The Quality of Life Via Semi Markov Reward Modelling

Zacharias Kyritsis () and Aleka Papadopoulou ()
Additional contact information
Zacharias Kyritsis: Aristotle University of Thessaloniki
Aleka Papadopoulou: Aristotle University of Thessaloniki

Methodology and Computing in Applied Probability, 2017, vol. 19, issue 4, 1029-1045

Abstract: Abstract The quality of life is recognized as an important element which is generally treated at least as primary or secondary criterion in most clinical trials. So, its measurement and statistical analysis remain an issue. In the present, a non-homogeneous semi Markov model is defined for the description of the evolution of a patient’s health status. Also, a stochastic process where the state space elements are the quality of life states of a patient and the transition probabilities are dependent on the health status is considered. A methodology via Markov modelling in order to combine the health and the quality of life status of a patient is provided. Next, we extend the above model by attaching rewards of making transitions between the states of the two kinds for indexing through rewards the quality of life for patients of all health states. The mean, the second non central moment, the variance and the high order moments of a patients interval reward are calculated by means of the basic parameters of the semi Markov chain and the quality status process. The above results provide us with a tool which could operate as a measure for indicating the quality of life status. Finally, the theoretical results are illustrated numerically with synthesized data.

Keywords: Semi markov; Quality of life; Rewards; 60K15; 60J10; 60J20 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11009-016-9516-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:19:y:2017:i:4:d:10.1007_s11009-016-9516-5

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-016-9516-5

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().