The Importance of Quality-of-Life Research

Clinical studies involving HRQOL endpoints have remained a niche pursuit over the past several decades owing to a variety of conceptual and methodological challenges. Enhanced interest in such studies is now driven by several powerful factors, including patient consumerism, product marketing, health economics, regulatory evolution and recent study results that have revealed how predictive HRQOL measures can be. New information technologies are available that can support cost-effective deployment of HRQOL studies longitudinally over large populations. Leading outcomes research organizations are creating new collaborative associations to increase the speed and efficiency of HRQOL clinical research.

Printer-friendly Version

There are no comments so far.

If you signup and login, you can post comments.

Clinical versus experiential data

Clinical trials have traditionally utilized measures of morbidity and mortality as primary endpoints, with therapeutic efficacy assessed in terms of clinical findings such as physical exams, lab results, radiographic images and physician-assessed patient functional states. The late 1970s witnessed the beginnings of quality-of-life studies, in which psychometric tools were used to quantify patient attitudes, symptoms, preferences and functional status directly from self-reported patient information.

Collection techniques for these data subsequently evolved from structured interviews into formalized questionnaires of two primary types: lengthy multidimensional general health surveys (e.g. the SF-36 (1)), and focused disease-specific questionnaires (e.g. the Seattle Angina Questionnaire (2)). Depending on the specific instrument, both types of questionnaire generate both raw scores and calculated ‘scales’, which are often normalized to a 0–100 range. In addition, ‘utility’ measures (3) have been developed to assess patient preferences by posing choices between hypothetical health status alternatives (e.g. the ‘standard gamble’ and ‘time tradeoff’ measures). Utility measures are reported on a scale of 0–1 and, when coupled with lifeexpectancy data, yield quality-adjusted life years (4), which are used in cost-effectiveness calculations.

Questions of performance, validity and meaning

Some substantive conceptual challenges lurk behind this conversion of subjective patient attitudes into objective numeric values. The process of self-reporting leaves the interpretation of questions up to the literacy, comprehension and whim of the patient. The degree of fresh attention the patient brings to each performance of the questionnaire depends on its length and complexity. If the instrument is very long, the patient may skip through it, while if it is short, the patient might ‘learn’ his/her responses and tend to repeat them. Because sequentially administered HRQOL questionnaires are usually given at intervals of 2 weeks or more, the temporal granularity of the phenomena they measure is quite coarse. Even optimally completed questionnaires must be rigorously validated against external measurements in order for patient scores to be meaningful. In particular, the magnitude of change that gives clinical significance requires specific determination during validation (5).

HRQOL measures also face daunting methodological and logistical challenges. When responding to paper form questionnaires, patients can easily omit responses to questions or mark multiple responses when only one is appropriate. Error detection and correction either occurs late via a patient callback, which introduces bias, or by the use of a missing-value imputation algorithm, which introduces a different bias. Calculation of dimension/scale scores can involve complicated, error-prone algorithms requiring special staff expertise. The distribution and collection of paper forms involves many steps, causing increased costs. Data transcription into a computer database requires either expensive data-entry staff or scanning and optical character-recognition systems, both of which are subject to errors. If not assiduously countered, these logistical problems can cause HRQOL evaluations to be not only subjective and hard to interpret, but also fundamentally flawed and misleading.

Niche applications of HRQOL

Despite—or perhaps because of—these challenges, HRQOL research has occupied a small, but growing, niche in the clinical research world. Current MEDLINE data reveal that quality-of-life publications constituted 0.1% of all indexed publications in 1980, 0.4% in 1990 and still only about 0.9% now (6). Much of the literature describes the development of new questionnaires and their use in descriptive, cross-sectional and cohort studies to characterize populations defined by geographic, demographic or diagnostic criteria. More recently, randomized, interventional studies using HRQOL measures have joined the literature, constituting about 3% of all HRQOL studies now (6). Although the growth rate of published HRQOL research is somewhat higher than that of general publications, the absolute number of studies remains fairly low.

Reasons behind increased HRQOL research

Several ongoing developments are increasing the level of interest in HRQOL research.

Pharmaceutical and device manufacturers

In the current aggressively competitive marketplace, manufacturers have unprecedented need to differentiate their products from those of their competitors. This is most apparent in ‘lifestyle’ drug categories such as antidepressants, ulcer medications and anti-inflammatory agents. To this end, product manufacturers are adding HRQOL information to marketing campaigns, particularly those directed at the consumer (DTC advertising). HRQOL data also often form part of the pitch to large purchasers such as managed care organizations and pharmacy benefits managers. HRQOL data have typically been collected—though often not analyzed if the primary endpoints were significant— during studies for new drug applications (NDAs). The fact that one new technology (laser transmyocardial revascularization) won US Food and Drug Administration (FDA) approval due in a large part to the HRQOL data collected suggests that such data will likely play a larger role in future NDAs.

Regulatory agencies

Faced with pressure from product manufacturers to accept HRQOL data for advertising and labeling claims, the FDA and the European Agency for the Evaluation of Medicinal Products are in the process of formulating new guidance statements. FDA policy will likely stipulate standards for how HRQOL instruments are designed and validated, how HRQOL measures are integrated and analyzed with other data, and how specific HRQOL results are converted into labeling claims (7) (8).

Consumers

Spurred by DTC advertisements and empowered by Internet consumer health sites, patients are seeking out more information on therapeutic products and are wielding that information during treatment discussions with their physicians. The HRQOL benefits of products frequently assume much greater importance in patients’ decision making than standard clinical measures. Patients will increasingly demand to know quantitatively and precisely how they can expect to be impacted by the therapeutics they take.

Healthcare payors

Employers, insurers and the government increasingly look for detailed economic analyses of the healthcare products and services they purchase. Quality-adjusted life-year calculations from utility measures are used to form key components of cost-effectiveness and cost-utility analyses (4).

Providers

Physicians are beginning to realize the value of collecting performance measures and patient outcomes data to generate quality indicators for use during contract negotiations and marketing campaigns. HRQOL data collected longitudinally in a physician’s patient population could constitute powerful evidence of the quality of care the physician provides. However, revenue models to support such data collection are currently not clear, and until they are, physicians are unlikely to perform such studies in their practices. The new crop of disease-management companies that contract for the care of specialized patient populations based on diagnostic ‘carve-outs’ do have a compelling need to quantify their program’s cost-effectiveness for both marketing and internal program management purposes. HRQOL studies could form important components of such return-on-investment metrics.

Researchers

Recent work has shown how remarkably predictive carefully validated disease-specific HRQOL instruments can be in tracking patient health status responsively and reliably. The Seattle Angina Questionnaire (2) (9) and Kansas City Cardiomyopathy Questionnaire (10) are demonstrably as reproducible (or more so) for heart patients as some of the traditional ‘hard data’ measures relied upon by cardiologists (e.g. treadmill tests and ejection fractions). They can accurately identify high-risk and high-cost patient subpopulations (11). Next-generation developments in HRQOL instrument design include minimizing the length of questionnaires to reduce measurement burden, controlling for language and cultural variables, and defining dynamic, computer-adaptive testing algorithms (12). The incorporation of such validated instruments into central endpoints of international, long-term randomized controlled trials such as the COURAGE trial (a comparison of percutaneous coronary revascularization to medical therapy) (13) is raising the profile of HRQOL measures in such research.

Demand for large-scale, repeated assessment of HRQOL data thus appears to be emerging in a wide variety of contexts, from traditional clinical trial settings to clinical practice facilities where clinical research might not previously have been performed.

Using information technology in HRQOL research

The traditional interview- and paper form-based methods of HRQOL research do not scale well because of the high costs, logistical complexity and risk of errors involved. Most organizations lack the infrastructure and the large number of trained staff required by standard techniques. Fortunately, a palette of new information technologies makes innovative solutions possible that increase the ease, speed and reliability of HRQOL studies, while cutting their cost.

Scanned forms

Numerous vendors offer methods by which HRQOL data can be scanned into a digital format for importation into a database. More complete solutions include tools for form creation, remote scanning and e-mail transport, and scanning-error detection and adjudication. These systems reduce data entry time but provide no mechanism to reduce errors introduced by patients.

Automated telephone systems

Various programmable telephone systems are available to collect HRQOL data in an oral interview fashion. By using the virtually ubiquitous telephone, these systems can reach nearly any patient (as long as the patient is not deaf). Patient responses keyed in via the telephone can be digitally captured without additional effort. However, HRQOL questions consisting of multiple-choice responses impose a potentially excessive burden on patients’ short-term memories. Unless the instrument has been validated for this use, the data may be suspect, or at least not directly comparable to those derived from other methods (14).

PC systems

Many experimental and commercial systems using personal computers—often with a touch-screen monitor to simplify user interactivity via a kiosk format—have been created (15) (16) (17) (18) (19) (20) (21). Error detection and correction routines are typically part of these programs, as is support for complex instruments with skip logic and visual scales (such as some utility measures). Published evaluations uniformly confirm the ease, speed and accuracy with which these systems collect digital data. However, supporting these PC systems may be a more complicated task than many sites can handle, and many facilities find that on-site patient use of PCs imposes objectionable logistical bottlenecks to smooth patient flow. Further, multi-site deployments are not well served by separate stand-alone PCs, nor do they allow system access by patients in their homes.

Palm-top systems

Several experimental and commercial systems using hand-held computers exist (22). These vendors primarily target professional data-entry staff with ‘electronic case report form’ products, but data collection tools for direct patient use are also available.

Internet-based systems

Numerous custom and commercial Internet-based clinical trial tools exist (13) (23) (24) (25) (26) (27) (28), and many consumer-oriented health sites provide access to HRQOL instruments as well. These solutions offer all the interactivity and error-control advantages of PC systems, plus they decentralize test administration so that patients can perform the tests wherever and whenever it is convenient to do so. The collection of large, geographically distributed data-sets directly into study databases is thus possible, as is near-realtime data analysis. However, despite the recent rapid increase in the number of people with Internet access, many important subpopulations remain unconnected. The ill, elderly and enfeebled—likely the most important subjects of clinical research—are typically the ones least likely to be on the right side of the ‘digital divide’. These patients will be more easily queried via telephone and mailed forms.

Clearly, no one system is yet ideal, but all have a useful role in certain contexts. This table charts each alternative against important criteria to consider when collecting HRQOL data. Over time, the accelerating convergence within telecommunication and computing technologies will no doubt radically shape the evolution of available solutions.

Feature Paper forms Scanned forms Automated telephone PC Palm-top Internet
Decentralized point of data collection Yes Yes Yes No Maybe Yes
Patient error/omission detection and correction No No Yes Yes Yes Yes
Digital data conversion error risk High Medium Low Low Low Low
Setup costs Low Medium High (build), Medium (buy) Medium–high Medium–high High (build), Medium (buy)
Recurring costs High Medium–high Low Low Low Low
Organizational infrastructural sophistication Low Medium Low Low Low High
Organizational support capabilities High Medium–high Low Medium–high Medium–high Low

Emerging collaborative outcomes research organizations

Harnessing innovative information technologies to deliver cost-effective data is only part of the solution, however. Long-term, longitudinal measurement of the broader effects of therapeutic interventions will draw from areas of patient referral much larger than those of individual academic medical centers or contract research organizations. Timely recruiting and retention of adequate numbers of appropriate patients will require new processes and procedures. Performing these studies will be best accomplished by site management organizations with national scope or innovative ‘virtual’ collaborative groups such as the new Cardiovascular Outcomes Research Consortium (CORC). CORC brings together the outcomes research sections of most of the principal leaders in US cardiac HRQOL studies, including Yale (CT), Emory (GA), Stanford (CA), Northwestern (IL), Duke (NC), the University of Texas, Case Western Reserve University (OH), Mid America Heart Institute (MO), the University of Missouri- Kansas City and the University of Iowa. CORC’s goal is to create an information infrastructure through which its members can solve the remaining conceptual and methodological problems with HRQOL research. CORC expects its efforts to elevate the discipline to new levels of authoritativeness and influence.

Conclusions

Several decades of small-scale health-related quality-of-life studies have revealed that, despite certain conceptual issues, HRQOL data can produce vital information about patients’ interaction with illness and their response to treatment. Current integration of HRQOL data into central endpoints of randomized controlled clinical trials is now possible due to the creation and validation of reliable disease-specific instruments. Rapidly evolving information technologies will support cost-effective performance of HRQOL research at a scope previously unthinkable. Innovative new collaborations among visionary investigators will direct the discipline’s evolution toward a ‘technology of patient experience’ (29).

References

  1. Ware J, Sherbourne C. The MOS 36-item short-form health survey (SF-36): Conceptual framework and item selection. Med Care 1992;30:473–83.
  2. Spertus JA, Winder JA, Dewhurst TA et al. Development and evaluation of the Seattle Angina Questionnaire: A new functional status measure for coronary artery disease. J Am Coll Cardiol 1995;25:333–41.
  3. Revicki DA, Kaplan RM. Relationship between psychometric and utility-based approaches to the measurement of health-related quality of life. Qual Life Res 1993;2:477–87.
  4. Raisch DW. Understanding quality-adjusted life years and their application to pharmacoeconomic research. Ann Pharmacother 2000;34:906–14.
  5. Hays RD, Woolley JM. The concept of clinically meaningful difference in health-related quality-of-life research. How meaningful is it? Pharmacoeconomics 2000;18:419–23.
  6. Entrez-Pubmed. Available from: URL: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi. Date accessed: April 24, 2001.
  7. Lewis C. Grappling with the quality of life: Patients, FDA and drug companies struggle to link therapies with well-being. FDA Consumer Magazine 2001, March–April. Available from: URL: http://www.fda.gov/fdac/features/2001/201_life.html.
  8. Burke LB. Acceptable evidence for pharmaceutical advertising and labeling. DIA workshop on Pharmacoeconomic and Quality of Life Labeling and Marketing Claims; 2000 Oct 3; New Orleans (LA). Available from: URL: http://www.fda.gov/cder/ddmac/brklDIA1000/index.htm.
  9. Dougherty CM, Dewhurst T, Nichol WP et al. Comparison of three quality of life instruments in stable angina pectoris: Seattle Angina Questionnaire, Short Form Health Survey (SF-36), and Quality of Life Index-Cardiac Version III. J Clin Epidemiol 1998;51:569–75.
  10. Green CP, et al. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: A new health status measure for heart failure. J Am Coll Cardiol 2000;35:1245–55.
  11. Jones PG, Spertus JA, McDonnell M et al. Health status predicts long-term outcome in patients with coronary artery disease. J Am Coll Cardiol 2001;37(Suppl. A):491A.
  12. Revicki DA, Cella DF. Health status assessment for the twenty-first century: Item response theory, item banking and computer adaptive testing. Qual Life Res 1997;6:595–600.
  13. Spertus JA, et al. New frontiers in assessing outcomes for clinical trials: Implementation of computerized health status/utility assessment in the COURAGE trial. J Am Coll Cardiol 2001;37(Suppl. A):498A.
  14. McHorney CA, Kosinski M, Ware JE. Comparisons of the costs and quality of norms for the SF-36 health survey collected by mail versus telephone interview: Results from a national survey. Med Care 1994;32:551–67.
  15. Alemi F, Higley P. Reaction to “talking” computers assessing health risks. Med Care 1995;33:227–33.
  16. Detmar SB, Aaronson NK. Quality of life assessment in daily clinical oncology practice: A feasibility study. Eur J Cancer 1998;34:1181–6.
  17. Ellis LB, Joo HY, Gross CR. Use of a computer-based health risk appraisal by older adults. J Fam Pract 1991;33:390–4.
  18. Golden WE. Health status measurement. Implementation strategies. Med Care 1992;30(Suppl. 5):MS187–95. Discussion in: Med Care 1992;30(Suppl. 5):MS196–209.
  19. Henry SB, Lush M, Constantino M et al. Health status measurement in computer-based patient record systems. Nurs Adm Q 1997;21:50–60.
  20. Lenert LA, Hornberger JC. Computer-assisted quality of life assessment for clinical trials. Proc AMIA Annu Fall Symp 1996:992–6.
  21. Lofland JH, Schaffer M, Goldfarb N. Evaluating health-related quality of life: Cost comparison of computerized touch-screen technology and traditional paper systems. Pharmacotherapy 2000;20:1390–5.
  22. Le PP, Kohane IS, Weeks JC. Using a pen-based computer to collect health-related quality of life and utilities information. Proc Annu Symp Comput Appl Med Care 1995:839–43.
  23. Bliven BD, Kaufman SE, Spertus JA. Electronic collection of health-related quality of life data: Validity, time benefits, and patient preference. Qual Life Res (In press).
  24. Kaufman SE, Spertus J. The WebRes Project: Web-based client-server technologies for multicenter clinical research. Proc Annu Symp Comput Appl Med Care 1995:990.
  25. Kaufman S, Spertus J. A Java-based World Wide Web computer system for longitudinal assessment of health outcome measures using the Seattle Angina Questionnaire. J Am Coll Cardiol 1997;29(Suppl. A):9A.
  26. Lenert LA. The reliability and internal consistency of an Internet-capable computer program for measuring utilities. Qual Life Res 2000;9:811–7.
  27. Lenert LA. iMpact3: Online tools for development of web sites for the study of patients’ preferences and utilities. Proc AMIA Symp 2000:1172.
  28. Soetikno RM, Mrad R, Pao V et al. Quality-of-life research on the Internet: Feasibility and potential biases in patients with ulcerative colitis. J Am Med Inform Assoc 1997;4:426–35.
  29. Ellwood PM. Shattuck lecture: Outcomes management, a technology of patient experience. N Engl J Med 1988;318:1549–56.

Created: January 12, 2009 16:19; Last updated: February 07, 2009 12:31