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01 · ABSTRACT

Abstract

Aims: The aim of this study is to provide statistical inference of the sojourn time and the transition probability from the disease free to the preclinical state of lung cancer for male and female smokers using lung cancer data from the National Lung Screening Trial (NLST).

 

Materials and Methods: We applied a likelihood function to the lung cancer data, to obtain Bayesian inference of the transition probability and the sojourn time distribution. A log-normal distribution was used for the transition probability density function multiplied by 30%, and a Weibull distribution was used to model the sojourn time in the preclinical state.

 

Results: The estimate of screening sensitivity is 0.61 for males and 0.62 for females. Early transition happened before age 50 and lasted until after age 90. The transition probability from the disease free to the preclinical state has a single maximum at around age 73 for males and 72 for females. For male, the Bayesian posterior mean, and median sojourn time are 1.33 and 1.27 years, respectively. For female, the corresponding posterior mean, and median sojourn time are 1.23 and 1.21 years, respectively.

 

Conclusion: Our estimation showed that male smokers are more vulnerable to lung cancer, because they have a higher transition probability density than the same aged female smokers. The female smokers have a slightly shorter mean sojourn time than the male, meaning that they are quicker to develop clinical symptom of lung cancer.

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02 · PUBLICATION RECORD

Article details

JournalMedical Research Archives
IssueVol 9 No 5 (2021): Vol.9 Issue 5, May, 2021
SectionResearch Articles
Published25 May 2021
DOI10.18103/mra.v9i5.2399
ISSN2375-1924
03 · RIGHTS & REUSE

Rights & reuse

This article is published under a Creative Commons Attribution License (CC BY 3.0) and may be shared or distributed by anyone as long as attribution is given to the journal.

Authors & affiliations

FR

Farhin Rahman

Department of Bioinformatics and Biostatistics, University of Louisville, KY, USA

DW

Donfeng Wu

Department of Bioinformatics and Biostatistics, University of Louisville, KY, USA

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