IJE Advance Access originally published online on August 19, 2004
International Journal of Epidemiology 2004 33(6):1389-1397; doi:10.1093/ije/dyh276
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IJE vol.33 no.6 © International Epidemiological Association 2004; all rights reserved.
Education and Debate |
Interval estimation by simulation as an alternative to and extension of confidence intervals
Departments of Epidemiology and Statistics, University of California, Los Angeles, Los Angeles, CA 90095–1772, USA. E-mail: lesdomes{at}ucla.edu
There are numerous techniques for constructing confidence intervals, most of which are unavailable in standard software. Modern computing power allows one to replace these techniques with relatively simple, general simulation methods. These methods extend easily to incorporate sources of uncertainty beyond random error. The simulation concepts are explained in an example of estimating a population attributable fraction, a problem for which analytical formulas can be quite unwieldy. First, simulation of conventional intervals is illustrated and compared to bootstrapping. The simulation is then extended to include sampling of bias parameters from prior distributions. It is argued that the use of almost any neutral or survey-based prior that allows non-zero values for bias parameters will produce an interval estimate less misleading than a conventional confidence interval. Along with simplicity and generality, the ease with which simulation can incorporate these priors is a key advantage over conventional methods.
Keywords Attributable fraction, attributable risk, Bayesian methods, bias, bootstrapping, confidence intervals, confounding, meta-analysis, Monte Carlo methods, relative risk, risk analysis, simulation
Accepted 2 June 2004
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