Person Mobility in the Design and Analysis of Cluster-Randomized Cohort Prevention Trials

Person mobility is an inescapable fact of life for most cluster-randomized (e.g., schools, hospitals, clinic, cities, state)
cohort prevention trials. Mobility rates are an important substantive consideration in estimating the effects of an intervention.
In cluster-randomized trials, mobility rates are often correlated with ethnicity, poverty and other variables associated with
disparity. This raises the possibility that estimated intervention effects may generalize to only the least mobile segments
of a population and, thus, create a threat to external validity. Such mobility can also create threats to the internal validity
of conclusions from randomized trials. Researchers must decide how to deal with persons who leave study clusters during a
trial (dropouts), persons and clusters that do not comply with an assigned intervention, and persons who enter clusters during
a trial (late entrants), in addition to the persons who remain for the duration of a trial (stayers). Statistical techniques
alone cannot solve the key issues of internal and external validity raised by the phenomenon of person mobility. This commentary
presents a systematic, Campbellian-type analysis of person mobility in cluster–randomized cohort prevention trials. It describes
four approaches for dealing with dropouts, late entrants and stayers with respect to data collection, analysis and generalizability.
The questions at issue are: 1) From whom should data be collected at each wave of data collection? 2) Which cases should be
included in the analyses of an intervention effect? and 3) To what populations can trial results be generalized? The conclusions
lead to recommendations for the design and analysis of future cluster-randomized cohort prevention trials.