Simulation Procedure in Periodic Cancer Screening Trials

A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affe...

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Published inJournal of modern applied statistical methods Vol. 4; no. 2; p. 522
Main Authors Wu, Dongfeng, Wu, Xiaoqin, Banicescu, Ioana, Cariño, Ricolindo L
Format Journal Article
LanguageEnglish
Published United States 01.11.2005
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Abstract A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from disease-free state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood estimates (MLE) from generated data. A practical procedure is presented, along with results for when both sensitivity and transition probability into the preclinical state are age-dependent. The procedure is also suitable for other applications.
AbstractList A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from disease-free state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood estimates (MLE) from generated data. A practical procedure is presented, along with results for when both sensitivity and transition probability into the preclinical state are age-dependent. The procedure is also suitable for other applications.
Author Wu, Dongfeng
Cariño, Ricolindo L
Wu, Xiaoqin
Banicescu, Ioana
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  givenname: Ricolindo L
  surname: Cariño
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