On the estimation of the incidence and prevalence in two-phase longitudinal sampling design.

Two-phase sampling design is a common practice in many medical studies. Generally, the first-phase classification is fallible but relatively cheap, while the accurate second phase state-of-the-art medical diagnosis is complex and rather expensive to perform. When constructed efficiently it offers great potential for higher true case detection as well as for higher precision at a limited cost. In this article, we consider epidemiological studies with two-phase sampling design. However, instead of a single two-phase study, we consider a scenario where a series of two-phase studies are done in a longitudinal fashion on a cohort of interest. Another major design issue is non-curable pattern of certain disease (e.g. Dementia, Alzheimer's etc.). Thus often the identified disease positive subjects are removed from the original population under observation, as they require clinical attention, which is quite different from the yet unidentified group. In this article, we motivated our methodology development from two real-life studies. We consider efficient and simultaneous estimation of prevalence as well incidence at multiple time points from a sampling design-based approach. We have explicitly shown the benefit of our developed methodology for an elderly population with significant burden of home-health care usage and at the high risk of major depressive disorder.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany.

In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle.