A simple method of determining confidence intervals for population attributable risk from complex surveys.
Methods to assess uncertainty in the estimated population attributable risk (PAR) by calculating 95 per cent confidence intervals (CIs) are not readily available in software for complex sample surveys. Using the Bonferroni inequality, a simple method to obtain CIs for the PAR is developed. The method is demonstrated using a simulation in a (2 x 2) table as well as a cohort study to calculate CIs for PAR of coronary heart disease death (using proportional hazards regression). This article demonstrates a straightforward, theoretically valid method of determining CIs for the PAR. Using this method, researchers analysing complex surveys can routinely provide a population perspective and a valid measure of the uncertainty for these estimates.
Natarajan
Sundar
S
VA New York Harbor Healthcare System, New York, NY 10010, USA. sundar.natarajan@med.nyu.edu
Lipsitz
Stuart R
SR
Rimm
Eric
E
D015331:Cohort Studies; D016001:Confidence Intervals; D006306:Health Surveys; D006323:Heart Arrest / Q000401:mortality; D006801:Humans; D016016:Proportional Hazards Models; D018570:Risk Assessment; D035501:Uncertainty; D014481:United States