A robust joint modeling approach for longitudinal data with informative dropouts

Zhang, Weiping; Xie, Feiyue; Tan, Jiaxin

doi:10.1007/s00180-020-00972-6

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…The literature on statistical methods for handling missing data with repeated measurements is relatively wide. In this regards, as recently summarized in Zhou et al (2020), we can distinguish between different approaches, which include selection models (Molenberghs et al, 1997;Maruotti, 2015), pattern-mixture models (Little, 1994;Follmann and Wu, 1995;Kenward et al, 2003;Marino and Alfò, 2020), and shared-parameter (or joint) models (Wulfsohn and Tsiatis, 1997;Henderson et al, 2000;Hsieh et al, 2006;Rizopoulos, 2012;Bartolucci and Farcomeni, 2015a;Lange et al, 2015;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout

Pandolfi,

Bartolucci,

Pennoni

2021

Preprint

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We propose an inferential approach for maximum likelihood estimation of the hidden Markov models for continuous responses. We extend to the case of longitudinal observations the finite mixture model of multivariate Gaussian distributions with Missing At Random (MAR) outcomes, also accounting for possible dropout. The resulting hidden Markov model accounts for different types of missing pattern: (i) partially missing outcomes at a given time occasion; (ii) completely missing outcomes at a given time occasion (intermittent pattern); (iii) dropout before the end of the period of observation (monotone pattern). The MAR assumption is formulated to deal with the first two types of missingness, while to account for informative dropout we assume an extra absorbing state. Maximum likelihood estimation of the model parameters is based on an extended Expectation-Maximization algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.

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Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout

Pandolfi,

Bartolucci,

Pennoni

2021

Preprint

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A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse

Liu

Yau

2021

Stat Papers

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Two‐stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification

Zhang,

Yuen,

Yan

2024

Computer aided Civil Eng

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In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two‐stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.

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A robust joint modeling approach for longitudinal data with informative dropouts

Cited by 4 publications

References 39 publications

Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout

Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout

A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse

Two‐stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification

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