9 June 2012
Missing data in observational and randomized studies
Missing data arise in most real-world situations, and can cause bias or lead to inefficient analyses. The development of statistical methods to address missingness has been actively pursued in recent years, and sophisticated software to appropriately account for it is available within Stata. In this course, participants will learn ways to minimize missingness, the nomenclature for missing data methods, appropriate ways to describe patterns of missing data as well as how to account for incomplete observations using multiple imputation and sensitivity analysis. The course will emphasize practical skills with biomedical examples of observational studies and randomized trials.
N.J. Horton (Smith College)
Physicians, clinicians and public health professionals from public and private institutions who are looking for systematic training in the principles of epidemiology and biostatistics, or epidemiology applied to health care planning and evaluation.
Students in biostatistics and epidemiology, and researchers both from public and private institutions who wish to increase their familiarity with quantitative methods or to deepen their knowledge of a specific area of interest.
Students will learn ways to minimize missingness, the
nomenclature for missing data methods, ways to describe
patterns of missing data, and how to account for incomplete
observations using multiple imputation and sensitivity analysis.
EUR 1150: For Students. Registration before 31 March 2012.
After 31 March 2012 €1,350.
EUR 1250: Registration before 31 March 2012.
After 31 March 2012 €1,450.
A limited number of Scholarships are available.
Scholarships cover the cost of tuition, for at most one week. Only registered students may apply. The request to be considered for a scholarship should be communicated no later than March 1, 2012.