Robust likelihood-based analysis of multivariate data with missing values

The model-based approach to inference from multivariate data with missingvalues is reviewed. Regression prediction is most useful when the covariates are
predictive of the missing values and the probability of being missing, and in these
circumstances predictions are particularly sensitive to model misspecification. The
use of penalized splines of the propensity score is proposed to yield robust modelbased
inference under the missing at random (MAR) assumption, assuming monotone
missing data. Simulation comparisons with other methods suggest that the
method works well in a wide range of populations, with little loss of efficiency relative
to parametric models when the latter are correct. Extensions to more general
patterns are outlined.