Abstract
Chronic disease usually spans years of a person’s lifetime and includes a disease free period, a preclinical, or latent period, where there are few overt signs of disease, a clinical period where the disease manifests and is eventually diagnosed, and a follow-up period where the disease might progress steadily or remain stable. It is often of interest to investigate the relationship between risk factors measured at a point in time (usually during the disease free or preclinical period), and the development of disease at some future point (e.g., 10 years later). We outline some popular designs for the identification of subjects and discuss issues in measurement of risk factors for analysis of chronic disease. We discuss some of the complexities in these analyses, including the time dependent nature of the risk factors and missing data issues. We then describe some popular statistical modeling techniques and outline the situations in which each is appropriate. We conclude with some speculation toward future development in the area of chronic disease data and analysis.
DOI
10.22237/jmasm/1036108860
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
