Date: 2014-09-19

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Incomplete data can arise in many different situations for many different reasons. Sometimes the data may be incomplete for reasons beyond the control of the experimenter. However, it is also possible that this missingness is part of the study design. By using a two-phase sampling approach where only a small sub-sample gives complete information, it is possible to greatly reduce the cost of a study and still obtain precise estimates. This talk will introduce the concepts of incomplete data and two-phase sampling designs and will discuss adaptive two-phase designs which exploit information from an internal pilot study to approximate the optimal sampling scheme for an analysis based on mean score estimating equations.

Speaker

Michael McIsaac is an Assistant Professor in the Department of Public Health Sciences at Queen’s University, Kingston, Ontario.