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We consider studies where multiple measures on an outcome variable arecollected over time, but some subjects drop out before the end of follow up.Analyses of such data often proceed under either a 'last observation carriedforward' or 'missing at random' assumption. We consider two alternativestrategies for identification; the first is closely related to thedifference-in-differences methodology in the causal inference literature. Thesecond enables correction for violations of the parallel trend assumption, solong as one has access to a valid 'bespoke instrumental variable'. These arecompared with existing approaches, first conceptually and then in an analysisof data from the Framingham Heart Study.

Alternative approaches for analysing repeated measures data that are missing not at random