Literature DB >> 35936652

Estimation of Preclinical State Onset Age and Sojourn Time for Heavy Smokers in Lung Cancer.

Dongfeng Wu1, Shesh N Rai1, Albert Seow2.   

Abstract

Estimation of the three key parameters: onset age of the preclinical state, sojourn time and screening sensitivity is critical in cancer screening, since all other terms are functions of the three. A novel link function to connect sensitivity with time in the preclinical state and the likelihood method were used in this project; since sensitivity depends on how long one has entered the preclinical state relative to the total sojourn time. Simulations using Markov Chain Monte Carlo and maximum likelihood estimate were carried out to estimate the key parameters for male and female heavy smokers separately in the low-dose computed tomography group of the National Lung Screening Trial. Sensitivity for male and female heavy smokers were 0.883 and 0.915 respectively at the onset of the preclinical state, and increased to 0.972 and 0.981 at the end. The mean age to make the transition into the preclinical state was 70.94 or 71.15 for male and female heavy smokers respectively, and 90% of heavy smokers at risk for lung cancer would enter the preclinical state in age interval (55.7, 85.8) for males and (54.2, 87.7) for females, and the transition peaked around age 69 for both genders. The mean sojourn time in the preclinical state was 1.43 and 1.49 years, and the 99% credible intervals for the sojourn time were (0.21, 2.96) and (0.37, 2.69) years for male and female heavy smokers correspondingly. Based on the result, low-dose CT should be started at age 55 and ended before 85 for heavy smokers. This provided important information to policy makers.

Entities:  

Keywords:  Cancer screening; Heavy smoker; Low-dose computed tomography; Sensitivity; Sojourn time; Transition density

Year:  2022        PMID: 35936652      PMCID: PMC9355113          DOI: 10.4310/21-sii696

Source DB:  PubMed          Journal:  Stat Interface        ISSN: 1938-7989            Impact factor:   0.716


  17 in total

1.  The National Lung Screening Trial: overview and study design.

Authors:  Denise R Aberle; Christine D Berg; William C Black; Timothy R Church; Richard M Fagerstrom; Barbara Galen; Ilana F Gareen; Constantine Gatsonis; Jonathan Goldin; John K Gohagan; Bruce Hillman; Carl Jaffe; Barnett S Kramer; David Lynch; Pamela M Marcus; Mitchell Schnall; Daniel C Sullivan; Dorothy Sullivan; Carl J Zylak
Journal:  Radiology       Date:  2010-11-02       Impact factor: 11.105

2.  Sojourn time and lead time projection in lung cancer screening.

Authors:  Dongfeng Wu; Diane Erwin; Gary L Rosner
Journal:  Lung Cancer       Date:  2010-11-13       Impact factor: 5.705

3.  MLE and Bayesian inference of age-dependent sensitivity and transition probability in periodic screening.

Authors:  Dongfeng Wu; Gary L Rosner; Lyle Broemeling
Journal:  Biometrics       Date:  2005-12       Impact factor: 2.571

4.  Estimation of sensitivity depending on sojourn time and time spent in preclinical state.

Authors:  Seongho Kim; Dongfeng Wu
Journal:  Stat Methods Med Res       Date:  2012-11-04       Impact factor: 3.021

5.  Modeling growth of a heterogeneous tumor.

Authors:  Wei-Yin Chen; Phanidhar R Annamreddy; L T Fan
Journal:  J Theor Biol       Date:  2003-03-21       Impact factor: 2.691

6.  Reduced lung-cancer mortality with low-dose computed tomographic screening.

Authors:  Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks
Journal:  N Engl J Med       Date:  2011-06-29       Impact factor: 91.245

7.  Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

Authors:  P C Prorok; G L Andriole; R S Bresalier; S S Buys; D Chia; E D Crawford; R Fogel; E P Gelmann; F Gilbert; M A Hasson; R B Hayes; C C Johnson; J S Mandel; A Oberman; B O'Brien; M M Oken; S Rafla; D Reding; W Rutt; J L Weissfeld; L Yokochi; J K Gohagan
Journal:  Control Clin Trials       Date:  2000-12

8.  A Bayesian nonlinear mixed-effects disease progression model.

Authors:  Seongho Kim; Hyejeong Jang; Dongfeng Wu; Judith Abrams
Journal:  J Biom Biostat       Date:  2015-12-30

9.  Bayesian lead time estimation for the Johns Hopkins Lung Project data.

Authors:  Hyejeong Jang; Seongho Kim; Dongfeng Wu
Journal:  J Epidemiol Glob Health       Date:  2013-06-14
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