Abstract:
Non-negative continuous outcomes with a substantial number of zero values and incomplete
longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate
the potential dependence of survival status and longitudinal medical costs in joint modeling, where
censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to
capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in
the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM)
to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the
conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects
on average costs. We conducted a series of simulation studies to evaluate the superior performance
of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the
model to a set of real electronic health record (EHR) data recently collected in Iran. We found that
the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value,
with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation
between costs and survival, which was consistent with the simulation results.
