title: Multivariate survivorship analysis using two cross-sectional samples.
abstract: As an alternative to survival analysis with longitudinal data, I introduce a method that can be applied when one observes the same cohort in two cross-sectional samples collected at different points in time. The method allows for the estimation of log-probability survivorship models that estimate the influence of multiple time-invariant factors on survival over a time interval separating two samples. This approach can be used whenever the survival process can be adequately conceptualized as an irreversible single-decrement process (e.g., mortality, the transition to first marriage among a cohort of never-married individuals). Using data from the Integrated Public Use Microdata Series (Ruggles and Sobek 1997), I illustrate the multivariate method through an investigation of the effects of race, parity, and educational attainment on the survival of older women in the United States.
authors.affiliation: Population Studies Center, University of Pennsylvania, Philadelphia 19104-6298, USA. mhill@pop.upenn.edu
authors.forename: M E
authors.initials: ME
authors.lastname: Hill
mesh_terms: D000368:Aged; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D004522:Educational Status; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D010298:Parity; D044469:Racial Groups; D012307:Risk Factors; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology