Abstract
Quality of life (QOL) measures are becoming an integral part of the analysis of clinical trials data to determine the efficacy of interventions. A brief overview of the QOL measures and their corresponding methods of analysis is presented. Then, we propose a statistical model for a discrete QOL measure based on a first order homogeneous Markov process. Heuristically, the model incorporates covariates and allows for nonignorable censoring. Using the model, the efficacy of an intervention can be evaluated by comparing among the treatment groups the expected length of stay in the “good” QOL state in conjunction with the analysis of survival time.
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© 1996 Springer Science+Business Media Dordrecht
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Palesch, Y.Y., Gross, A.J. (1996). Statistical Models for Quality of Life Measures. In: Jewell, N.P., Kimber, A.C., Lee, ML.T., Whitmore, G.A. (eds) Lifetime Data: Models in Reliability and Survival Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5654-8_33
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DOI: https://doi.org/10.1007/978-1-4757-5654-8_33
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