The prevention and handling of the missing data

Hyun Kang, Hyun Kang

Abstract

Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data.

Keywords: Expectation-Maximization; Imputation; Missing data; Sensitivity analysis.

References

    1. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549–576.
    1. Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367:1355–1360.
    1. O'Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther. 2012;91:550–554.
    1. Rubin DB. Inference and missind data. Biometrika. 1976;63:581–592.
    1. DeSarbo S, Green PE, Carroll JD. An alternating least-squares procedure for estimating missing preference data in product-concept testing. Decision Sciences. 1986;17:163–185.
    1. Wisniewski SR, Leon AC, Otto MW, Trivedi MH. Prevention of missing data in clinical research studies. Biol Psychiatry. 2006;59:997–1000.
    1. Scharfstein DO, Hogan J, Herman A. On the prevention and analysis of missing data in randomized clinical trials: the state of the art. J Bone Joint Surg Am. 2012;94(Suppl 1):80–84.
    1. Wilcox S, Shumaker SA, Bowen DJ, Naughton MJ, Rosal MC, Ludlam SE, et al. Promoting adherence and retention to clinical trials in special populations: a women's health initiative workshop. Control Clin Trials. 2001;22:279–289.
    1. Donner A. The relative effectiveness of procedures commonly used in multiple regression analysis for dealing with missing values. Am Stat. 1982;36:378–381.
    1. Kim JO, Curry J. The treatment of missing data in multivariate analysis. Sociol Methods Res. 1977;6:215–241.
    1. Malhotra N. Analyzing marketing research data with incomplete information on the dependent variable. J Mark Res. 1987;24:74–84.
    1. Hamer RM, Simpson PM. Last observation carried forward versus mixed models in the analysis of psychiatric clinical trials. Am J Psychiatry. 2009;166:639–641.
    1. Panel on Missing Data in Clinical Trials. The prevention and treatment of missing data in clinical trials. 2nd ed. Washington DC: National Academies Press; 2010. pp. 107–114.
    1. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. JRSSB. 1997;39:1–38.
    1. Sinharay S, Stern HS, Russell D. The use of multiple imputation for the analysis of missing data. Psychol Methods. 2001;6:317–329.
    1. Rubin DB. Multiple imputation after 18+ years (with discussion) J Am Stat Assoc. 1996;91:473–489.
    1. Acock AC. Working with missing values. J Marriage Fam. 2005;67:1012–1028.

Source: PubMed

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