A Stochastic Approximation of EM Algorithm for Handling Missing Data in Cox Regression Models Article Swipe
Missing data is a common issue in data analysis, and in particular in survival analysis where the goal is to explain the time to event from a set of covariates. In the presence of missing data within these covariates, conventional imputation methods such as single imputation often lead to biased results. This work investigates the use of the Stochastic Approximation of the Expectation Maximization algorithm (SAEM) as a more robust approach for handling missing data in Cox regression models. Under the Missing At Random assumption, the proposed methodology integrates both observed and missing covariates into the Cox model estimation, by employing a simulation-based expectation step with the Metropolis-Hastings algorithm to generate plausible values for missing data. Through stochastic approximation, it estimates missing values during inference while ensuring stable and accurate parameter estimation. The proposed SAEM algorithm showed, on simulated and real data, better performance than usual approaches, both in term of parameter estimation and in term of survival time prediction.
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Metadata
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A Stochastic Approximation of EM Algorithm for Handling Missing Data in Cox Regression ModelsWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-06Full publication date if available
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Eliz Peyraud, Julien Jacques, Guillaume MetzlerList of authors in order
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https://hal.science/hal-05057848Publisher landing page
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YesWhether a free full text is available
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0Total citation count in OpenAlex
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