Quality of life is an important component in the evaluation of therapies, especially in advanced cancer. Methods available for the analysis of longitudinal quality-of-life data include linear mixed models (including growth curve models), generalized linear models, generalized estimating equations, and joint modeling of quality of life and the missingness process. Quality-adjusted survival (Q-TWiST) has also been useful to compare treatments. By weighting the durations of health states according to their quality of life, one arrives at a single end point reflecting the duration of survival and the quality of life. We propose methods for incorporating longitudinal quality-of-life data into quality-adjusted survival. We divide follow-up time into two states, "poor" and "good," based on a cut-off applied to observed quality-of-life scores. Disease progression is handled as a separate state. We then use survival analysis methods to estimate the mean duration of each state as well as mean quality-adjusted time. The analysis is repeated by varying the cut-off to illustrate the range of possible results. Finally a single summary analysis is achieved by averaging (possibly with weights) across the cut-offs used. We illustrate the methodology using data from a cancer clincial trial.