|
Activity Number:
|
478
- Missing Data
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #329261
|
Presentation
|
|
Title:
|
Model Compatible Multiple Imputation Method for Minimizing the Impact of Covariate Detection Limit in Logistic Regression
|
|
Author(s):
|
Shahadut Hossain*
|
|
Companies:
|
UAE University
|
|
Keywords:
|
Multiple Imputation;
Logistic regression ;
Left-truncation;
Ad-hoc substitution;
, Detection limit
|
|
Abstract:
|
Presence of detection limit (DL) in covariates causes inflated bias and inaccurate mean squared error to the estimators of the regression parameters. This paper suggests a response-driven multiple imputation method to correct the deleterious impact introduced by the covariate DL in the estimators of the parameters of simple logistic regression model. The performance of the method has been thoroughly investigated, and found to outperform the existing competing methods. The proposed method is computationally simple and easily implementable by using three existing R libraries. The method is robust to the violation of distributional assumption for the covariate of interest.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2018 program
|
