Missing Data and Multiple Imputation

Friday, June 17, 2016 - Friday, June 17, 2016 10:00 AM - 3:30 PM
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Course Description

This course will highlight the advantages of multiple imputation as a strategy for addressing missing data, and provide guidance on best practices. Multiple imputation can help researchers to take full advantage of their available data, preserve sample size in multivariable analyses with missing covariate data, and reduce bias. A hands-on exercise will allow participants to quantitatively describe and impute missing data in either SAS or Stata and interpret the results. Tips for developing and documenting a multiple imputation strategy for publication will be discussed.

This course is eligible for an EPIC scholarship. Visit the scholarship application page for more information.

Course Objectives

By the end of the course, participants will be able to:

  1. List and categorize causes of missing data commonly encountered in research
  2. Describe the advantages and limitations of multiple imputation as compared with common alternatives including complete case analysis, a missing data indicator, and single imputation
  3. Plan and document a strategy to use multiple imputation as a main approach or sensitivity analysis
  4. Check and interpret multiple imputation results Access resources for further learning and practice


Previous training in biostatistics or statistics including descriptive statistics and basic regression modeling required. Familiarity with SAS or Stata software will be strongly preferred, though if already proficient with SPSS or R participants may be able to use these for multiple imputation with limited instructor support.

Course Reading List
  1. Raghunathan TE. What do we do with missing data? Some options for analysis of incomplete data. Annual review of public health. 2004;25:99-117.
  2. Klebanoff MA, Cole SR. Use of multiple imputation in the epidemiologic literature. American journal of epidemiology. 2008 Aug 15;168(4):355-7.


Shakira F. Suglia, ScD, MS

Dr. Suglia's research takes a multi-disciplinary approach toward understanding health disparities, examining the impact of environmental exposures and social stressors on disease and health. She focuses in particular on the health of children and adolescents, and their relation to social and environmental issues such as violence, housing, and traffic exposures. Dr. Suglia's current work, examines how social stressors, in particular violence, and physical environmental factors experienced during adolescence influence cardiovascular health in adulthood. In her work with the Boricua Youth Study, she examines the role of social stressors experienced during childhood and adolescence in relation to cardiovascular and metabolic risk profiles among Puerto Rican youth living in the South Bronx, NY and San Juan, Puerto Rico. She will further examine the role of acculturation, cultural stress and social context as a potential modifier of the child stress and cardiovascular health association.

Course Fee

Registration is $250.00


The registration period has closed for this event.


Hammer LL204

701 West 168th Street
New York, NY 10032

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