Estimating a population cumulative incidence under calendar time trends.

Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE

Original Publication: London : BioMed Central, [2001-

Algorithms* ; Models, Theoretical*; Mental Disorders/*epidemiology ; Risk Assessment/*statistics & numerical data; Adolescent ; Adult ; Attention Deficit Disorder with Hyperactivity/diagnosis ; Attention Deficit Disorder with Hyperactivity/epidemiology ; Autism Spectrum Disorder/diagnosis ; Autism Spectrum Disorder/epidemiology ; Child ; Computer Simulation ; Denmark/epidemiology ; Female ; Finland/epidemiology ; Humans ; Incidence ; Kaplan-Meier Estimate ; Male ; Mental Disorders/diagnosis ; Middle Aged ; Obsessive-Compulsive Disorder/diagnosis ; Obsessive-Compulsive Disorder/epidemiology ; Prevalence ; Proportional Hazards Models ; Risk Assessment/methods ; Risk Factors ; Time Factors ; Tourette Syndrome/diagnosis ; Tourette Syndrome/epidemiology ; Young Adult

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.

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Keywords: Cumulative incidence; Dependent censoring; Stratification; Time to event; Time trends

Date Created: 20170113 Date Completed: 20171106 Latest Revision: 20181113

20240829

PMC5225659

10.1186/s12874-016-0280-6

28077076