Comparison of regression imputation methods of baseline covariates that predict survival outcomes.

ABSTRACT:

Introduction

Missing data are inevitable in medical research and appropriate handling of missing data is critical for statistical estimation and making inferences. Imputation is often employed in order to maximize the amount of data available for statistical analysis and is preferred over the typically biased output of complete case analysis. This article examines several types of regression imputation of missing covariates in the prediction of time-to-event outcomes subject to right censoring.

Methods

We evaluated the performance of five regression methods in the imputation of missing covariates for the proportional hazards model via summary statistics, including proportional bias and proportional mean squared error. The primary objective was to determine which among the pa

SUBMITTER: Solomon N 

PROVIDER: S-EPMC8057424 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature