Abstract Number:
2952
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Vivek Pradhan (1), Douglas Nychka (2), Soutir Bandyopadhyay (2)
Institutions:
(1) Pfizer Inc, Cambridge, MA, (2) Colorado School of Mines, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Binary regression, a key technique in applied statistics, often encounters missing values in practice.
Complete case analysis (CC) is commonly used, involving the exclusion of subjects with missing values,
particularly in large sample sizes. However, it is well-known that CC can lead to biased estimates with
small or medium-sized datasets. Existing methods for handling missing data typically focus on either missing covariates or missing responses, but not both simultaneously. In biomedical research and other real-world applications, missing values commonly occur in both the response variable and the covariates. In this presentation, we propose a method that effectively handles missing data in both response and covariate levels. Our method assumes that missing covariate data are missing at random (MAR) and that missing responses are nonignorable. Additionally, we propose a bias correction method based on Firth (1993) for fitting models with small samples. The proposed methods offer a comprehensive approach to address missing data in binary regression, demonstrating its effectiveness in both simulated scenarios and practical applications.
Keywords:
Missing data|Likelihood |Binary regression| | |
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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