Abstract
Molecular epidemiology studies commonly exhibit missing observations. Methods for extracting correct and efficient analyses from incomplete data are well known in statistics, but relatively few such methods have diffused into applications. I review some areas of incomplete data research that are relevant to molecular epidemiology and appeal for greater efforts by statisticians to translate their methods into practice.