What question did this study set out to answer?

To develop nonparametric likelihood methods for estimating parameters in multistate semi-Markov processes subject to partially censored observations.

August 1, 1978Biometrika

Semi-Markov models for partially censored data

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To develop nonparametric likelihood methods for estimating parameters in multistate semi-Markov processes subject to partially censored observations.
Assumed an underlying semi-Markov model where state changes follow an embedded Markov chain and sojourn times are independent with distributions conditional on adjacent states.
Derived and nonparametrically maximized the general likelihood function for partially censored data.
Formulated nonparametric maximum likelihood estimators that possess desirable theoretical properties.
Established approximate distributional properties for the proposed estimators to enable statistical inference.

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Implication

Methodological study demonstrates nonparametric likelihood estimation for partially censored multistate processes, indicating reliable inference for complex state transitions.

Cite This Study

Lagakos et al. (1978) studied this question.

synapsesocial.com/papers/6a12b0e2c031bb6829a71bae https://doi.org/10.1093/biomet/65.2.311

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Implication

Methodological study demonstrates nonparametric likelihood estimation for partially censored multistate processes, indicating reliable inference for complex state transitions.

Semi-Markov models for partially censored data

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Discussion

Implication

Cite This Study

Semi-Markov models for partially censored data

Key Points

Discussion

Implication

Cite This Study

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