Analysis strategies for serial multivariate ultrasonographic data that are incomplete

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Abstract
Ultrasonographic measurement of intima‐media thicknesses in the carotid artery has emerged as an important non‐invasive means of assessing atherosclerosis, and has served to define primary outcome measures related to progression of arterial lesions in several large clinical trials and epidemiologic studies. It is characteristic that measurements often cannot be obtained from all sites during repeated examinations. This leads to incomplete multivariate serial data, for which the set and number of visualized sites may vary across time. We have contrasted several conditional and unconditional maximum likelihood analytical approaches, and have evaluated these with a simulation experiment based on characteristics of ultrasound measurements collected during the course of the Asymptomatic Carotid Artery Plaque Study. We examined analyses based on unweighted and generalized least squares regression in which we estimated cross‐sectional summary statistics using raw means, unconditional maximum likelihood estimates and full maximum likelihood estimates. Since the genesis of missing data is not fully clear, and since the approaches we examined are based, to some degree, on the assumption that data are missing at random, we also examined the relative impact of deviations from such an assumption on each of the approaches considered. We found that maximum likelihood based approaches increased the expected efficiency of the analysis of serial ultrasound data over ignoring missing data by up to 21 per cent.

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