Estimating regression to the mean and true effects of an intervention in a four-wave panel study

First, to analyse whether a taxation-related decrease in spirit prices had a similar effect on spirit consumption for low-, medium- and high-level drinkers. Secondly, as the relationship between baseline values and post-intervention changes is confounded with regression to the mean (RTM) effects, to apply different approaches for estimating the RTM effect and true change.

Consumption of spirits and total alcohol consumption were analysed in a four-wave panel study (one pre-intervention and three post-intervention measurements) of 889 alcohol consumers sampled from the general population of Switzerland.

Two correlational methods, one method quantitatively estimating the RTM effect and one growth curve approach based on hierarchical linear models (HLM), were used to estimate RTM effects among low-, medium- and high-level drinkers.

Adjusted for RTM effects, high-level drinkers increased consumption more than lighter drinkers in the short term, but this was not a persisting effect. Changes in taxation affected mainly light and moderate drinkers in the long term. All methods concurred that RTM effects were present to a considerable degree, and methods quantifying the RTM effect or adjusting for it yielded similar estimates.

Intervention studies have to consider RTM effects both in the study design and in the evaluation methods. Observed changes can be adjusted for RTM effects and true change can be estimated. The recommended method, particularly if the aim is to estimate change not only for the sample as a whole, but for groups of drinkers with different baseline consumption levels, is growth curve modelling. If reliability of measurement instruments cannot be increased, the incorporation of more than one pre-intervention measurement point may be a valuable adjustment of the study design.