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Trajectory of change

One of the most effective tools of outcomes informed care is the technique of monitoring the patient's change on the outcome questionnaires against an expected "trajectory of change". This is sometimes called the prediction approach. If the patient's rate of change falls outside of an expected range of scores, the case is thought to be off track, prompting the clinician to review the treatment carefully and take steps to assure that the patient does not terminate prematurely.

Michael Lambert's research on feedback to reduce treatment failures is based on this idea, and virtually every outcomes management system in use today makes use of a predicted trajectory of change.

One method to estimate expected change is to use a large normative sample to calculate the regression formula (slope and intercept) and to use the test scores at intake to predict scores at subsequent measurement points, using the GeneralLinearModel.

While this method will estimate scores at each measurement point for patients who have remained in treatment, the variance of the residual scores is quite large, so that by the second or third measurement point the standard deviation of the residuals is as large as the standard deviations of the test score at time one.

To view an example of a Trajectory of Change Graph employed by the ACORN outcomes management system, download this Excel workbook. This particular example plots the trajectory of change at three week measurement intervals.


One cautionary note: While the Trajectory of Change graph is useful to visualize average scores at different measurement intervals, it is probably a mistake to think in terms of "momentum" or "inertia", in the sense that prior change may not be very predictive of subsequent change. For more on this phenomena, see the topic RandomWalkHypothesis.

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-- JebBrown - 27 Feb 2007

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