Application of Discrete-Time Multistate Stochastic Process to Community-based Screening for Hypertension
Date Issued
2008
Date
2008
Author(s)
Wang, Wei-Jhih
Abstract
Introductionhe application of stochastic process to chronic disease or cancer has been widely used in biomedical field. However, the majority of models have the emphasis on modeling occurrence of event been based on a continuous -time stochastic process. Very few studies have focused on the use of discrete-time stochastic process. In the realm of economic evaluation of cancer or chronic disease screening, it is customary to ask the question like the following “How many examinations, on average, should be taken in order to detect first occurrence of disease”.bjectivehe aim of this study was to model the average number of screens, using geometric model or negative binomial_based multistate model, to detect disease. esource and Methode use community based screening data in Keelung, focus on hypertension. In order to make continuous-time model with the discrete-time stochastic process, we demonstrated how exponential distribution can be connected to geometric distribution with binary outcome, i.e: hypertension or not. The concept was also extended to develop multistate model for estimating the screening times before occurrence of disease. To transform the exponential distribution into discrete time with small interval, we divide a time period into several epochs, and regard every epoch commensurate whether to have an event. When the first success event occurs after y epochs, we can regard y times failure before a success event given geometric distribution. In addition, we also took into account the reversible process of three-state model by using negative binomial distribution. Besides, we assess the effect of covariates on different situations.stimated resultn average, person will be detected first hypertension after 3.94 screening times in two-state model, and every covariate (sex, age, diabetes, obesity, and education level) was significant. Using mean sojourn time, we inferred normal to stage 2 hypertension would take 19 person years. It’s 2.33 times detect stage 2 hypertension, and 3.47 times from stage 1 to stage 2 hypertension on average. Regarding covariates, every covariate plays a significant role in the occurrence of stage 1 hypertension. Sex and age affected transition from stage 1 to stage 2 hypertension. Without hypertension, first pre-hypertension was after 8.64 screening times, and second pre-hypertension after 17.28 times. Progressing to hypertension, it took 3.09 times of screening, from pre-hypertension to hypertension.iscussionn conclusion, we developed the discrete time stochastic multistate model to estimate expected number of screening times before detecting pre-hypertension or hypertension. The generalize linear models can be further developed to make the generalize discrete time models. The alternative can be considered by using Bayesian method.
Subjects
expected screening times
geometric distribution
negative binomial distribution
discrete time stochastic process
hypertension
SDGs
Type
thesis
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