Multiple Imputation for Missing Data in Survival Analysis

Gupta Vinay Kumar

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English

Self Employed
30 March 2023

Database design & theory; Machine learning; Information architecture

Summary
Details

In this method a dummy variable for each predictor is included in the regression model. These dummy variables indicate whether or not the data in the predictors is missing (Cohen and Cohen, 1985). Cases with missing data on a predictor are coded as having some constant value, usually the mean for observed cases on that predictor. Though the method use all the available information in the data and may produce reasonably good standard error estimates, it could not become popular as it produces biased estimates of the regression coefficients, even if the data are MCAR (Jones, 1996). There are different methods in which missing values of variables are substituted with some plausible values (Little and Rubin, 2002; Schafer, 1999). The data obtained through these methods are then treated as complete data and is analyzed using conventional statistical methods. Single imputation refers to fill in one value for each missing value in a variable (Haukoos and Newgaurd, 2007). Some single imputation methods are described below.

By: Gupta Vinay Kumar
Imprint: Self Employed
Dimensions: Height: 229mm, Width: 152mm, Spine: 6mm
Weight: 163g
ISBN: 9798889951261
Pages: 116
Publication Date: 30 March 2023
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active