Biased Estimation of Adjusted Odds Ratios From Incomplete Covariate Data Due to Violation of the Missing at Random Assumption

Vach, W. and Illi, S. (1996): Biased Estimation of Adjusted Odds Ratios From Incomplete Covariate Data Due to Violation of the Missing at Random Assumption. Collaborative Research Center 386, Discussion Paper 17

Abstract

We investigate the possible bias due to an erroneous missing at random assumption if adjusted odds ratios are estimated from incomplete covariate data using the maximum likelihood principle. A relation between complete case estimates and maximum likelihood estimates allows us to identify situations where the bias vanishes. Numerical computations demonstrate that the bias is most serious if the degree of the violation of the missing at random assumption depends on the value of the outcome variable or of the observed covariate. Implications for the analysis of prospective and retrospective studies are given.

• Item Type: Paper
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: German