2009
DOI: 10.1136/bmj.b2393
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Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

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 them

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Cited by 5,481 publications
(4,737 citation statements)
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References 24 publications
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“…These include the prospective population‐based cohort design, the relatively large sample size and our ability to adjust for multiple biological, clinical and lifestyle covariates, including HPV. In addition to this, we explored the risk of bias due to missing data by employing a multiple imputation approach 47. Results of the imputed analysis was broadly consistent with those of the complete case analysis.…”
Section: Discussionmentioning
confidence: 84%
“…These include the prospective population‐based cohort design, the relatively large sample size and our ability to adjust for multiple biological, clinical and lifestyle covariates, including HPV. In addition to this, we explored the risk of bias due to missing data by employing a multiple imputation approach 47. Results of the imputed analysis was broadly consistent with those of the complete case analysis.…”
Section: Discussionmentioning
confidence: 84%
“…Interactions between the variables included in the multivariable model and type of AML were estimated within the Cox model and presented in a forest plot where age was categorized for presentational purposes. To test for bias due to baseline variables missing at random (MAR), a sensitivity analysis was performed using multiple imputation for all variables with missing data, using predictive mean matching (with the function aregImpute in R) [14,15]. Outcome and survival, was included as the Nelson-Aalen estimator in the imputation model, although not imputed itself because it contained no missing values.…”
Section: Methodsmentioning
confidence: 99%
“…As there were significant predictors of missingness in our baseline data, data cannot be presumed to be missing at random, potentially biasing estimates (Sterne et al., 2009). In the subsample of participants without lifetime NSSI at age 14 (our inclusion criteria), there was minimal missing data on NSSI at age 17 (12%, see results for more details).…”
Section: Methodsmentioning
confidence: 99%