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In longitudinal data analysis, the response is usually modeled conditionally on a random effect that often has a Gaussian distribution. Bayesian nonparametric (BNP) statistics do not generally impose probability distribution for either response or random effect. The Dirichlet process (DP), which is a common BNP model, is formerly presented for key properties and a constructive definition from which several DP variants can be obtained. (e.g., the Dependent DP). BNP models are here revised to select the best joint model for two binary responses associated with a longitudinal four-arm randomized parallel trial conducted in Bengo Province exploring the effects of four interventions on wasting and stunting for 121 Angolan children with intestinal parasitic infections. Finally, we present a simulation study for evaluation the performance of the proposed BNP joint model.

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Joint Models of Longitudinal Binary Responses: A Bayesian Nonparametric Approach

  • 会议论文

  • 发表日期：

    2025年

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  • André Nunes,
  • Giovani L. Silva,
  • Luzia Gonçalves

摘要

In longitudinal data analysis, the response is usually modeled conditionally on a random effect that often has a Gaussian distribution. Bayesian nonparametric (BNP) statistics do not generally impose probability distribution for either response or random effect. The Dirichlet process (DP), which is a common BNP model, is formerly presented for key properties and a constructive definition from which several DP variants can be obtained. (e.g., the Dependent DP). BNP models are here revised to select the best joint model for two binary responses associated with a longitudinal four-arm randomized parallel trial conducted in Bengo Province exploring the effects of four interventions on wasting and stunting for 121 Angolan children with intestinal parasitic infections. Finally, we present a simulation study for evaluation the performance of the proposed BNP joint model.