Conditional models accounting for regression to the mean in observational multi‐wave panel studies on alcohol consumption

Abstract

To develop statistical methodology needed for studying whether effects of an acute-onset intervention differ by consumption group that accounts correctly for the effect of regression to the mean (RTM) in observational panel studies with three or more measurement waves. A general statistical modelling framework, based on conditional models, is presented for analysing alcohol panel data with three or more measurements, that models the dependence between initial drinking level and change in consumption controlling for RTM. The method is illustrated by panel data from Finland, southern Sweden and Denmark, where the effects of large changes in alcohol taxes and travellers' allowances were studied. The suggested model allows for drawing statistical inference of the parameters of interest and also the identification of non-linear effects of an intervention by initial consumption using standard statistical software modelling tools. There was no evidence in any of the countries of the changes being larger among heavy drinkers, but in southern Sweden there was evidence that light drinkers raised their level of consumption. Conditional models are a versatile modelling framework that offers a flexible tool for modelling and testing changes due to intervention in consumption by initial consumption while controlling simultaneously for RTM.