Medical expenditure data analysis has recently become an important problem in biostatistics. These data typically have a number of features making their analysis rather difficult. Commonly, they are heavily right-skewed, contain a large percentage of zeros, and often exhibit large numbers of missing observations because of death and/or the lack of follow-up. They are also commonly obtained from records that are linked to large longitudinal data surveys. In this manuscript, we suggest a novel approach to modeling these data through the use of generalized method of moments estimation procedure combined with appropriate weights that account for both dropout due to death and the probability of being sampled from among the National Long Term Care Survey (NLTCS) subjects. This approach seems particularly appropriate because of the large number of subjects relative to the length of observation period (in months). We also use a simulation study to compare our proposed approach with and without the use of weights. The proposed model is applied to medical expenditure data obtained from the 2004-2005 NLTCS-linked Medicare database. The results suggest that the amount of medical expenditures incurred is strongly associated with higher number of activities of daily living (ADL) disabilities and self-reports of unmet need for help with ADL disabilities. Copyright © 2016 John Wiley & Sons, Ltd.
Keywords: generalized method of moments (GMM); inverse probability weighting-generalized estimating equations (IPW-GEE); longitudinal data survey; medical expenditure data; modified sandwich estimator.
Copyright © 2016 John Wiley & Sons, Ltd.