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Article

Using a Counting Process Method to Impute Censored Follow-Up Time Data

Centre for Clinical Epidemiology and Biostatistics (CCEB), School of Medicine and Public Health, The University of Newcastle (UoN), Callaghan, NSW 2308, Australia
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Int. J. Environ. Res. Public Health 2018, 15(4), 690; https://doi.org/10.3390/ijerph15040690
Received: 17 March 2018 / Revised: 1 April 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
Censoring occurs when complete follow-up time information is unavailable for patients enrolled in a clinical study. The process is considered to be informative (non-ignorable) if the likelihood function for the model cannot be partitioned into a set of response parameters that are independent of the censoring parameters. In such cases, estimated survival time probabilities may be biased, prompting the need for special statistical methods to remedy the situation. The problem is especially salient when censoring occurs early in a study. In this paper, we describe a method to impute censored follow-up times using a counting process method. View Full-Text
Keywords: counting process; censoring; Cox proportional-hazard regression; Kaplan-Meier; imputation; survival analysis counting process; censoring; Cox proportional-hazard regression; Kaplan-Meier; imputation; survival analysis
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MDPI and ACS Style

Efird, J.T.; Jindal, C. Using a Counting Process Method to Impute Censored Follow-Up Time Data. Int. J. Environ. Res. Public Health 2018, 15, 690. https://doi.org/10.3390/ijerph15040690

AMA Style

Efird JT, Jindal C. Using a Counting Process Method to Impute Censored Follow-Up Time Data. International Journal of Environmental Research and Public Health. 2018; 15(4):690. https://doi.org/10.3390/ijerph15040690

Chicago/Turabian Style

Efird, Jimmy T., and Charulata Jindal. 2018. "Using a Counting Process Method to Impute Censored Follow-Up Time Data" International Journal of Environmental Research and Public Health 15, no. 4: 690. https://doi.org/10.3390/ijerph15040690

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