Abstract
BackgroundMarkov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history.MethodsData gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R2 index as model fitting index.ResultsBoth Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model and p = 0.023 for Markov model). Finally, R2 for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model).ConclusionThe MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures.
Highlights
The study of natural history of chronic diseases is doubly complex due to their complex nature and multifactorial causality [1,2,3]
In this study, a Markov system dynamic (MSD) model was designed to model the natural history of metabolic syndrome (MetS), i.e. progression from its components
The findings showed that both the Markov and MSD models were adequate enough to predict the secular trends of the MetS
Summary
The study of natural history of chronic diseases is doubly complex due to their complex nature and multifactorial causality [1,2,3]. Despite the difference between the Markov and the system dynamic models in terms of the stochastic and deterministic nature of the states, due to the important similarity of Markov model with system dynamic model in terms of “state” and “transition”, these two models can be combined with each other or even in some cases converted to each other [14]. This hybrid model have been mainly used in non-medical fields and repairable systems for reliable analysis in a more realistic way [12, 15,16,17]. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history
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