To propose statistical measures to identify different longitudinal patterns of change in quantitative health indicators.
The authors propose several simple measures to discriminate between stable-unstable, increasing-decreasing, linear-nonlinear, monotonic-nonmonotonic patterns of change. They then suggest using factor analysis to select the subset of nonredundant measures, and cluster analysis, based on the selected measures, to identify subgroups of patients with similar longitudinal trajectories. The proposed approach is illustrated using data on osteoarthritis disability from a longitudinal study undertaken in Toronto, Ontario, in 1996-2001. Disability was measured at four points in time for 835 patients, using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index.
The proposed measures allowed the detection of individual patients with specific patterns of change and identification of four different groups of patients with WOMAC scores that are (1) regularly increasing, (2) regularly decreasing, (3) stable over time, or (4) highly unstable, with abrupt changes or short-term fluctuations.
The proposed approach may be used to (1) screen even large databases to identify particular patterns of change; or (2) summarize different patterns of change by classifying patients into groups with similar trajectories. Once identified, the groups can be investigated to determine whether there are differences in patient characteristics or outcomes.