Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Top Cited Papers
Open Access
- 29 June 2009
- Vol. 338 (jun29 1) , b2393
- https://doi.org/10.1136/bmj.b2393
Abstract
Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with themKeywords
This publication has 14 references indexed in Scilit:
- Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studiesBMJ, 2007
- Imputation is beneficial for handling missing data in predictive modelsJournal of Clinical Epidemiology, 2007
- Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort studyBMJ, 2007
- Much Ado About NothingThe American Statistician, 2007
- Using the outcome for imputation of missing predictor values was preferredJournal of Clinical Epidemiology, 2006
- Robustness of a multivariate normal approximation for imputation of incomplete binary dataStatistics in Medicine, 2006
- Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journalsClinical Trials, 2004
- A comparison of inclusive and restrictive strategies in modern missing data procedures.2001
- Multiple imputation of missing blood pressure covariates in survival analysisStatistics in Medicine, 1999
- Biased Estimation of the Odds Ratio in Case-Control Studies due to the Use of Ad Hoc Methods of Correcting for Missing Values for Confounding VariablesAmerican Journal of Epidemiology, 1991