Print  Close

An effective macro to analyze non-normal data with missing values

Abstract Number:

1847 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Fengzheng Zhu (1)

Institutions:

(1) N/A, N/A

First Author:

Fengzheng Zhu  
N/A

Presenting Author:

Fengzheng Zhu  
N/A

Abstract Text:

Multiple imputation (MI) is a popular experimental procedure for handling missing data, while robust regression implements novel empirical evidence for the decision regarding non-normal or normal variables. By down-weighting the influence of outliers, Robust regression minimize residual impact on the coefficient estimates. A macro developed by the Fortrea Company, combines two methods together to accurately capture this relationship in continuous efficacy laboratory data and protect against potential non‐normality/outliers in the original or imputed dataset. This paper provides an example programming procedure and suggests possible improvements in the macro based on the author's experience.

Keywords:

Multiple imputation |missing data|non-normal |Robust regression | |

Sponsors:

Section for Statistical Programmers and Analysts

Tracks:

SAS and other commercial software programming

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand