Handling Missing Data in Instrumental Variable Methods for Causal Inference.

It is very common in instrumental variable studies for there to be missing instrument data. For example, in the Wisconsin Longitudinal Study one can use genotype data as a Mendelian randomization-style instrument, but this information is often missing when subjects do not contribute saliva samples, or when the genotyping platform output is ambiguous. Here we review missing-at-random assumptions one can use to identify instrumental variable causal effects, and discuss various approaches for estimation and inference.

Ten-year publication trends in dermatology in mainland China.

Background: China has been experiencing huge changes in all aspects, including dermatologic research, since its reform in 1978. However, it is not known how the economic and intellectual development has influenced the publication trends in the field of dermatology, which could mirror the scientific development in other medical disciplines. In the present study, we analyzed publication trends from dermatology departments in mainland China from 2002 to 2011.