title: An analytic method for longitudinal mortality studies.
abstract: Our knowledge of mortality risks comes largely from longitudinal (cohort) studies. The most commonly used analytic tool is the Cox proportional hazards model for survival analysis. An alternative approach is a simple cross-sectional analysis of person-years. The key to the method is logistic regression, where the outcome variable is lived/died in the given year and the explanatory variables are age, sex, and other potential risk factors. This approach can be used to model any dichotomous outcome and has several important advantages over the more traditional survival analysis. As an example, we compare the two methods using a large data base of patients with spinal cord injury.
authors.affiliation: Department of Statistics, University of California, Riverside, CA 92521-0138, USA.
authors.forename: D
authors.initials: D
authors.lastname: Strauss
authors.forename: R
authors.initials: R
authors.lastname: Shavelle
authors.forename: M J
authors.initials: MJ
authors.lastname: DeVivo
authors.forename: S
authors.initials: S
authors.lastname: Day
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D005260:Female; D006801:Humans; D008017:Life Expectancy; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009026:Mortality; D016016:Proportional Hazards Models; D013119:Spinal Cord Injuries / Q000401:mortality; D016019:Survival Analysis; D014481:United States