Estimating a population cumulative incidence under calendar time trends. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Hansen, Stefan; Overgaard, Morten; Andersen, Per; Parner, Erik
• Is Part Of: BMC medical research methodology. Volume 17:Issue 1(2017)
• Page Start: 1
• Page End: 10
• Publication Date: 2017-12
• Subjects: Cumulative incidence -- Time to event -- Dependent censoring -- Stratification -- Time trends; Medicine -- Research -- Methodology -- Periodicals; 610.72
• DOI: 10.1186/s12874-016-0280-6
• Languages: English
• ISSNs: 1471-2288

Abstract

Abstract Background The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan–Meier or Aalen–Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk. Methods We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan–Meier and Aalen–Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis. Results We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period. Conclusions Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives.