The performance of multiple imputation for missing covariates relative to complete case analysis

Statistics in Medicine

Volumn 29, Issue 12, 2010, Pages 1357-

  Horton, Nicholas J ^a   White, Ian R ^b   Carpenter, James ^c  

^a SMITH COLLEGE   (United States)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Complete case analysis; Missing data; Missing not at random; Multiple imputation

Indexed keywords

COVARIANCE; LETTER; PERFORMANCE; REGRESSION ANALYSIS; SIMULATION;

BIAS (EPIDEMIOLOGY); BIOSTATISTICS; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS;

EID: 77952838083     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3872     Document Type: Letter

References (4)

1. Giorgi R, Belot A, Gaudart J, Launoy G. the French Network of Cancer Registries FRANCIM. The performance of multiple imputation for missing covariate data within the context of regression relative survival analysis. Statistics in Medicine 2008; 27:6310-6331.
2. Little RJA, Rubin DB. Statistical Analysis with Missing Data (2nd edn). Wiley: New York, 2002.
3. White IR, Royston P. Imputing missing covariate values for the Cox model. Statistics in Medicine 2009; 28:1982-1998.
4. White IR, Carlin JB. Is multiple imputation always better than complete-case analysis for handling missing covariate values? Under review.