- All clinical research studies of treatment outcomes in glaucoma should collect patient-centered outcome data.
- Research evaluating new interventions for glaucoma should collect data regarding treatment satisfaction, side effects and lifestyle burden on patients. The impact on carers and health care systems should also be considered.
- If patient-reported outcome measures (PROMs) are used to collect QoL information by questionnaire, these tools should have been evaluated in a glaucoma cohort using an Item Response Theory (IRT) method.
- IRT-calibrated Item Banking, supported by Computer Adaptive Testing, is a preferable model of PROM data administration and utilization of the future.
- Objective functional assessment of glaucoma-related visual disability should complement PROM data in the future.
- Utility is a health metric that evaluates the economic impact of a condition; there is a lack of validated instruments that measure utility specific to glaucoma and a need for a glaucoma-specific utility metric to help justify health dollar spent.
It is important to measure the impact of a condition or therapeutic intervention on patients’ lives. This information helps educate clinicians, patients and policymakers about the burden of illness; tailor treatment to the individuals’ needs; and define guidelines for use of emerging medical technology and understand socio-economic determinants of health. However, measures to capture this information are not frequently incorporated into clinical care, suggesting a lack of consensus regarding which measures should be used, and when.
Patient-centered outcomes (PCO) are outcomes from medical care important to patients. One common PCO is the patient reported outcome measure (PROM); a health outcome reported by the patient directly. Well-crafted PROMs realign the clinicians’ focus to something similar to the patient’s needs and wants, and offer the promise of better and more meaningful clinician-patient relationships.1 The use of PROMs can improve clinical outcomes.2,3 PROMs are essential for clinical research and several decision makers, namely the Food and Drug Administration (FDA), mandate the assessment of patient-reported outcomes in all clinical trials.4 Indeed many large RCTs in glaucoma are being designed with PROMs as the main (primary) outcome measure.5,6
Functional analysis involves the direct assessment of an individual’s ability to perform a visually-challenging task that might be limited by visual morbidity such as glaucomatous optic neuropathy. It is important to directly analyze function in addition to self-reflected data; the latter might be influenced by recall bias, mood, and other nonclinical factors.
Utility analysis involves a form of PROM that measures utility value (UV), key to health-economic assessment. Evaluating the cost-effectiveness of glaucoma services is vital to the justification of ongoing allocation of resources to glaucoma management.7
In clinical glaucoma research there is a strong need for well-validated, and easy to implement PROMs, functional and utility analysis tools. Such tools are required to measure the multidimensional impact of glaucoma and/or interventions to improve glaucoma outcomes in terms of (1) everyday visual challenges; (2) discomfort and complications from treatment; (3) time, inconvenience and cost of ongoing care; (4) psychological elements (5) the impact on their relatives and society at large (Fig. 1) and (6) health economic impact. However, these tool(s) are currently lacking.
As our arsenal for treating glaucoma rapidly expands with an exciting, and at times bewildering, array of therapeutic options, our means to evaluate the patient impact of these new treatments is lagging.8
A PROM is a self-reported questionnaire involving a number of questions, also known as items, asked of the patient, who records their responses, typically in a Likert (several answers for each item on a scale) or binary scale, reflecting the strength of their response.9 Items are typically grouped into similar themes or domains. PROMs can be short, assessing one domain, or longer attempting to capture data on multiple domains. PROMs are often derived from focus group interviews or qualitative research.10,11
ANALYSIS OF PROM DATA
Data derived from PROMs should not be quantitatively evaluated in a similar manner to other clinical data. First, the numbers generated on a Likert scale are labels, not reflective of evenly-spaced numerical values. We, therefore, cannot assume that the relationship between responses such as “none,” “a little bit,” and “some” coded as “1,” “2,” and “3,” respectively, relate to each other in an interval manner. Furthermore, the relationship differs for each item on the PROM. When answering PROMs, a variety of somatic, psychological or cultural influences may weigh in, independent of glaucoma and provide unwanted noise to the dimension being assessed. This, in turn, can affect the measurement precision of the trait being assessed. For instance, we would want reassurance that a specific item trends in the same direction as the other items, and varies in a consistent direction with worsening glaucoma severity.
ITEM RESPONSE THEORY (EG RASCH ANALYSIS)
To deal with these psychometric issues, PROM data are best analyzed using Item Response Theory (IRT). A version of IRT commonly used in research is Rasch analysis; this is a mathematical model that uses questionnaire data to assign a probability of difficulty with any item/question based on the calculated level of ability in the tested individual.12,13 The probability of a correct response is modeled as a logistic function of the difference between the person and item scores which represent the ability of the person and the difficulty of the item, respectively.14,15 In other words, persons with a given level of ability would be modeled as more likely to report being able to do easier tasks and less likely to report being able to do more difficult tasks. In addition to analyzing test results among a study population, Rasch analysis provides an estimate of the soundness of the psychometric properties of the instrument, letting us know to what extent the instrument can be trusted to give accurate, reliable and precise estimates of ability in the tested individuals. (Further details and worked examples of Rasch analysis can be found in the literature).14,16 Dimensions analyzed include:
- Assessment of response categories: Are the cohort’s answers on the Likert scale ordered or disordered?
- Measurement of precision: How well can the PROM discern between different severity levels of impairment?
- Item targeting: Does the mean item difficulty appropriately match mean personability; that is, is the test appropriately challenging for this cohort? Poorly targeted items will yield the same response from all or nearly all persons in the study.
- Unidimensionality: How faithfully does a PROM dimension or domain represent a single problem or trait?
In this way, Rasch analysis provides a robust method of evaluating the suitability of a PROM to distinguish between individuals of varying levels of ability within a study cohort. Furthermore, an iterative approach to Rasch analysis allows serial refinement of items and patient data, until the dataset approaches satisfactory fit to the Rasch model; through the elimination of questions/items that do not seem to capture the same underlying construct as other items or measure the data with sufficient precision.17,18
In clinical glaucoma research PROMs validated in past validation studies (and sometimes reconstructed to optimize validity and performance) using IRT (eg, Rasch) should be used in preference to PROMs not validated in this manner. In addition, IRT methods should be used to analyze new PROM data. In some cases, the difficulties of the items from large previous studies can even be used to derive more accurate scores of person-level ability (also known as anchoring).
CURRENT LIMITATIONS OF PROMS
There is a current lack of well-validated PROMs that measure the multidimensional impact of glaucoma on the patient, their relatives, and society as a whole.
While there are many PROMs available for use in clinical glaucoma research, most are problematic and/or have not been evaluated using IRT (Table 1). They can be broadly grouped into general health-related, vision-specific, and glaucoma-specific PROMs.
Most general health and even vision-specific PROMs are insensitive to the visual burden of glaucoma, which may be less obvious to the patient and/or affect different functions when compared with diseases that affect central vision (eg, macular disease or cataract).30 Although patients with glaucoma are generally unaware of any visual limitation until the disease is moderately advanced, vision-related activity limitation can be detected even in patients with visual field defects who are otherwise unaware of their condition using the correct PROMs.51 PROMs that are not glaucoma-specific have been inadequate in detecting subtle visual disability in early/moderate glaucoma, perhaps because they are asking the wrong questions, that is, questions more attuned to central visual function or visual abilities affected by non-glaucomatous visual morbidity.23,52,53
PROMs designed specifically for glaucoma from focus groups are available; however only one, the Glaucoma Activity Limitation-9 (GAL-9), has been validated appropriately using Item Response Theory as well as classical tools of criterion, convergent and divergent validity (Table 2). The GAL-9 is a condensed version of the original Glaucoma Quality of Life (GQL)-15 of which only 9 items passed Rasch analysis in a German cohort.14 In an Indian cohort, 10 items of the GQL-15 passed Rasch analysis, hence it has also been converted into the GAL-10.54 In addition, the Glaucoma Symptom Scale (GSS), when pooled with the GQL-15, has passed IRT analysis;55 however, it has not been analyzed on its own with IRT.
Although the GAL-9 measures ‘activity limitation’ related to visual loss from glaucoma well, it does not evaluate other aspects of glaucoma’s impact on the individual such as comfort of treatment, psychosocial dimensions or burden of monitoring and treatment. Such aspects are what is critically important in evaluating new therapies for glaucoma, and where new tools are most acutely needed.
PROMs can be intrusive and impractical to complete within a time-pressured clinical practice. However digital-access allowing patients to complete PROMs at home by e-kiosk linked to personal smart-phone, tablet, or PC might solve this problem and revolutionize our ability to generate PROM data.56
PROMs are not the only way to assess patient views. Semistructured or open-ended interviews, focus group work and Patient-Reported Experience Measures (which seek to evaluate the patient’s experience with the care provision) may capture important information that PROMs can be too narrow to ascertain. Furthermore, patients’ views can be ascertained in patient-physician discussions, as they are every day in clinical care, though these discussions do not translate well into quantifiable outcomes. While IRT deals admirably with many weaknesses of raw PROM data, no statistical analysis can make up for unquantifiable bias that can exist in PROMs—hence the importance of complementary assessment of patient function and experience, such as functional analysis (described below).
The use of discreet paper-pencil–based PROMs has limitations. As stated above, PROMs have a finite number of items and have either too few items for appropriate precision or are confined to only 1-2 domains. Another problem is that the original IRT analysis used to engineer the PROM is based on the responses of the sample population, and hence the generalizability to other populations cannot be assumed. An example is the difference in the Rasch analysis of the GQL-15 cited above between a German and Indian cohort resulted in the GAL-9 or the GAL-10.14,54
To overcome these weaknesses, item banking, specifically computerized, adaptive, glaucoma-specific item banking, seems to be a superior method of collecting patient self-reported data.
An item bank is a large collection of calibrated (ie, known-difficulty), prevalidated items that measure the domains of quality of life-related to glaucoma.57 Only a few bank items are answered by each participant. A computer adaptive testing (CAT) system can be used to implement the items from the bank.58 Each underlying trait has multiple items calibrated to assess different levels of participant ability, and the person’s responses to one influence the next item chosen by the system. This is to appropriately determine the personal response to the underlying measure (Fig. 2), that is, to avoid unnecessarily asking about easy items in persons with high ability, or about difficult items in persons with low ability. Even though participants may answer different items, the scores are meaningful in the same terms. In this manner, testing is similar to staircase methods used to determine point sensitivity in computerized visual field analysis, where stimulus sensitivity is determined by past responses at that location and/or nearby locations. Such testing can be administered along multiple domains (mobility, ocular discomfort, inconvenience of treatment, anxiety) to describe broadly how glaucoma is affecting that individual. Moreover, as item banks are perpetual, new items can be added to suit changes in disease management or changes in daily living and technology; for example, a PROM from the 1990s would have nothing about smartphone use, whereas a PROM developed today might.
Compared with traditional PROMs, CAT requires fewer items to arrive at equally precise scores reducing test burden, enhances validity and reliability, and reduces ceiling and floor effects through better targeting of appropriate questions.58 By providing real-time feedback, CAT also facilitates integration of psychometric testing promptly into the research or clinical interaction. As health data is shifted to the Electronic Medical Record (EMR), the computerized nature of item banking, with the potential to be influenced by data on the EMR is promising.
Currently, several item banks for glaucoma are being constructed but none are available for wide use.59,60
Self-reported questionnaires, whether traditional pencil-paper discrete PROMs or computer-based item banks have limitations based on the self-reported nature of the test. Psychological factors recall bias and personality may influence patients’ responses. Patients may under- or over-estimate their functional impairment. Two patients with the same degree of clinically-measured vision loss may rate their disability differently on a questionnaire.41,61
These limitations can be overcome by functional analysis: measurement of patient performance for a task or set of tasks. These tasks may only be surrogates of the tasks in real life. Glaucoma has a measurable, meaningful impact on many aspects of daily living that may increase patients’ challenges and frustrations. Driving, reading speed, walking and balancing, adapting from light to dark environments or vice versa, recognizing faces and searching for objects are all impacted measurably by glaucoma.62,63 Walking safely, whether on steps, curbs or in low illumination, is a particular hazard for glaucoma patients.64 The ability for these challenging tasks can be measured and form part of an objective, functional analysis to complement self-reported questionnaire data.
Several tests have been developed that evaluate multiple tasks of visual function for glaucoma patients. The first such test was the Assessment of Visual Disability Related to Vision (ADREV), later compressed to the 9-item Assessment of Ability Related to Vision (AARV).65–67 Although pioneering, these tests’ applicability to glaucoma is limited as they are predominantly influenced by central visual function.68 They also require participants to navigate a large space which may not be easily recreated. Also, outcomes may be influenced by neuro/musculoskeletal morbidities.41,69
Recently, computer-based tests have been used to simulate multitask assessment in glaucoma patients. Such testing, reflective of daily tasks, can be standardized, timed, and harness new technology to be interactive and/or involve gaze monitoring. The Cambridge Glaucoma Visual Function Test (CGVFT) comprises of tests projected on screen and designed to reflect peripherally visually-challenging tasks of varying difficulty levels, all to time with a degree of central gaze control (Fig. 3). Validated with Rasch analysis, the CGVFT had good correlation with visual field and PROM metrics.70 Virtual-reality software to generate an artificial 3-dimensional environment has also been used to simulate daily tasks affected by glaucoma.71 However, few tests have been created to fully capture the wide range of functional tasks in glaucoma, including mobility, reading, searching, and facial recognition.
Utility analysis involves generating UVs to estimate the ratio between the cost of a health-related intervention and the benefit it produces. Typically UVs assess the preference values that patients attach to their overall health/vision status, which typically range from 0 (death) to 1 (perfect health), or for vision-related UVs, from 0 (blindness) to 1 (perfect vision). UVs are one component of quality-adjusted life years (QALYs), the other being life expectancy.72 As health resources are becoming constrained, QALYs are increasingly important for cost-utility analysis for treatment and other allocation of health funding.
There is a lack of validated instruments that measure utility specific to glaucoma patients. Evidence suggests that generic utility measures, although widely used in health research, lack sensitivity to evaluate glaucoma-related impairment,73–75 though one study has used such measures to determine treatment differences in the context of angle closure.5
Various strategies of economic modeling for chronic glaucoma service delivery have used different methods for estimating utility values.52,76–79 There has not been a consistent approach, or optimal study design, for generating these values. This represents a significant knowledge gap for health economic modeling for chronic glaucoma.
In glaucoma, the tools used to calculate utility in previous studies have been calculated by conjoint analysis (in which patients have to rank various attributes eg darkness vision, peripheral vision, and glare in terms of which matter most to them) and time trade-off (TTO) analysis (asking patients how much lifespan they would trade for perfect vision) (Table 1).45,48,74,80 High frequencies of ceiling effects, which can lead to underestimating lifestyle impact, have been reported for UV instruments among these studies; furthermore poor correlation between TTO and conjoint analysis has been reported.73 TTO has been shown to correlate with moderate to severe glaucoma, while remaining relatively insensitive to early disease, which can produce significant anxiety and treatment side effects even when vision remains intact.49,81
Alternatively, the utility can be assessed by a multiattribute utility instrument that comprises a descriptive classification system composed of several attributes describing QoL with associated levels of increasing severity.50 A scoring algorithm is then used to assign UVs to each health state described by the instrument. Currently, there is a lack of validated multiattribute utility instruments that measure utility specific to patients with glaucoma.52,79 Work by one of the co-authors (EL) is however underway to develop a glaucoma-specific utility instrument using a discrete choice experiment (DCE) methodology to enhance the scientific rigor of the instrument. This DCE presents to participants with a series of choice sets containing 2 or more health states, each made up of glaucoma-specific QoL dimensions with varying levels of difficulty.
LIMITATIONS OF UTILITY ANALYSIS
UVs and QALYs are not the only options for evaluating health economic impact. The disability-adjusted life year (DALY) is an alternative to QALY. The DALY is a standardized quantitative measure of the burden of disease that combines morbidity with mortality and permits comparison of untreated with treated disease.82 DALYs can be used to compare cost-effectiveness of disease treatment with prevention strategies. Aside from having opposite signs DALYs and QALYs are interchangeable; DALYs have become the accepted way to quantify global disease burden. Synonymous with UVs for QALYs, DALYs are calculated using disability weights (DWs). Unlike UVs DWs are derived from one process applied to all diseases and health states, and hence not suited to create a glaucoma-specific measure.83
Like QALYs, DALYs primarily focus on activity limitation from visual impairment (which becomes a primary driver of QoL only in moderate/advanced glaucoma) and do not address other factors such as treatment-related side effects, disease-related anxiety, and burden of treatment and monitoring important in early glaucoma.
Future studies will need to consider QALYs and DALYs as tools, capable of providing a single measure of morbidity in the assessment of glaucoma interventions and treatments.
Health economic models evaluating the impact of timing and frequency of monitoring and/or interventions, as well as opportunity costs might ultimately be more useful than calculating QALYs or DALYs.79
The accurate measurement of the impact of glaucoma and its management on patients, carers, and society is imperative. Advances in clinical care delivery require better metrics to evaluate their quality of life impact and cost-effectiveness. The recommendations and discussion outlined in this paper aim to serve as a starting point for future work to measure and drive improved outcomes in glaucoma research.
1. Nelson EC, Eftimovska E, Lind C, et al. Patient reported outcome measures in practice. BMJ. 2015;350:g7818.
2. Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34:557–565.
3. Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organizations in an oncologic setting. BMC Health Serv Res. 2013;13:211.
4. Varma R, Richman EA, Ferris FL III, et al. Use of patient-reported outcomes in medical product development: a report from the 2009 NEI/FDA Clinical Trial Endpoints Symposium. Invest Ophthalmol Vis Sci. 2010;51:6095–6103.
5. Azuara-Blanco A, Burr J, Ramsay C, et al. Effectiveness of early lens extraction for the treatment of primary angle-closure glaucoma (EAGLE): a randomized controlled trial. Lancet. 2016;388:1389–1397.
6. Vickerstaff V, Ambler G, Bunce C, et al. Statistical analysis plan for the Laser-1st versus Drops-1st for Glaucoma and Ocular Hypertension Trial (LiGHT): a multi-center randomized controlled trial. Trials. 2015;16:517.
7. Tuulonen A. Economic considerations of the diagnosis and management for glaucoma in the developed world. Curr Opin Ophthalmol. 2011;22:102–109.
8. Lavia C, Dallorto L, Maule M, et al. Minimally-invasive glaucoma surgeries (MIGS) for open angle glaucoma: a systematic review and meta-analysis. PloS One. 2017;12:e0183142.
9. Viswanathan AC, McNaught AI, Poinoosawmy D, et al. Severity and stability of glaucoma: patient perception compared with objective measurement. Arch Ophthalmol. 1999;117:450–454.
10. Glen FC, Crabb DP. Living with glaucoma: a qualitative study of functional implications and patients’ coping behaviors. BMC Ophthalmol. 2015;15:128.
11. Nelson P, Aspinall P, Papasouliotis O, et al. Quality of life in glaucoma and its relationship with visual function. J Glaucoma. 2003;12:139–150.
12. Wright BD, Linacre JM. Observations are always ordinal; measurements, however, must be interval. Arch Phys Med Rehab. 1989;70:857–860.
13. Rasch G. Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research. Copenhagen: University of Chicago Press; 1960.
14. Khadka J, Pesudovs K, McAlinden C, et al. Reengineering the glaucoma quality of life-15 questionnaire with rasch analysis. Invest Ophthalmol Vis Sci. 2011;52:6971–6977.
15. Khadka J, Gothwal VK, McAlinden C, et al. The importance of rating scales in measuring patient-reported outcomes. Health Qual Life Outcomes. 2012;10:80.
16. Dougherty BE, Nichols JJ, Nichols KK. Rasch analysis of the Ocular Surface Disease Index (OSDI). Invest Ophthalmol Vis Sci. 2011;52:8630–8635.
17. Svensson E. Guidelines to statistical evaluation of data from rating scales and questionnaires. J Rehab Med. 2001;33:47–48.
18. Mallinson T. Why measurement matters for measuring patient vision outcomes. Optom Vis Sci. 2007;84:675–682.
19. Janz NK, Wren PA, Lichter PR, et al. Quality of life in newly diagnosed glaucoma patients: the collaborative initial glaucoma treatment study. Ophthalmology. 2001;108:887–897; discussion 898.
20. Mills RP, Janz NK, Wren PA, et al. Correlation of visual field with quality-of-life measures at diagnosis in the Collaborative Initial Glaucoma Treatment Study (CIGTS). J Glaucoma. 2001;10:192–198.
21. Mills T, Law SK, Walt J, et al. Quality of life in glaucoma and three other chronic diseases: a systematic literature review. Drugs Aging. 2009;26:933–950.
22. Pollard WE, Bobbitt RA, Bergner M, et al. The Sickness Impact Profile: reliability of a health status measure. Med Care. 1976;14:146–155.
23. Parrish RK II, Gedde SJ, Scott IU, et al. Visual function and quality of life among patients with glaucoma. Arch Ophthalmol. 1997;115:1447–1455.
24. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30:473–483.
25. Wilson MR, Coleman AL, Yu F, et al. Functional status and well-being in patients with glaucoma as measured by the Medical Outcomes Study Short Form-36 questionnaire. Ophthalmology. 1998;105:2112–2116.
26. Mangione CM, Phillips RS, Seddon JM, et al. Development of the ‘Activities of Daily Vision Scale.’ A measure of visual functional status. Med Care. 1992;30:1111–1126.
27. Sherwood MB, Garcia-Siekavizza A, Meltzer MI, et al. Glaucoma’s impact on quality of life and its relation to clinical indicators. A pilot study. Ophthalmology. 1998;105:561–566.
28. Cassard SD, Patrick DL, Damiano AM, et al. Reproducibility and responsiveness of the VF-14. An index of functional impairment in patients with cataracts. Arch Ophthalmol. 1995;113:1508–1513.
29. Sloane ME, Ball K, Owsley C, et al. The visual activities questionnaire: developing an instrument for assessing problems in everyday visual tasks. Noninvasive Assessment of the Visual System: Summaries of Papers. Washington DC: Optical Society of America; 1992:26–29.
30. Nassiri N, Mehravaran S, Nouri-Mahdavi K, et al. National eye institute visual function questionnaire: usefulness in glaucoma. Optom Vis Sci. 2013;90:745–753.
31. Mangione C, Lee PP, Gutierrez PR, et al. National eye institute visual function questionnaire field test investigators. Development of the 25-item National Eye Institute visual function questionnaire. Arch Ophthalmol. 2001;119:1050–1058.
32. Mangione CM, Lee PP, Pitts J, et al. Psychometric properties of the National Eye Institute Visual Function Questionnaire (NEI-VFQ). NEI-VFQ Field Test Investigators. Arch Ophthalmol. 1998;116:1496–1504.
33. Gutierrez PWM, Johnson C, Gordon M, et al. Influence of glaucomatous visual field loss on health-related quality of life. Arch Ophthalmol. 1997;115:777–784.
34. Jampel H, Friedman DS, Quigley H, et al. Correlation of the binocular visual field with patient assessment of vision. Invest Ophthalmol Vis Sci. 2002;43:1059–1067.
35. Medeiros FA, Gracitelli CP, Boer ER, et al. Longitudinal changes in quality of life and rates of progressive visual field loss in glaucoma patients. Ophthalmology. 2015;122:293–301.
36. Kowalski JW, Rentz AM, Walt JG, et al. Rasch analysis in the development of a simplified version of the national eye institute visual-function questionnaire-25 for utility
estimation. Qual Life Res. 2012;21:323–334.
37. Pesudovs K, Gothwal VK, Wright T, et al. Remediating serious flaws in the National Eye Institute Visual Function Questionnaire. J Cataract Refract Surg. 2010;36:718–732.
38. Lee BL, Gutierrez P, Gordon M, et al. The Glaucoma Symptom Scale. A brief index of glaucoma-specific symptoms. Arch Ophthalmol. 1998;116:861–866.
39. Rossi GC, Pasinetti GM, Scudeller L, et al. Ocular surface disease and glaucoma: how to evaluate impact on quality of life. J Ocular Pharmacol Therap. 2013;29:390–394.
40. Rossi GC, Pasinetti GM, Scudeller L, et al. Risk factors to develop ocular surface disease in treated glaucoma or ocular hypertension patients. Eur J Ophthalmol. 2013;23:296–302.
41. Skalicky S, Goldberg I. Depression and quality of life in patients with glaucoma: a cross-sectional analysis using the Geriatric Depression Scale-15, assessment of function related to vision, and the Glaucoma Quality of Life-15. J Glaucoma. 2008;17:546–551.
42. Goldberg I, Clement CI, Chiang TH, et al. Assessing quality of life in patients with glaucoma using the Glaucoma Quality of Life-15 (GQL-15) questionnaire. J Glaucoma. 2009;18:6–12.
43. Nelson P, Aspinall P, O’Brien C. Patients’ perception of visual impairment in glaucoma: a pilot study. Br J Ophthalmol. 1999;83:546–552.
44. Skalicky SE, Goldberg I, McCluskey P. Ocular surface disease and quality of life in patients with glaucoma. Am J Ophthalmol. 2012;153:1–9.
45. Gupta V, Srinivasan G, Mei SS, et al. Utility
values among glaucoma patients: an impact on the quality of life. Br J Ophthalmol. 2005;89:1241–1244.
46. Paletta Guedes RA, Paletta Guedes VM, Freitas SM, et al. Utility
values for glaucoma in Brazil and their correlation with visual function. Clin Ophthalmol. 2014;8:529–535.
47. Saw SM, Gazzard G, Au Eong KG, et al. Utility
values in Singapore Chinese adults with primary open-angle and primary angle-closure glaucoma. J Glaucoma. 2005;14:455–462.
48. Sun X, Zhang S, Wang N, et al. Utility
assessment among patients of primary angle closure/glaucoma in China: a preliminary study. Br J Ophthalmol. 2009;93:871–874.
49. Bozzani FM, Alavi Y, Jofre-Bonet M, et al. A comparison of the sensitivity of EQ-5D, SF-6D and TTO utility
values to changes in vision and perceived visual function in patients with primary open-angle glaucoma. BMC Ophthalmol. 2012;12:43.
50. Finger RP, Kortuem K, Fenwick E, et al. Evaluation of a vision-related utility
instrument: the German vision and quality of life index. Invest Ophthalmol Vis Sci. 2013;54:1289–1294.
51. McKean-Cowdin R, Wang Y, Wu J, et al. Impact of visual field loss on health-related quality of life in glaucoma: the Los Angeles Latino Eye Study. Ophthalmology. 2008;115:941–948.
52. Goh RL, Fenwick E, Skalicky SE. The visual function questionnaire: utility
index: does it measure glaucoma-related preference-based status? J Glaucoma. 2016;25:822–829.
53. Jones L, Garway-Heath DF, Azuara-Blanco A, et al. and United Kingdom Glaucoma Treatment Study I. Are patient self-reported outcome measures (PROMs) sensitive enough to be used as endpoints in clinical trials? Evidence from the United Kingdom Glaucoma Treatment Study. Ophthalmology. 2018. [Epub ahead of print].
54. Gothwal VK, Reddy SP, Bharani S, et al. Impact of glaucoma on visual functioning in Indians. Invest Ophthalmol Vis Sci. 2012;53:6081–6092.
55. Walt JG, Rendas-Baum R, Kosinski M, et al. Psychometric evaluation of the Glaucoma Symptom Identifier. J Glaucoma. 2011;20:148–159.
56. McDonald L, Glen FC, Taylor DJ, et al. Self-monitoring symptoms in glaucoma: a feasibility study of a web-based diary tool. J Ophthalmol. 2017;2017:8452840.
57. Pesudovs K. Item banking
: a generational change in patient-reported outcome measurement. Optom Vis Sci. 2010;87:285–293.
58. Bjorner JB, Chang CH, Thissen D, et al. Developing tailored instruments: item banking
and computerized adaptive assessment. Qual Life Res. 2007;16 (Suppl 1):95–108.
59. Khadka J, McAlinden C, Craig JE, et al. Identifying content for the glaucoma-specific item bank to measure quality-of-life parameters. J Glaucoma. 2015;24:12–19.
60. Matsuura M, Hirasawa K, Hirasawa H, et al. Developing an item bank to measure quality of life in individuals with glaucoma, and the results of the interview with patients: the effect of visual function, visual field progression rate, medical, and surgical treatments on quality of life. J Glaucoma. 2017;26:e64–e73.
61. Mabuchi F, Yoshimura K, Kashiwagi K, et al. High prevalence of anxiety and depression in patients with primary open-angle glaucoma. J Glaucoma. 2008;17:552–557.
62. Crabb DP. A view on glaucoma—are we seeing it clearly? Eye. 2016;30:304–313.
63. Ramulu P. Glaucoma and disability: which tasks are affected, and at what stage of disease? Curr Opin Ophthalmol. 2009;20:92–98.
64. Ramulu PY, van Landingham SW, Massof RW, et al. Fear of falling and visual field loss from glaucoma. Ophthalmology. 2012;119:1352–1358.
65. Lorenzana L, Lankaranian D, Dugar J, et al. A new method of assessing ability to perform activities of daily living: design, methods and baseline data. Ophthalmic Epidemiol. 2009;16:107–114.
66. Ekici F, Loh R, Waisbourd M, et al. Relationships between measures of the ability to perform vision-related activities, vision-related quality of life, and clinical findings in patients with glaucoma. JAMA Ophthalmol. 2015;133:1377–1385.
67. Altangerel U, Spaeth GL, Steinmann WC. Assessment of function related to vision (AFREV). Ophthal Epidemiol. 2006;13:67–80.
68. Richman J, Lorenzana LL, Lankaranian D, et al. Importance of visual acuity and contrast sensitivity in patients with glaucoma. Arch Ophthalmol. 2010;128:1576–1582.
69. Warrian KJ, Katz LJ, Myers JS, et al. A comparison of methods used to evaluate mobility performance in the visually impaired. Br J Ophthalmol. 2015;99:113–118.
70. Skalicky SE, McAlinden C, Khatib T, et al. Activity limitation in glaucoma: objective assessment by the cambridge glaucoma visual function test. Invest Ophthalmol Vis Sci. 2016;57:6158–6166.
71. Goh RLZ, Kong YXG, McAlinden C, et al. Objective assessment of activity limitation in glaucoma with smartphone virtual reality goggles: a pilot study. Transl Vis Sci Technol. 2018;7:10.
72. Kymes SM. An introduction to decision analysis in the economic evaluation of the prevention and treatment of vision-related diseases. Ophthal Epidemiol. 2008;15:76–83.
73. Aspinall PA, Johnson ZK, Azuara-Blanco A, et al. Evaluation of quality of life and priorities of patients with glaucoma. Invest Ophthalmol Vis Sci. 2008;49:1907–1915.
74. Browne C, Brazier J, Carlton J, et al. Estimating quality-adjusted life years from patient-reported visual functioning. Eye. 2012;26:1295–1301.
75. Kobelt G, Jonsson B, Bergstrom A, et al. Cost-effectiveness analysis in glaucoma: what drives utility
? Results from a pilot study in Sweden. Acta Ophthalmol Scand. 2006;84:363–371.
76. Varma RL, Lee PP, Goldberg I, et al. An assessment of the health and economic burdens of glaucoma. Am J Ophthalmol. 2011;152:515–522.
77. Burr JM, Kilonzo M, Vale L, et al. Developing a preference-based Glaucoma Utility
Index using a discrete choice experiment. Optom Vis Sci. 2007;84:797–808.
78. van Gestel A, Webers CA, Beckers HJ, et al. The relationship between visual field loss in glaucoma and health-related quality-of-life. Eye. 2010;24:1759–1769.
79. Boodhna T, Crabb DP. More frequent, more costly? Health economic modelling aspects of monitoring glaucoma patients in England. BMC Health Serv Res. 2016;16:611.
80. Bhargava JS, Patel B, Foss AJ, et al. Views of glaucoma patients on aspects of their treatment: an assessment of patient preference by conjoint analysis. Invest Ophthalmol Vis Sci. 2006;47:2885–2888.
81. Jampel H, Schwartz A, Pollack I, et al. Glaucoma patients’ assessment of their visual function and quality of life. J Glaucoma. 2002;11:154–163.
82. Sassi F. Calculating QALYs, comparing QALY and DALY calculations. Health Policy Plan. 2006;21:402–408.
83. Salomon JA, Vos T, Hogan DR, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. Lancet. 2012;380:2129–2143.