A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions
Hilary Aralis1, Ron Brookmeyer1
1UCLA Department of Biostatistics, Fielding School of Public Health, Los Angeles, CA, USA.
Statistical Methods in Medical Research
|November 10, 2017
Summary
This study introduces a new algorithm for analyzing complex health data, improving estimates for disease progression and recovery. The stochastic expectation-maximization algorithm offers a more accurate method for understanding patient transitions between health states.
Area of Science:
- Biostatistics
- Epidemiology
- Health Data Science
Background:
- Multistate models are crucial for analyzing life history processes like disease progression and recovery.
- Estimating transition intensities in these models is challenging with intermittent observations and possible back transitions.
- Existing methods like minimal path estimation can introduce bias in parameter estimates.
Purpose of the Study:
- To present a novel iterative stochastic expectation-maximization (EM) algorithm for analyzing intermittently observed semi-Markov models.
- To address the intractability of the likelihood function in such models.
- To improve the accuracy of parameter estimates, particularly for back transitions.
Main Methods:
- Developed an iterative stochastic EM algorithm utilizing a simulation-based approximation of the likelihood function.
- Implemented the algorithm using rejection sampling.
- Applied the algorithm to the Nun Study, analyzing dementia progression in elderly subjects.
Main Results:
- The proposed stochastic EM algorithm demonstrates feasibility and good performance in simulation studies.
- It substantially reduces bias in model parameter estimates compared to minimal path estimation.
- Significant improvements were observed in the estimation of back transitions.
Conclusions:
- The developed stochastic EM algorithm is a computationally feasible and accurate method for estimating intermittently observed semi-Markov models.
- This approach offers substantial improvements in estimating back transitions, crucial for understanding complex health trajectories.
- The method is applicable to real-world health studies, such as the analysis of dementia progression.
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