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Estimating a population cumulative incidence under calendar time trends.
BMC Med Res Methodol. 2017 Jan 11;17(1):7
Authors: Hansen SN, Overgaard M, Andersen PK, Parner ET
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.
PMID: 28077076 [PubMed – indexed for MEDLINE]
via https://www.ncbi.nlm.nih.gov/pubmed/28077076?dopt=Abstract