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

Jimmy Efird,Charulata Jindal

doi:10.3390/ijerph15040690

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 (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.

Highlights

Censoring in a survival analysis should be non-informative and not related to any aspect of the study that could bias results [1,2,3,4,5,6,7]
We present an example of early censoring to illustrate how the resulting survival probabilities may be biased
At 5 years, rounding to the nearest whole number, we see that the survival times are identical (i.e., 26%)

Summary

Introduction

Censoring in a survival analysis should be non-informative and not related to any aspect of the study that could bias results [1,2,3,4,5,6,7]. Toxic side effects of an investigational drug may prompt the most ill patients to withdraw early from the study. Other patients may opt to leave before the intended end of a trial if the treatment is effective and they feel well. Even when censoring is non-informative (e.g., relocation to another city because of plant closure), by chance alone, it may still have a serious effect on estimated survival probabilities, especially if the dropouts occur early in the study. We present an example of early censoring to illustrate how the resulting survival probabilities may be biased. We describe a method to impute censored follow-up times by rearranging the data as a counting process and generating jump-point plots

Methods

Results

Discussion

Conclusion