Analyzing Quality of Life ( QOL ) Endpoints in Clinical Trials via the SAS System

The cure for many chronic diseases, such as cancer and AIDS, remains elusive. Clinical research increasingly has focused more on maximizing patient QOL because improvements in survival and response to treatment have become more difficult to achieve. Patient quality of life is rapidly becoming a standard endpoint in clinical treatment trials for such diseases. Analytical techniques for QOL endpoints differ from those used for the more traditional endpoints of survival and response to treatment. We will summarize the "state of the art" for the analysis of these psychosocial outcome measures and provide the requisite SAS algorithms. An overview of the technical background will precede a presentation of SAS macros for QOL tool performance assessment (reliability and v a l i d i t y ) c o m p a r i s o n a m o n g competing instruments (Bland and Altman, 1996), Q-TWiST analysis (Gelber, Goldhirsch and Cole, 1996), power analysis (Cohen, 1988), longitudinal analysis (Diggle, Liang and Zegher, 1994) and handling the problems of missing data in longitudinal studies. Example studies are drawn from Mayo/North Central Cancer Treatment Group experiences. QOL IN HEALTH CARE RESEARCH The purpose of this review article is to give the reader a broad overview of various statistical techniques that are presently being applied to QOL endpoints in health care research. The discussion will center largely in oncology research. The methodology, however, applies readily to other conditions, notably AIDS, in which the focus of the clinical effort is not so much to provide a cure but instead to enhance the quality of the time a patient has available. The impetus for the recent upsurge in QOL research activity can be traced back to publications regarding the "war on cancer" declared by President Nixon . Several publications subsequently reported that the war on cancer was not being won, in terms of incidence and mortality rates (Beardsley, 1994 for example). An oft-quoted passage in a seminal article in the New England Journal of Medicine (1987) tacitly summarized the state of cancer research in saying that "when the cure remains elusive it is time to focus on the patient not the disease" (Tannock, 1997). The amount of literature detailing QOL research has expanded enormously over the past five years (Sloan et al, 1998a). It is impossible to summarize the literature sufficiently in the space available here. Instead, we refer the reader to a small subset of literature that collectively would form an overview for the status of QOL research in 1997. If the reader could only have one book on QOL research it would have to be the tome of Spilker (1996) which provides a wealth of every aspect of QOL research. The book by Dimsdale and Baum (1995) would also serve the reader well in a concise format. There is a compendium for QOL literature and copies of tools on the worldwide web put together by Marcell Tamburini et al. at http://www.glam. com/ql/url.htm. There are a number of key journal resources that the QOL researcher may find useful. The August 1997 issue of Controlled Clinical Trials was almost entirely dedicated to QOL research. In 1996, the American Society for Clinical Oncology (ASCO) published guidelines for incorporation of QOL endpoints into clinical trials by stating that QOL is secondary only to survival in importance as an endpoint and was more important than tumor response. In 1996, the National Cancer Institute published a monograph summarizing the use of QOL in Cancer Clinical Trials (Klausner et al, 1996). The goals of this document were "a) to define elements of QOL that are relevant to clinical decision-making and serve as endpoints in clinical Statistics, Data Analysis, and Modeling