two concerning the extent to which treatment interfered with activities of daily living and social activities and two questions on concerns about physical appearance. We also included three questions about the amount of time spent receiving treatment every day. 2.2.3. Clinical and laboratory measures Clinical and laboratory measures included the Karnofsky Performance Status score [21], CD4⫹ and CD8⫹ T-cell counts, and hemoglobin level. Body mass index was calculated from height and weight (kg/m2). 2.2.4. Clinician-assessed CMV disease status and vision On ophthalmologic examination, clinicians reported whether one or both eyes had CMV lesions and where the lesions were located. Location was defined by three zones—zone 1 being the most critical area of involvement because it includes the macula, optic disc, and the surrounding area within 3000 ␮m of the center of the fovea or 1500 ␮m of the optic disc [16]. Study personnel used standardized procedures and logarithmic charts [16] from the Early Treatment of Diabetic Retinopathy Study [17,18] to determine visual acuity. For patients unable to undergo testing in a standard visual acuity lane, a Ferris-Bailey Near Card was used. Data were collected as the number of letters read correctly at a specified distance. After adjustment for distance, the number of letters read was standardized to a scale of 0 to 100. (Patients unable to read the chart but able to count fingers, detect hand motion, or perceive light only were assigned a visual acuity score of ⫺10 letters; no light perception was denoted as ⫺25 letters.) Selected Snellen equivalents for letters read are as follows: 85 letters ⫽ 20/20; 70 letters ⫽ 20/40; 35 letters ⫽ 20/200. These standardized letters represent a linear measure of visual acuity, for which a difference of 15 letters represents a doubling of the visual angle, regardless of the initial visual acuity [17].

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Study personnel also assessed visual field, measuring the peripheral extent of the field (minus scotoma) along 12 meridians with the IV/4E test object on a Goldman or Topcon perimeter [16,19]. The total degrees of visual field were calculated by summing the 12 meridian scores. 2.2.5. Area of retinal involvement The percentage of retinal area covered by CMV retinitis lesions was measured by graders at the SOCA Fundus Photograph Reading Center. Graders examined fundus photographs of the center of the eye and eight surrounding fields [16,20] and used grids superimposed on the photographs to measure the area of involvement of the posterior retina [7]. 2.3. Analysis 2.3.1. Computation of scales Quality of life scale scores were created by summing item scores within scales and transforming the sum linearly to a 0 to 100 scale corresponding to the percent of the maximum minus the minimum score; higher values indicated better status (better vision, fewer symptoms, less adverse impact of treatment, and better health). For clinical, laboratory, and vision measures, higher scores usually denoted better status. For area of retinal involvement, a higher percentage indicated greater pathology. 2.3.2. Descriptive statistics We examined mean and median scores, standard deviations, and distributions of responses for each item and scale. For continuous variables, arithmetic means are presented in the tables if the distribution was approximately normal; medians are presented if the distribution was skewed. 2.3.3. Performance of the scales We estimated internal consistency reliability of the 11 multi-item scales using Cronbach’s alpha [22]. To assess the convergent and discriminant construct validity of the scales, we used multitrait analysis [23] to determine whether each item correlated more highly with other items in its putative scale than with other scale scores. An item was defined as a scaling success with respect to another scale if its correlation with other items in its own scale differed by 2 standard errors or more from its correlation with the other scale. To further assess validity, we compared baseline scale scores cross-sectionally to findings from clinical examination and from laboratory and vision testing. Vision measures were defined as belonging to the better or worse eye at baseline (the worse eye was defined separately for each vision measure, i.e., the eye with the lower visual acuity, lower visual field, or higher percentage involvement). Based on review of the literature and on clinical experience, we hypothesized that 1) physical health subscales (general health, physical function, energy, pain, role function, and social function scales) would be moderately correlated (r ⫽ .30–.60) with Karnofsky score and body mass index and mildly correlated (r ⫽ .10–.29) with CD4⫹ T-cell count

and hemoglobin [9,10]; 2) visual function score would be moderately correlated (r ⫽ .30–.60) with visual acuity, visual field, and area of retinal involvement in the better eye [6]; 3) visual symptom score would be moderately correlated with visual acuity, visual field, and area of retinal involvement in the worse eye [6]; 4) global vision score would be moderately correlated with measures for both eyes [6]; and 5) vision scale means would be significantly lower for patients who had bilateral disease, zone 1 involvement, or a best corrected visual acuity worse than 20/40. Pearson correlation coefficients and t tests were used to test the significance of the associations between variables. To assess responsiveness [24], we compared longitudinal changes in scale scores to changes in clinical measures. We hypothesized that declines in physical health subscales would be greater in patients whose Karnofsky scores de-

Table 1 Patient characteristics at baseline (N ⫽ 279) Demographics % Male % White Mean age (year) % ⭓College grad % Employed AIDS history % Men having sex with men % IV drug users Median time since AIDS dx (months) Mean #OIs CMV history % Bilateral % Zone 1a disease, either eye % Zone 1a disease, both eyes % Area ⭓25% in either eye % Best corrected VA worse than 20/40 Median time since CMV diagnosis (months) % Extra-ocular CMV Laboratory values (median) CD4⫹ (cells/␮l) Hgb (g/dl) Physical exam Mean Karnofsky Mean weight (kg) Body mass index Vision measures Mean visual field (degrees) Uninvolved eyes Involved eyes Mean visual acuity (letters)b Uninvolved eyes Involved eyes Median retinal involvement (% area) Involved eyes

91.8 60.6 38.6 41.6 21.1 80.5 9.0 18.3 3.3 62.4 69.2 19.4 63.8 5.0 5.1 19.7 7.5 10.9 78 65.5 23.1

683 486 86 76 14.7

IV ⫽ intravenous; AIDS ⫽ acquired immunodeficiency syndrome; OI ⫽ opportunistic infection; CMV⫽ cytomegalovirus. a Zone 1 is the area of the retina ⭐1500 ␮m from the edge of the optic nerve or ⭐3000 ␮m from the center of the fovea. b Selected Snellen equivalents to letters read are as follows: 90 letters ⫽ 20/15; 85 letters ⫽ 20/20; 80 letters ⫽ 20/25; 70 letters ⫽ 20/40.

B.K. Martin et al. / Journal of Clinical Epidemiology 54 (2001) 376–386

clined in a 6-month period of time compared to patients with stable or improved Karnofsky scores. These changes were evaluated with the t test. We also hypothesized that decreases in patient-reported vision scores would be associated with decreases in visual acuity and visual field and increases in area of retinal involvement. Because we had patient-reported and clinical measures of vision that were essentially continuous in nature and longitudinal, we used Generalized Estimating Equations linear regression [25] to estimate the change in scale score relative to change in the clinical measure, adjusted for follow-up time.

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3. Results

score (floor) on any of the scales, with the exception of role functioning (61.2%) and global vision (12.5%). More patients reported the highest possible score: 19% or more of patients scored at the ceiling on role, social, and cognitive function, pain, treatment impact, body image, and visual function. Patients reported spending a mean of 1.9 h per day (S.D. 1.5) receiving treatment, reducing their usual activities for 1.4 h/day (S.D. 1.0), spending half a day or more in bed on an average of 1.4 days (S.D. 2.0) during the previous week, and reducing their usual activities on an average of 2.2 days (S.D. 2.6). Although the majority of patients reported not working because of their health (58.1%), 14.7% reported working full-time, 9.0% part-time, and 15.4% keeping house, taking care of family, or attending school.

3.1. Patient characteristics

3.3. Internal consistency reliability

Patient characteristics at baseline are shown in Table 1. A majority of the 279 participants in this study were homosexual or bisexual white men, with a mean age of 38.6 years at baseline; only 9.0% reported use of illicit injectable drugs. Patients had late-stage AIDS, as evidenced by a median time since diagnosis with AIDS of 18.3 months and a median CD4⫹ count of 7.5 cells/␮l. However, as specified by entry criteria, patients were functioning relatively well; mean Karnofsky score was 78. Eyes with CMV retinitis were showing evidence of vision loss; mean visual acuity was 76 ETDRS letters (20/30⫹1), and mean visual field was 486 degrees in these eyes. The median extent of retinitis in involved eyes was 14.7% of the retinal area.

As shown in Table 3, Cronbach’s alpha at baseline for multiple-item scales ranged from .68 to .88 for the general health scales and from .76 to .87 for the CMV retinitis-specific scales.

3.2. Distribution of scores The distribution of scores at baseline for each scale is shown in Table 2. Few patients reported the lowest possible

3.4. Construct validity The scales generally fulfilled criteria for discriminant construct validity [26]. Each scale’s Cronbach’s alpha coefficient exceeded its correlation with other scale scores (Table 3). In addition, most items correlated more highly with their own scale than with other scale scores (data not shown). However, one item in the visual symptom scale (“bumping into people or things”) correlated as well or better with the visual function and global vision scales. Because this item did not seem to discriminate among the vision domains, it was eliminated from final analyses. Of the remaining item–scale correlations, 95% were determined to

Table 2 Baseline distribution of quality of life scores Scales (# of items)a General health perceptions (5) Quality of life (1) Physical function (6) Energy (4) Body pain (1) Mental health (5) Cognitive function (4) Social function (1) Role function (2) Transition (1) Body image (2) Treatment impact (2) Visual function (7) Visual symptoms (5) Vision rating (2) Hours/day for treatment Hours/day activity reduced Days/week in bed ⭓12 h Days/week activity reduced ⭓12 h a

Mean

Median

S.D.

Range

% at floor

% at ceiling

50.8 59.5 60.5 50.1 67.3 70.8 81.9 61.1 29.0 50.7 82.2 77.4 69.2 60.9 40.1 1.9 1.4 1.4

52.4 50.0 66.7 50.0 60.0 76.0 90.0 60.0 0.0 50.0 100.0 87.5 76.2 65.0 42.9 1.5 1.0 0.0

20.3 22.7 25.8 23.7 26.1 20.9 20.5 30.7 39.6 24.9 26.5 24.4 23.8 24.2 25.1 1.2 2.0 2.2

(0.0–95.2) (0.0–100.0) (0.0–100.0) (0.0–100.0) (0.0–100.0) (0.0–100.0) (10.0–100.0) (0.0–100.0) (0.0–100.0) (0.0–100.0) (0.0–100.0) (0.0–100.0) (4.8–100.0) (0.0–100.0) (0.0–100.0) (0.0–8.0) (0.0–24.0) (0.0–7.0)

0.4 2.9 0.7 0.4 0.7 0.4 0.0 6.8 61.2 5.4 2.9 0.4 1.1 0.7 12.5

0.0 9.3 7.2 2.2 27.6 4.3 30.2 22.6 19.1 10.0 51.6 38.4 10.4 4.3 1.8

2.2

1.0

2.6

(0.0–7.0)

Scores are standardized on a 0–100 scale with higher scores reflecting a better quality of life.

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Table 3 Cronbach’s alphaa and intercorrelations among scales

1. General health 2. Quality of life 3. Physical function 4. Energy 5. Body pain 6. Mental health 7. Cognitive function 8. Social function 9. Role function 10. Transition 11. Body image 12. Treatment impact 13. Visual function 14. Visual symptoms 15. Vision rating a

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

.82 .68 .61 .78 .46 .53 .37 .60 .40 .31 .28 .31 .36 .28 .25

– .42 .60 .28 .50 .34 .43 .26 .19 .24 .29 .28 .24 .23

.84 .69 .41 .37 .47 .53 .43 .21 .12 .17 .41 .20 .22

.88 .42 .44 .39 .58 .41 .24 .17 .21 .31 .20 .18

– .28 .22 .35 .28 .16 .15 .14 .19 .17 .10

.84 .44 .40 .25 .18 .43 .36 .18 .20 .13

.86 .35 .28 .10 .22 .20 .31 .21 .20

– .29 .16 .15 .23 .25 .15 .13

.68 .06 .11 .18 .29 .26 .25

– .02 .12 .18 .10 .06

.80 .39 .01 .09 .01

.76 .24 .31 .23

.87 .57 .66

.79 .67

.78

Cronbach’s alpha is shown on the diagonal for all multi-item scales (it is not calculated for single-item scales).

represent scaling successes (each item’s correlation with other items in its own scale differed by at least 2 standard errors from its correlation with another scale). The failure of a few items to meet the above criterion was largely due to a high correlation between the general health and energy scales and a high correlation of the global vision scale with both the visual function and visual symptom scales. Scales generally demonstrated the hypothesized pattern of correlations with variables (Table 4). General healthrelated quality of life scores were most strongly related to Karnofsky score. In particular, scales related to physical health (general health perceptions, physical function, pain, energy, role function, and social function) were moderately correlated with Karnofsky score (r ⫽ .27–.49) and body mass index (r ⫽ .13–.29). These scales had smaller correlations with CD4⫹ and CD8⫹ T-cell counts (r ⫽ .02–.19) and hemoglobin (r ⫽ .09–.25) (P ⬍ .08). As expected, the mental health and cognitive function scales generally had lower correlations with the clinical variables than did the other scales.

Table 4 Construct validity: correlation of general health scales with clinical and laboratory measures at baseline Pearson correlation coefficients Karnofsky CD4⫹ Hemoglobin Body mass index General health Quality of life Physical function Energy Body pain Mental health Cognitive function Social function Role function

.44 .29 .49 .40 .27 .14 .17 .39 .37

.11 ⫺.02 .19 .11 .08 ⫺.03 .07 .02 .13

.14 .12 .25 .18 .14 .01 .04 .15 .09

.20 .12 .29 .20 .13 ⫺.01 .09 .19 .15

Patient-reported vision scores at baseline were lower for patients with greater abnormalities observed on ophthalmologic examination (Table 5). Vision scores showed a consistent pattern of moderate correlation with findings from visual testing and examination (visual acuity, visual field, and extent of involvement). For visual function scores, the correlations were stronger with visual testing and examination findings for the better eye (r ⫽ .40–.55) than for the worse eye. Visual symptom scores and global vision scores were more strongly correlated with findings for the worse eye (r ⫽ .27–.39 and r ⫽ .41–.55, respectively). Scores for all three vision scales were significantly worse for patients with bilateral disease compared to those with unilateral disease, for those with zone 1 disease compared to those with peripheral disease, and for those with a best corrected visual acuity worse than 20/40 compared to those with better visual acuity. 3.5. Responsiveness Declines in general health-related quality of life measures were larger in patients whose Karnofsky score declined over a 6-month period compared to patients with stable or improved Karnofsky scores (Table 6). Most of the scales related to physical health (general health perceptions, energy, pain, role function, and social function) showed significant declines. The physical function scale, however, was lower but not significantly so (P ⫽ .10). Scales of visual function and visual symptoms were responsive to changes over time in findings from visual testing and examination in the better eye (Table 7). On the visual function scale, deterioration of 1 letter (visual acuity) corresponded to a decrease of 0.75 points, 10 degrees of visual field with 0.8 points, and 1% of retinal area involved with 0.61 points. On the visual symptom scale, loss of 1 letter corresponded to 0.36 points, 10 degrees of visual field with 0.5 points, and 1% of retinal area involved with 0.32 points.

B.K. Martin et al. / Journal of Clinical Epidemiology 54 (2001) 376–386

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Table 5 Construct validity: vision scales vs. vision measures at baseline A. Pearson correlations of vision scales with other continuous vision variablesa Visual acuity

Visual function Visual symptoms Vision rating

Visual field

% Involvement

Better

Worse

Better

Worse

Better

Worse

.55 .22 .38

.41 .27 .47

.52 .30 .37

.50 .39 .55

⫺.40 ⫺.27 ⫺.32

⫺.35 ⫺.28 ⫺.41

B. Mean vision scale scores for categorical vision variables Bilateral disease

Visual function Mean S.D. Visual symptoms Mean S.D. Vision rating Mean S.D.

Zone 1 disease b

VA 20/40 or better b

Yes

No

P value

Yes

No

P value

Yes

No

P valueb

65.6 25.2

75.1 20.0

.001

66.0 23.9

76.6 21.8

.001

71.9 21.4

30.4 22.9

.0001

58.5 24.4

65.0 23.3

.031

57.8 23.6

67.9 24.0

.001

62.3 23.6

41.1 24.5

.0003

37.1 25.8

45.2 23.1

.009

35.0 22.5

51.7 26.8

.0001

42.1 24.1

11.1 21.7

.0001

a P values for the hypothesis that the correlation coefficients were equal to 0 were all ⭐.0003; the upper and lower confidence limits for the correlation coefficients all fell within ⫾ .08–.12 of the coefficients. b P values for the differences in the mean scale scores were obtained with the t test.

Thus, loss of 15 letters (a doubling of the visual angle) would correspond to a decrease of 11.25 points on the visual function scale and 5.40 points on the visual symptom scale. 4. Discussion Assessment of health-related quality of life represents a relatively recent and important addition to clinical and health services research. In both HIV/AIDS and ophthalmology, quality of life may add to our understanding of how treatments affect health either by confirming clinical findings or by elucidating situations in which clinical findings do not adequately capture patient quality of life. Table 6 Responsiveness of general health scales to decline in Karnofsky score from baseline to 6-month visit Patients with Patients with stable or improved ⭓10-point decline Karnofsky score in Karnofsky score N Change in Quality of life General health Physical function Energy Body pain Mental health Cognitive function Social function Role function Transition

59

68

⫺2.4 ⫺0.3 ⫺2.8 ⫺3.3 ⫺0.9 2.2 1.5 1.3 ⫺1.1 ⫺1.9

⫺5.9 ⫺8.7 ⫺9.0 ⫺13.4 ⫺10.9 ⫺1.6 0.2 ⫺9.1 ⫺19.0 ⫺6.1

P valuea

.37 .01 .10 .01 .02 .21 .64 .06 ⬍.01 .40

a P values for the differences in the mean score changes were obtained with the t test.

An example of concordant findings with conventional and quality of life endpoints is a comparative study of zidovudine and zalcitabine. The advantage of zidovudine was evident in quality of life scores months before they became apparent on conventional endpoints [27]. Also, in a randomized trial of thalidomide for oral aphthous ulcers, quality of life measures helped to explain the benefits of clinical improvement [28]. In other studies, quality of life findings Table 7 Responsiveness of vision scales to change in clinical vision measures

A. Change in visual function scale associated with change in: Visual acuity (letters) Better eye Worse eye Visual field (degrees) Better eye Worse eye % Area involved Better eye Worse eye B. Change in visual symptom scale associated with change in: Visual acuity (letters) Better eye Worse eye Visual field (degrees) Better eye Worse eye % Area involved Better eye Worse eye

␤

S.E.

P value

.75 .07

.10 .05

⬍.000001 .183

.08 .02

.01 .01

⬍.000001 .015

⫺.61 ⫺.04

.12 .08

⬍.000001 .631

.36 .03

.10 .06

.0003 .579

.05 .03

.02 .01

.002 .023

⫺.32 ⫺.13

.12 .10

.006 .226

Parameters and P values were obtained from GEE linear regression and adjusted for follow-up time.

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were discordant with results suggested by other outcome variables. For example, in early studies of zidovudine, conventional clinical endpoints suggested an advantage of treatment, while further studies showed that clinical differences were offset by patient reports of symptoms and reduced quality of life [29–31]. Similarly, vision-related quality of life measures may be concordant or discordant with clinical measures of vision or of eye disease severity. Steinberg and colleagues found that patient-reported visual functioning was better than vision testing as an indicator of success in cataract extraction [15]. Many factors other than vision may determine the ability to perform the activities addressed in vision-related quality of life instruments (e.g., concentration, fatigue, motivation, etc.). Whether concordant or discordant with clinical measures, application of patient-reported outcome measures results in a more comprehensive investigation of the efficacy of treatment. Before given quality of life measures can become useful complements to clinical endpoints in the evaluation of treatments, the performance of the measures must be examined. To test validity, we looked at the correlation at baseline of both the general and disease-specific quality of life measures with clinical variables. We were able to confirm that the quality of life measures were related as expected with clinical variables, but the clinical variables did not provide a means for testing whether the various quality of life dimensions were distinct. For this we used multitrait analysis. Most of the general health-related scales appeared to represent distinct dimensions. However, the lack of discrimination between the items in the general health perception scale and the energy scale suggests that these concepts may track together closely in patients with advanced AIDS. Although the general health perception scale used in this study yields a more normal distribution of scores in our population than does the corresponding scale in the MOS-HIV questionnaire [8], the concept being measured may overlap with the energy scale. Of the vision-related measures, the visual function scale performed best in multitrait analysis. We refined the visual symptom scale by deleting from further analyses the item (“bumping into people or things”) that did not discriminate among the vision domains. Multitrait analysis also revealed a strong relationship between the scale to assess global vision and scales to assess visual functioning and visual symptoms. In previous testing of our questionnaire on a smaller sample of CMV retinitis patients, we observed that patient reports of their global vision reflected both their experience with performing activities that require vision and their experience with visual symptoms [6]. The current findings confirm this observation and suggest that, although this two-item scale may provide a useful summary score, the scale is not distinct from the other two patient-reported vision scales. Thus, if one were seeking an overall score for vision-related quality of life, it would be inappropriate to average or combine this scale score with the others.

With respect to reliability, the scales demonstrated acceptable internal consistency. With the exception of role function (.68), Cronbach’s alpha coefficients exceeded the generally agreed upon standard for group comparisons of .70 [32]. Given the data collection schedule in the CRRT, we were not able to adequately assess test–retest reliability. Sequential administrations of the questionnaire were far apart enough in time that changes from one administration to the next could represent real changes in patients’ perceptions of their health and vision. In general, in a severely ill population, it is difficult to assess reliability with repeat administrations of a quality of life instrument. However, the lack of data on test–retest reliability is not a large limitation; in the clinical trials setting, it is considered more important to demonstrate the responsiveness of the quality of life measures to changes over time in patients health [33,34]. Few studies have demonstrated the responsiveness of patient-reported measures of vision to changes in other visionrelated measures. The analysis often suggested for evaluating responsiveness is to compare patient-reported measures from patients whose condition has remained stable to those obtained from patients whose condition has deteriorated. In this study, a more refined analysis of responsiveness is possible, in which changes in patient-reported vision measures can be compared at several points in time to concurrent changes in carefully recorded results from vision testing and fundus photography. The relationship among patient-reported vision measures and clinical measures appeared somewhat different in longitudinal analyses than in cross-sectional analyses. At baseline, we found, as did Steinberg et al. [15], that visual functioning is more highly correlated with vision measures in the better eye than in the worse eye. As hypothesized, we also found that the visual symptoms scale was more highly correlated with vision measures in the worse eye. However, in longitudinal analyses, the changes in patient reports both for function and symptoms were consistently related to changes in visual acuity, visual field, and extent of retinitis in the better eye. It may be that changes in an unaffected or mildly affected eye are more likely to be noticed than are changes superimposed on an eye in which patients are already experiencing vision loss. For quality of life instruments used in clinical trials (to evaluate changes over time), it is important to examine potential ceiling and floor effects of the measures. That is, we do not want large proportions of the population to report either the best or worst possible scores for a scale, which would make it difficult to detect improvement or deterioration, respectively. In CMV retinitis, however, patients’ vision is unlikely to improve, except in a few circumstances, such as when a retinal detachment is repaired. Thus, although 10% of patients reported the best possible visual functioning at baseline, this ceiling effect is unlikely to represent a measurement problem. Floor effects, on the other hand, are problematic. Floor effects were not observed for the visual function scale and visual symptom scale, but

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12.5% of patients reported the lowest possible score for the global vision scale. Inclusion of additional levels or items would allow for greater spread in patient responses; spreading out the responses of patients at the low end of the scale may thereby eliminate the floor effect and improve its usefulness in clinical trials. The potential for ceiling effects may exist for some of the general health-related scales of the MOS-HIV [9]. For example, despite the fact that the patients in this study had advanced HIV disease, 20–30% of patients reported the best possible score for scales such as role functioning, cognitive functioning, and social functioning. Ceiling effects in these domains may not affect the evaluation of treatments for CMV retinitis, as such treatments are not likely to improve general measures of health. However, in clinical trials for anti-HIV therapies, ceiling effects could pose problems if treatment-related improvements may occur in patients who already report the highest scores in these areas (i.e., no problems with cognitive functioning and no limitations in social functioning, work or usual activities). In the setting of new, highly effective antiretroviral agents, ceiling effects that prevent measurement of improvements in general health domains warrant attention. The role function scale, which had only four possible levels of responses, displayed both floor and ceiling effects at baseline. This scale is useful for quantifying the proportion (61%) of patient in this population who, at the beginning of the study, were already limited in their ability to perform their usual activities. The usefulness of the scale for measuring longitudinal change, however, is minimal because of the floor and ceiling effects. After evaluating the performance of a quality of life instrument, the question of how to clinically interpret the measures may still remain. One approach to aid in the understanding of quality of life scores is to express a difference (or change) in score relative to a variable that is meaningful to the intended audience. In this case, eye care specialists may achieve a better understanding if scores are equated to results from vision testing. Thus, a decrease of 11 points on our visual function scale corresponds to the loss of 15 letters (or 3 lines) on a logarithmic eye chart (such a decrease in visual acuity corresponds to the difference between 20/20 vision and the point at which one cannot drive legally without corrective lenses). Although patient-reported measures are not interchangeable with vision testing measures, such equations may help clinicians to determine whether differences in patient-reported measures are clinically important. In conclusion, we have tested a brief patient-reported assessment battery that combined a core general questionnaire with a disease-specific module. This strategy allowed us to assess a comprehensive range of dimensions and to make comparisons across populations and treatments, while also improving responsiveness to changes related to specific interventions. Our results provide further evidence for the reliability, validity, and responsiveness of these measures in

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patients with AIDS and CMV retinitis. The results support the use of this instrument in clinical trials or other studies of CMV retinitis to measure quality of life outcomes. Further research is needed to demonstrate whether the scales can be useful in clinical practice to describe the extent of specific visual impairments or to track the effect of interventions.

Appendix A. Participating centers (13) Clinical Centers Baylor College of Medicine, Cullen Eye Institute, Houston, TX: Richard A. Lewis, MD, MS (Director); Louise M. Carr, CRA; Kay Doyle, RN; Victor Fainstein, MD; Ronald Gross, MD; Holly Paskell, RN; Tobias C. Samo, MD; James M. Shigley, CRA. Johns Hopkins University School of Medicine, Baltimore, MD: Douglas A. Jabs, MD (Director); John Bartlett, MD; Rebecca Becker, PA-C; Laura C. Coleson, RN, MPH; J. P. Dunn, MD; Judith Feinberg, MD; Mei-Ling Tay-Kearney, MD; Jo Leslie, PA-C; Tracy Miller, COT; Laura G. Neisser, COT; Richard D. Semba, MD. Louisana State University Medical Center, New Orleans, LA: Bruce Barron, MD (Director); Christine Jarrott, RN; Cynthia LeCount, RN, Gholam Peyman, MD; Dennis Swenie, MD. Memorial Sloan-Kettering Cancer Center and New York Hospital-Cornell Medical Center, New York, NY: Murk-Hein Heinemann, MD (Director); Catherine O’Leary, RN; Bruce Polsky, MD; Kathleen Squires, MD; Susanne Wise-Campbell, RN. Mount Sinai School of Medicine, New York, NY: Alan H. Friedman, MD, (Director); Tony W. Cheung, MD; Norma Justin, MS; Henry Sacks, MD, PhD; Colette Severin, MS; Steven Teich, MD; Fran Wallach, MD. New York University Medical Center, New York, NY: Dorothy N. Friedberg, MD, PhD (Director); Adrienne Addessi, MA RN; Douglas Dieterich, MD; Keven Frost; Richard Hutt, RN; Maria Pei; Therese Powers, MS; Carol Scoppe. Northwestern University, Chicago, IL: David V. Weinberg, MD, (Director); Lee Jampol, MD; Alice Lyon, MD; Annmarie Muñana, RN; Robert Murphy, MD; Kathleen Naughton, RN; Frank Palella, MD; Len Richine; Gloria Valadez. University of Alabama, Birmingham, AL: Kathleen Squires, MD (Director); James A. Kimble, MD; Jill Weingarten, RN. University of California, Los Angeles: Gary N. Holland, MD (Director); Suzette Chafey, RN, NP; W. David Hardy, MD; Ann K. Johiro, RN, MN, FNP; Chris Kimbrell, RN; Ralph D. Levinson, MD; Lesley J. MacArthur, MLA; Maureen Martin, RN, MN, FNP; Adnan Tufail, FRCO.

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University of California, San Diego: William R. Freeman, MD (Director); J. Fernando Arevalo-Colina, MD; Tom Clark, CRA; Cheryl L. Jarman; Linda Meixner, RN; Tze Chiang Meng, MD; Mary Ann Simanello, RN; Stephen Spector, MD. University of California, San Francisco: James O’Donnell, MD (Director); Theodore Bush, RN; Jacqueline Hoffman; Alexander Irvine, MD; Mark Jacobson, MD; James Larson, COT; Mary Payne, RN; Stuart Seiff, MD; Maxine Wanner. University of Miami School of Medicine, Miami, FL: Janet Davis, MD (Director); Claudio Cabrejos, MPH; Paul Mendez, MD; Timothy Murry, MD; Ruth Vandenbroucke. University of North Carolina, Chapel Hill, NC: Charles van der Horst, MD (Director); Jan Kylstra, MD; Shannon Latkin; David Wohl, MD. Resource centers Chairman’s Office, The Johns Hopkins University School of Medicine: Douglas A. Jabs, MD (Study Chairman); Joan M. Dodge; Joan L. Klemstine; Tracey A. Schuerholtz; Maria Stevens. Former Members: Amy C. Klemm, Renee M. Webb. Coordinating Center, The Johns Hopkins University School of Hygiene and Public Health: Curtis L. Meinert, PhD, (Director); Debra Amend-Libercci; Karen L. Collins; Betty J. Collison; John Dodge; Michele Donithan, MHS; Cathleen Ewing; Nancy Fink, MPH; Charlotte Gerczak, MLA; Adele M. Kaplan Gilpin, JD, PhD; Judith Harle; Janet T. Holbrook, MS, MPH; Robert Huffman; Milana R. Isaacson; Charlene R. Levine; Barbara K. Martin; Deborah J. Nowakowski; Rosetta M. Owens, Alfred Saah, MD, MPH; Michael Smith; Franklin Sun, MS; James Tonascia, PhD; Aynur Ünalp, MD; Mark L. Van Natta, MHS; Albert Wu, MD, MPH. Fundus Photography Reading Center, University of Wisconsin: Matthew D. Davis, MD (Director); Jane Armstrong; Judith Brickbauer; Rosemary Brothers; Marika Chop; Larry Hubbard, MAT; Dolores Hurlbert; Linda Kastorff; Yvonne Magli; Michael Neider; Vicki Stoppenbach; Marilyn Vanderhoof-Young. Drug Distribution Center, Odgen Bioservices Corporation, Rockville: Mark Walls. National Eye Institute, Bethesda: Natalie Kurinij, MD. National Institute of Allergy and Infectious Diseases, Bethesda: Beverly Alston, MD; Mary Foulkes, PhD. Committees Officers of the Study: Douglas A. Jabs, MD (Chair); Matthew D. Davis, MD; Natalie Kurinij, PhD; Curtis L. Meinert, PhD. Steering Committee: Douglas A. Jabs, MD (Chair); Adrienne Addessi, MA, RN; Tom Clark, CRA; Mat-

thew D. Davis, MD; William Freeman, MD; Janet Holbrook, MS, MPH; Gary Holland, MD; Larry Hubbard, MAT; Mark Jacobson, MD; Richard A. Lewis, MD, MS; Curtis Meinert, PhD; Richard Mowery, PhD; Robert Murphy, MD; Bruce Polsky, MD; James Tonascia, PhD; Maxine Wanner. SOCA-ACTG Joint Executive Committee: Douglas A. Jabs, MD (Chair); Matthew D. Davis, MD; William R. Duncan, PhD; Judith Feinberg, MD; Harold Kessler, MD; Joyce Korvick, MD; A. Gary Lambert; Curtis L. Meinert, PhD; Richard Mowery, PhD; Steve Schnittman, MD; James Tonascia, PhD. Policy and Data Monitoring Board: Byron W. Brown, Jr., PhD (Chair); Brian Conway, MD; James Grizzle, PhD; Robert Nussenblatt, MD; John P. Phair, MD; Harmon Smith, PhD; Richard Whitley, MD. Non-voting members: Beverly Alston, MD; Matthew D. Davis, MD; Mary Foulkes, PhD; Douglas A. Jabs, MD; Curtis L. Meinert, PhD; Richard L. Mowery, PhD; James Tonascia, PhD. Communnity Advisory Board: Mark Bowers; Ben Cheng; Kevin Frost; A. Gary Lambert; Derek Link.

Appendix B. SOCA vision-related quality of life questionnaire We would like to ask you some questions about your eyesight. 1. How much trouble do you now have with your eyesight (check only one): No trouble A little trouble A moderate amount of trouble A lot of trouble

( ( ( (

1) 2) 3) 4)

Card #1 1 No difficulty 2 A little 3 A moderate amount 4 Unable to do this 5 Don’t do for other reasons START USING CARD #1 Use card #1 for item 2. The following questions ask about problems with your eyesight you might have had during the past 4 weeks. 2. Do you have difficulty (even with glasses) in doing any of the following activities? a. Reading small print such as labels on medicine bottles, a telephone book, food labels:

_____

b. Reading a newspaper or book:

_____

(1 - 5) (1 - 5)

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c. Driving during the day:

_____ (1 - 5)

d. Driving at night:

_____ (1 - 5)

e. Reading traffic signs, street signs, store signs:

_____ (1 - 5)

f. Doing writing such as making lists, writing notes or letters:

_____ (1 - 5)

g. Watching television:

_____ (1 - 5)

c. Made you concerned about how you look?

_____ (1 - 5)

d. Made you embarrassed to go out in public?

_____ (1 - 5)

_____ (1 - 5)

STOP USING CARD #2 6. During the past 4 weeks, when receiving treatment for your eyes...

STOP USING CARD #1 Card #2 1 2 3 4 5

b. Interfered with your daily activities like bathing, dressing, shopping or preparing meals?

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a. How many hours a day did you spend on the treatment? b. How many hours a day did the treatment keep you from doing the things you wanted to do? c. Was the amount of time you had to spend every day on your treatment (check only one):

Not at all A little Somewhat Quite a lot A great deal

START USING CARD #2 Use card #2 for items 3 and 5. 3. During the past month, how much have you been bothered by... a. Blurred or distorted vision:

_____

b. Spots floating in front of your eyes:

_____

c. Blind spots or blurry spots:

_____

(1 - 5)

Much too long Too long About right Not applicable

–– –– • ––

–– –– • ––

( ( ( (

1) 2) 3) N)

(1 - 5)

Acknowledgments

(1 - 5)

d. Trouble seeing to one side or the other:

_____ (1 - 5)

e. Bumping into people or things:

_____ (1 - 5)

STOP USING CARD #2 4. In general, would you say your eyesight is (check only one): Excellent Very good Good Fair Poor

( ( ( ( (

1) 2) 3) 4) 5)

START USING CARD #2 Getting treatment for your CMV eye infection can be inconvenient, especially because the medicine needs to be given into your veins. 5. During the past 4 weeks how much has the treatment for your eyes... a. Interfered with your social activities with family, friends, neighbors, or groups?

_____ (1 - 5)

The Cytomegalovirus Retinitis Retreatment Trial was supported by cooperative agreements from the National Eye Institute to The Johns Hopkins University School of Medicine, Baltimore, MD (U10 EY 08052), The Johns Hopkins University School of Hygiene and Public Health, Baltimore, MD (U10 EY 08057), and the University of Wisconsin School of Medicine, Madison, WI (U10 EY 08067). Additional support was provided by the National Center for Research Resources through General Clinical Research Center grants 5M01 RR 00350 (Baylor College of Medicine, Houston, TX); 5M01 RR 00035 and 5M01 RR 05096 (Tulane University School of Medicine, New Orleans, LA); 5M01 RR 00071 (Mount Sinai Medical Center, New York, NY); 5M01 RR 00047 (New York Hospital-Cornell Medical Center, New York, NY); 5M01 RR 00096 (New York University, New York, NY); 5M01 RR 00048 (Northwestern University, Chicago, IL); 5M01 RR 00865 (University of California, Los Angeles, CA); 5M01 RR 00083 (University of California, San Francisco, CA); 5M01 RR 05280 (University of Miami, FL); and 5M01 RR 00046 (University of North Carolina, Chapel Hill, NC). Support also was provided by the National Institute of Allergy and Infectious Diseases through cooperative agreements U01 AI 27668 (The Johns Hopkins University); U01 AI 27674 (Tulane University, New Orleans, LA); U01 AI 27669 (Memorial Sloan-Kettering, New York, NY); U01 AI 25917 (New

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York Hospital-Cornell Medical Center, New York, NY); U01 AI 27667 (Mount Sinai Medical Center, New York, NY); U01 AI 27665 (New York University, New York, NY); U01 AI 25915 (Northwestern University, Chicago, IL); U01 AI 27660 (University of California, Los Angeles, CA); U01 AI 27670 (University of California, San Diego, CA); U01 AI 27663 (University of California, San Francisco, CA); and U01 AI 25868 (University of North Carolina, Chapel Hill, NC). Drugs were provided by Amgen, Inc (Thousand Oaks, CA), Astra USA, Inc (Westborough, MA), Bristol-Myers Squibb Co (Princeton, NJ), Burroughs Wellcome Co (Research Triangle Park, NC), and Syntex Research (Palo Alto, CA). The results of these analyses have been presented at: the SOCA Research Group meeting in Miami, Florida, April 1997; the Fifth Annual Drug Information Association Symposium on Quality of Life Evaluation in Hilton Head, South Carolina, April 1998; and the Association for Research in Vision and Ophthalmology meeting in Fort Lauderdale, Florida, May 1998.

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