Missing covariate data in medical research: To impute is better than to ignore

Missing covariate data in medical research: To impute is better than to ignore. Janssen, K. J., Donders, A. R. T., Harrell, F. E., Vergouwe, Y., Chen, Q., Grobbee, D. E., & Moons, K. G. Journal of Clinical Epidemiology, 63(7):721–727, July, 2010.

Paper doi abstract bibtex

Objective: We compared popular methods to handle missing data with multiple imputation (a more sophisticated method that preserves data). Study Design and Setting: We used data of 804 patients with a suspicion of deep venous thrombosis (DVT). We studied three covariates to predict the presence of DVT: D-dimer level, difference in calf circumference, and history of leg trauma. We introduced missing values (missing at random) ranging from 10% to 90%. The risk of DVT was modeled with logistic regression for the three methods, that is, complete case analysis, exclusion of D-dimer level from the model, and multiple imputation. Results: Multiple imputation showed less bias in the regression coefﬁcients of the three variables and more accurate coverage of the corresponding 90% conﬁdence intervals than complete case analysis and dropping D-dimer level from the analysis. Multiple imputation showed unbiased estimates of the area under the receiver operating characteristic curve (0.88) compared with complete case analysis (0.77) and when the variable with missing values was dropped (0.65). Conclusion: As this study shows that simple methods to deal with missing data can lead to seriously misleading results, we advise to consider multiple imputation. The purpose of multiple imputation is not to create data, but to prevent the exclusion of observed data. Ó 2010 Elsevier Inc. All rights reserved.

@article{janssen_missing_2010,
	title = {Missing covariate data in medical research: {To} impute is better than to ignore},
	volume = {63},
	issn = {08954356},
	shorttitle = {Missing covariate data in medical research},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0895435610000193},
	doi = {10.1016/j.jclinepi.2009.12.008},
	abstract = {Objective: We compared popular methods to handle missing data with multiple imputation (a more sophisticated method that preserves data). Study Design and Setting: We used data of 804 patients with a suspicion of deep venous thrombosis (DVT). We studied three covariates to predict the presence of DVT: D-dimer level, difference in calf circumference, and history of leg trauma. We introduced missing values (missing at random) ranging from 10\% to 90\%. The risk of DVT was modeled with logistic regression for the three methods, that is, complete case analysis, exclusion of D-dimer level from the model, and multiple imputation.
Results: Multiple imputation showed less bias in the regression coefﬁcients of the three variables and more accurate coverage of the corresponding 90\% conﬁdence intervals than complete case analysis and dropping D-dimer level from the analysis. Multiple imputation showed unbiased estimates of the area under the receiver operating characteristic curve (0.88) compared with complete case analysis (0.77) and when the variable with missing values was dropped (0.65).
Conclusion: As this study shows that simple methods to deal with missing data can lead to seriously misleading results, we advise to consider multiple imputation. The purpose of multiple imputation is not to create data, but to prevent the exclusion of observed data. Ó 2010 Elsevier Inc. All rights reserved.},
	language = {en},
	number = {7},
	urldate = {2020-05-12},
	journal = {Journal of Clinical Epidemiology},
	author = {Janssen, Kristel J.M. and Donders, A. Rogier T. and Harrell, Frank E. and Vergouwe, Yvonne and Chen, Qingxia and Grobbee, Diederick E. and Moons, Karel G.M.},
	month = jul,
	year = {2010},
	pages = {721--727},
	file = {Janssen et al. - 2010 - Missing covariate data in medical research To imp.pdf:/Users/neil.hawkins/Zotero/storage/ER2DXRPY/Janssen et al. - 2010 - Missing covariate data in medical research To imp.pdf:application/pdf},
}

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