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Abstract:
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In this talk, we will discuss some flexible methods to handle both association and temporal sequencing of distinct longitudinal measures, where the measures may be of mixed type (e.g., one continuous, the other binary) and recorded on nonuniform grids and different time points from one another. A smoothing step will be involved. The methods will be demonstrated on a dataset of hemodialysis patients, where longitudinal measures of health outcomes (e.g., infection) were recorded at different time points than longitudinal physiologic measures such as serum C-reactive protein levels (a marker for inflammation). An interesting scientific question to answer is whether experiences of infection follow or predate inflammation.
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