Design and analysis of trials with quality of life as an outcome: a practical guide

Abstract

Health Related Quality of Life (HRQoL) measures are becoming more frequently used in clinical trials, as both primary and secondary endpoints. Investigators are now asking statisticians for advice on how to plan (e.g., sample size) and analyze studies using HRQoL measures. HRQoL measures such as the SF-36 are usually measured on an ordered categorical (ordinal) scale. In the designing stages and when analyzing, the scales are often scored and the scores treated as if they were continuous and normally distributed. However the ordinal scaling of HRQoL measures leads to problems in determining sample size, and conventional parametric methods of estimation and hypothesis testing may not be appropriate for such outcomes. We present practical guidelines for the design and analysis of trials with HRQoL measures as outcomes. We used conventional statistical methods (i.e., t-tests and multiple regression), various ordinal regression models (proportional odds, continuation ratio, polytomous and stereotype) and bootstrap methods to analyze an HRQoL dataset. To illustrate the various methods we used HRQoL data on the SF-36 Role Limitations Emotional dimension for two groups of patients with leg ulcers. The bootstrap, t-test, and multiple regression methods gave similar results. The various ordinal regression models also gave similar results. If the HRQoL measure has a large number of ordered categories, most of which are occupied, and the underlying scale really is continuous but measured imperfectly by an instrument with a limited number of discrete values, then an informal rule of thumb is that this discrete scale should be treated as continuous if it has seven or more categories and as ordinal otherwise.