Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study.

ABSTRACT:

Background

Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.

Methods

In this paper we compare joint maximum likelihood metho

SUBMITTER: Ngwa JS 

PROVIDER: S-EPMC7876802 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature