• Title/Summary/Keyword: informative observation process

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A Study on One Factorial Longitudinal Data Analysis with Informative Drop-out

  • Lee, Ki-Hoon
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1053-1065
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    • 2006
  • This paper proposes a method in one-way layouts for longitudinal data with informative drop-out. When dropouts are informative, that is, correlated with unobserved data and/or the previous observed data, the simple imputation methods such as 'last observation carried forward' (LOCF) methods would arise the bias of the testing models. The maximum likelihood procedure combined with a logit model for the drop-out process is proposed to test treatment effects for one factorial designs and compared with LOCF method in two examples.

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Joint model of longitudinal data with informative observation time and competing risk (결시적 자료에서 관측 중단을 모형화하기 위해 사용되는 경쟁 위험의 적용과 결합 모형)

  • Kim, Yang-Jin
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.113-122
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    • 2016
  • Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.