Albuquerque, JoãoAlves, Ana C.Medeiros, Ana M.Bourbon, MafaldaAntunes, Marília2021-04-302021-04-302020-10J Stat Health Decis. 2020;2(2):73-74. doi:10.34624/jshd.v2i2.21156http://hdl.handle.net/10400.18/7720Introduction: One ofthe greatest challenges when working with clinical datasetsisto decide howto deal withmissing values. Removing observations with any missing values priorto data analysis, a process defined aslistwise deletion, is the standard default procedure in most statistical software packages, but may lead to great loss of valuable information [1]. The use of robust imputation methods may provide accurate estimates for missing values, allowing to include these observations into the analysis. The imputation strategy to adopt depends on the amount and type of missing information, and also on the relation between variables, allying statistical expertise with clinical understanding of the data. The main purpose of this work was to compare the performance oftwo differentmethods ofimputationto overcomemissingness on dyslipidemic patients regarding statin usage.porData ImputationStatinsDyslipidemiaDoenças Cardio e Cérebro-vascularesconference object10.34624/jshd.v2i2.21156