Haplotype-based analyses are thought to play a major role in the study of common complex diseases. This has led to the development of a variety of statistical methods for detecting disease-haplotype associations from case-control study data. However, haplotype phase is often uncertain when only genotype data is available. Methods that account for haplotype ambiguity by modeling the distribution of haplotypes can, if this distribution is misspecified, lead to substantial bias in parameter estimates even when complete genotype data is available. Here we study estimators that can be derived from score functions of appropriate likelihoods. We use the efficient score approach to estimation in the presence of nuisance parameters to a derive novel estimators that are robust to the haplotype distribution. We establish key relationships between estimators and study their empirical performance via simulation.