![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
Abstract
The quality of life of patients is an important component of evaluation of therapies. We present an overview of a statistical method called Q-TWiST (Quality-Adjusted Time Without Symptoms and Toxicity) which incorporates quality-of-life considerations into treatment comparisons. Multivariate censored survival data are used to partition the overall survival time into periods of time spent in a set of progressive clinical health states which may differ in quality of life. Mean health state durations, restricted to the follow-up limits of the clinical trial, are derived from the data and combined with value weights to estimate quality-adjusted survival. The methodology emphasizes treatment comparisons based on threshold utility analyses that highlight trade-offs between different health state durations; it is not intended to provide a unique result combining quality and quantity of life. We also describe three recent extensions of the methodology: covariates can be included using proportional hazards and accelerated failure time regression models, restricted estimates can be projected beyond follow-up limits using parametric models, and meta-analyses can be performed incorporating quality-of-life dimensions. The basic methods are demonstrated in an analysis of data from a clinical trial comparing long versus short duration adjuvant chemotherapy regimens for the treatment of breast cancer. The clinical health states are defined by the following three outcomes: (1) end of treatment toxicity, (2) disease recurrence, and (3) death. The results allow one to evaluate the trade-off between the increased toxic effects and the increased recurrence-free interval associated with the long duration treatment.
