title: Regression analysis of incomplete medical cost data.
abstract: The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time-dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer.
authors.affiliation: Department of Biostatistics, University of North Carolina, CB#7420 McGavran-Greenberg, Chapel Hill 27599-7420, USA. lin@bios.unc.edu
authors.forename: D Y
authors.initials: DY
authors.lastname: Lin
mesh_terms: D000368:Aged; D017048:Health Care Costs; D006801:Humans; D008137:Longitudinal Studies; D018803:Models, Economic; D015233:Models, Statistical; D012044:Regression Analysis; D013269:Stochastic Processes