Computing estimates of incidence, including lifetime risk: Alzheimer's Disease in the Framingham Study. The Practical Incidence Estimators (PIE) macro

Alexa Beiser, Ralph B. D'Agostino, Sudha Seshadri, Lisa M. Sullivan, Philip A. Wolf

Research output: Contribution to journalArticlepeer-review

140 Scopus citations


The incidence of disease is estimated in medical and public health applications using various different techniques presented in the statistical and epidemiologic literature. Many of these methods have not yet made their way to popular statistical software packages and their application requires custom programming. We present a macro written in the SAS macro language that produces several estimates of disease incidence for use in the analysis of prospective cohort data. The development of the Practical Incidence Estimators (PIE) Macro was motivated by research in Alzheimer's Disease (AD) in the Framingham Study in which the development of AD has been prospectively assessed over an observation period of 24 years. The PIE Macro produces crude and age-specific incidence rates, overall and stratified by the levels of a grouping variable. In addition, it produces age-adjusted rates using direct standardization to the combined group. The user specifies the width of the age groups and the number of levels of the grouping variable. The PIE macro produces estimates of future risk for user-defined time periods and the remaining lifetime risk conditional on survival event-free to user-specified ages. This allows the user to investigate the impact of increasing age on the estimate of remaining lifetime risk of disease. In each case, the macro provides estimates based on traditional unadjusted cumulative incidence, and on cumulative incidence adjusted for the competing risk of death. These estimates and their respective standard errors, are provided in table form and in an output data set for graphing. The macro is designed for use with survival age as the time variable, and with age at entry into the study as the left-truncation variable; however, calendar time can be substituted for the survival time variable and the left-truncation variable can simply be set to zero. We illustrate the use of the PIE macro using Alzheimer's Disease incidence data collected in the Framingham Study. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)1495-1522
Number of pages28
JournalStatistics in Medicine
Issue number11-12
StatePublished - Jun 15 2000
Externally publishedYes

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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