A quality-adjusted survival (Q-TWiST) model for evaluating treatments for advanced stage cancer.
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.
Cole
Bernard F
BF
Department of Community and Family Medicine, Dartmouth Medical School, Hanover, New Hamphire, USA. bernard.cole@dartmouth.edu
Gelber
Richard D
RD
Gelber
Shari
S
Mukhopadhyay
Pabak
P
D005910:Glioma / Q000401:mortality / Q000628:therapy*; D006801:Humans; D015233:Models, Statistical; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D016019:Survival Analysis