• 한국신뢰성학회지:신뢰성응용연구
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• 제6권3호
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• pp.225-237
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• 2006

This paper proposes a method of modelling the informative dropouts with QoL(quality of life) in survival analysis. QoL is the index to measure the health related quality of life of a patient who got some treatments for a disease. Dropouts are prevalent occurrences on longitudinal study They are commonly dependent to the QoL of patients, that is, severe disease or death and called informative dropouts. Modelling the mechanism of dropouts could achieve the more accurate inference for survival analysis. A likelihood method is proposed to estimate the survival parameter and test the patterns of dropouts.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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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.