Heckman-type selection models to obtain unbiased estimates with missing measures outcome: theoretical considerations and an application to missing birth weight data

Background In low-income settings, key outcomes such as biomarkers or clinical assessments are often missing for a substantial proportion of the study population. The aim of this study was to assess the extent to which Heckman-type selection models can create unbiased estimates in such settings.