A Latent Factor Model for Non-ignorable Missing Data
Monday, November 2, 2015
: 11:30 a.m. - 11:50 a.m.
Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable. However, this assumption leads to unrealistic simplification and is implausible for many cases. When non-ignorable missingness are preferred, classical pattern-mixture models with the data stratified according to a variety of missing patterns and a model specified for each stratum, are widely used for longitudinal data analysis. But this assumption usually results in under-identifiability, because of the need to estimate many stratum-specific parameters. Further, pattern mixture models have the drawback that a large sample is usually required. In this paper, the continuous latent factor model is proposed and this novel approach overcomes limitations which exist in pattern mixture models by specifying a continuous latent factor. The advantages of this model, including small sample feasibility, are evaluated by comparing with Roy's pattern mixture model, based on a practical example.
Define missing data problems in public health data
Describe CLFM estimation theory in non-ignorable missing data
Compare CLFM with other methods to handle non-ignorable missing data
Presenting author's disclosure statement:
Qualified on the content I am responsible for because: I am a tenured faculty member in the School of Mathematical and Statistical Sciences at Arizona State University. I have published many papers in leading applied statistics journals.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.