Article
Version 1
Preserved in Portico This version is not peer-reviewed
Using a Counting Process Method to Impute Censored Follow-up Time Data
Version 1
: Received: 17 March 2018 / Approved: 19 March 2018 / Online: 19 March 2018 (07:42:49 CET)
A peer-reviewed article of this Preprint also exists.
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. 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.
Abstract
Censoring occurs when complete follow-up time information is unavailable for patients enrolled in a clinical study. The process is considered to be informative (nonignorable) if the likelihood function for the censoring 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 is skewed toward the early phase of a study. In this paper, we describe a method to impute censored follow-up times using a counting process method.
Keywords
counting process; censoring; Cox proportional-hazard regression; Kaplan-Meier; imputation; survival analysis
Subject
Computer Science and Mathematics, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment