ABSTRACT
In Chapter 1, key concepts were set out that are relevant throughout this volume. First, missing data mechanisms were considered: missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR). Second, the choices of model framework to simultaneously model the outcome and missing-data were described: selection models, pattern-mixture models, and shared-parameter models. Third, the major routes of inference were reviewed: likelihood and Bayesian inference, methods based on inverse probability weighting, and multiple imputation. The latter classification also applies to Parts II, III, and IV of this volume.
