The objective of this study was to gain an understanding of the effects
of population heterogeneity, missing data, and causal
relationships on parameter estimates from statistical models when
analyzing change in medication use. From a public health
perspective, two timely topics were addressed: the use and
effects of statins in populations in primary prevention of
cardiovascular disease and polypharmacy in older population.



Growth mixture models were applied to characterize the
accumulation of cardiovascular and diabetes medications among
apparently healthy population of statin initiators. The causal effect of
statin adherence on the incidence of acute cardiovascular events was
estimated using marginal structural models in comparison with
discrete-time hazards models. The impact of missing data on the growth
estimates of evolution of polypharmacy was examined comparing
statistical models under different assumptions for missing data
mechanism. The data came from Finnish administrative registers and from
the population-based Geriatric Multidisciplinary Strategy for the Good
Care of the Elderly study conducted in Kuopio, Finland, during 2004–07.



Five distinct patterns of accumulating medications emerged among the
population of apparently healthy statin initiators during two
years after statin initiation. Proper accounting for time-varying
dependencies between adherence to statins and confounders using
marginal structural models produced comparable estimation results
with those from a discrete-time hazards model. Missing data mechanism
was shown to be a key component when estimating the evolution of
polypharmacy among older persons.



In conclusion, population heterogeneity, missing data and causal
relationships are important aspects in longitudinal studies that
associate with the study question and should be critically assessed
when performing statistical analyses. Analyses should be supplemented
with sensitivity analyses towards model assumptions.