Using Laplace Regression to Model and Predict Percentiles of Age at Death When Age Is the Primary Time Scale

Am J Epidemiol. 2015 Aug 1;182(3):271-7. doi: 10.1093/aje/kwv033. Epub 2015 Jun 20.


Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies.

Keywords: Laplace regression; age; survival analysis; survival percentiles; time scale.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Comorbidity
  • Death Certificates
  • Female
  • Follow-Up Studies
  • Humans
  • Kaplan-Meier Estimate
  • Life Expectancy*
  • Linear Models
  • Male
  • Middle Aged
  • Models, Statistical*
  • Smoking / epidemiology
  • Survival Analysis*
  • Survival Rate
  • Sweden / epidemiology