Abstract
The improvement in mortality rates in many countries over the world has a major impact on costs associated with living longer. The trend of life expectancy in Malaysia population has steadily increased. This is due to the advancement of medical technology and people awareness of their health risk. Cancer has been one of the leading causes of death in Malaysia for many years. There are four stages of lung cancer and the treatment for each lung cancer's stages may vary. If cancer were just spread in one place, the doctor may recommend a local treatment to get rid of the cancer completely. Whereas, if the cancer has spread to the other part of the body, more comprehensive treatments maybe needed thus will increase the costs of treatment. Therefore, it is crucial to estimate the transition probabilities between lung cancer stages to measure the accurate cost of treatment according to respective stages. The lack of extensive longitudinal data that trace the progression of cancer from one stage to more severe stages challenges the process of estimating transition probabilities of lung cancer. As an alternative, a specific method needs to be developed in order to make use of the existing cross-sectional data to estimate the probabilities. The objectives of this research are firstly, to analyse Malaysian cancer cases data by performing descriptive analysis using statistical method and secondly to compute the Cancer-free Life Expectancy for Malaysian population and compare with the life expectancy of Malaysian population.
Metadata
Item Type: | Thesis (Masters) |
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Creators: | Creators Email / ID Num. Omar, Muhammad Hakeem 2017717905 |
Contributors: | Contribution Name Email / ID Num. Thesis advisor Shair, Syazreen Niza UNSPECIFIED |
Subjects: | R Medicine > RA Public aspects of medicine > Medical care R Medicine > RC Internal Medicine > Cancer |
Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences |
Programme: | Master of Science (Actuarial Science) |
Keywords: | Mortality, lung cancer patients, Markov model |
Date: | 2022 |
URI: | https://ir.uitm.edu.my/id/eprint/75716 |
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