The cross-product ratio in bivariate lognormal and gamma distributions, with an application to non-randomized trials

Paul Baxter and Paul Marchant

Journal of Applied Statistics, 2010, vol. 37, issue 4, 529-536

Abstract: Non-randomized trials can give a biased impression of the effectiveness of any intervention. We consider trials in which incidence rates are compared in two areas over two periods. Typically, one area receives an intervention, whereas the other does not. We outline and illustrate a method to estimate the bias in such trials under two different bivariate models. The illustrations use data in which no particular intervention is operating. The purpose is to illustrate the size of the bias that could be observed purely due to regression towards the mean (RTM). The illustrations show that the bias can be appreciably different from zero, and even when centred on zero, the variance of the bias can be large. We conclude that the results of non-randomized trials should be treated with caution, as interventions which show small effects could be explained as artefacts of RTM.

Keywords: bivariate lognormal distribution; crime reduction interventions; Kibble bivariate gamma distribution; non-randomized trials; regression towards the mean (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1080/02664760902744962

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