Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

Ambler G, Omar RZ, Royston P (2007) A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome. Stat Methods Med Res 16: 277–298

Bodner TE (2008) What improves with increased missing data imputations? Struct Equation Model: Multidiscip J 15: 651–675

Dawson SJ, Makretsov N, Blows FM, Driver KE, Provenzano E, Le QJ, Baglietto L, Severi G, Giles GG, McLean CA, Callagy G, Green AR, Ellis I, Gelmon K, Turashvili G, Leung S, Aparicio S, Huntsman D, Caldas C, Pharoah P (2010) BCL2 in breast cancer: a favourable prognostic marker across molecular subtypes and independent of adjuvant therapy received. Br J Cancer 103: 668–675

Donders AR, van der Heijden G, Stijnen T, Moons KG (2006) Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 59: 1087–1091

Engels JM, Diehr P (2003) Imputation of missing longitudinal data: a comparison of methods. J Clin Epidemiol 56: 968–976

Greene FL, Page DL, Fleming ID, Fritz AG, Balch CM, Haller DG, Morrow M (eds) (2002) AJCC Cancer Staging Manual, 6th edn. Springer: New York, NY.

Greenland S, Finkle WD (1995) A critical look at methods for handling missing covariates in epidemiologic regression analyses. Am J Epidemiol 142: 1255–1264

Horton NJ, White IR, Carpenter J (2010) The performance of multiple imputation for missing covariates relative to complete case analysis. Stat Med 29: 1357

Kim JO, Curry J (1977) The treatment of missing data in multivariate analysis. Sociol Methods Res 6: 215–241

Little RA (1992) Regression with missing X's; a review. J Am Stat Assoc 87: 1227–1237

Little RA, Rubin DB (2002) Statistical Analysis with Missing Data. John Wiley and Sons: New York

Marshall A, Altman DG, Royston P, Holder RL (2010) Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol 10: 7

Moons KG, Donders RA, Stijnen T, Harrell Jr FE (2006) Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 59: 1092–1101

Rubin DB (1996) Multiple imputation after 18+ years. J Am Stat Assoc 91: 473–489

Rubin DB (2004) Multiple Imputation for Non Rresponse in Surveys. John Wiley and Sons: New York

Rubin DB, Schenker N (1991) Multiple imputation in health-care databases: an overview and some applications. Stat Med 10: 585–598

Schafer JL (1997) Analysis of Incomplete Multivariate Data. Chapman & Hall: London

Schafer JL (1999) Multiple imputation: a primer. Stat Methods Med Res 8: 3–15

Schafer JL, Graham JW (2002) Missing data: our view of the state of the art. Psychol Methods 7: 147–177

Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR (2009) Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338: b2393

Vach W (1991) Biased estimates of the odds ratio in case–control studies due to the use of ad hoc methods of correcting for missing values for confounding variables. Am J Epidemiol 134: 895–907

van Buuren S, Boshuizen HC, Knook DL (1999) Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med 18: 681–694

Van der Heijden G, Donders AR, Stijnen T, Moons KG (2006) Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59: 1102–1109

White IR, Royston P (2009) Imputing missing covariate values for the Cox model. Stat Med 28: 1982–1998