Editorial, J Appl Bioinform Comput Biol Vol: 1 Issue: 1
Missing Data: A Non-ignorable Issue in Modern Biostatistics
| Kendra K. Schmid and Baojiang Chen* | |
| Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA | |
| Corresponding author : Baojiang Chen Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA E-mail: baojiang.chen@unmc.edu |
|
| Received: June 11, 2012 Accepted: June 12, 2012 Published: June 14, 2012 | |
| Citation: Schmid KK, Chen B (2012) Missing Data: A Non-ignorable Issue in Modern Biostatistics. J Appl Bioinform Comput Biol 1:1. doi:10.4172/2329-9533.1000e101 |
Abstract
Missing Data: A Non-ignorable Issue in Modern Biostatistics
Missing data is a common feature in modern biostatistics. For most cases, the easily implemented method such as complete case analysis will yield biased conclusions if data are Missing At Random (MAR) or Missing Not At Random (MNAR). The vast majority of articles in the literature dealing with incomplete data make the unrealistic assumption that data are available for the response but incomplete for the covariates or risk factors, or alternatively, they assume that information on risk factors is complete, but the data on response are incomplete.
