Statistical models with unobservable random variables such as random-effect models have
been recently studied for analyzing data of complex types (eg longitudinal and time-to-event …

A consequence of using a parametric frailty model with nonparametric baseline hazard for
analyzing clustered time-to-event data is that its regression coefficient estimates could be …

The cluster randomization has been increasingly popular for pragmatic clinical trials by
many public health researchers. The main advantages of using the cluster randomization …

Albatross Analytics is a statistical and data science data processing platform that
researchers can use in disciplines of various fields. Albatross Analytics makes it easy to …

Natural history of hepatitis B or C is comprised of multiple milestones such as liver cirrhosis
and liver cancer. To fully characterize its natural course, semicompeting risks represent a …

Competing risks data usually arise when an occurrence of an event precludes other types of
events from being observed. Such data are often encountered in a clustered clinical study …

During follow-up patients may experience non-fatal events related to disease progression
and death. This is a “semi-competing risks” setting, as the occurrence of death before non …

For analyzing multiple events data, the illness death model is often used to investigate the
covariate–response association for its easy and direct interpretation as well as the flexibility …

In a semi-competing risks model in which a terminal event censors a non-terminal event but
not vice versa, the conventional method can predict clinical outcomes by maximizing …

In semi-competing risks (which generalizes the competing risks scenario), a subject may
experience both terminal and non-terminal events, usually dependent, where the event time …