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I have a data set containing a daily measurement recorded from 20 participants for 60 days. I am trying to develop methods for predicting/estimating decline in long-term monitoring studies, i.e. can measurement of a parameters on a daily basis be used to detect/predict functional decline by identification of negative trends or abberant measurements. I hope to be able to generate an index of decline by examining the trends (using some combination of sensor derived parameters and referencing these to baseline clinical data (pre-trial clinical assessment including clinical scales meauring frailty and balance) and a daily health questionaire. 60 days was chosen as the maximum practical length of time where we could expect to see changes, also we do not have ethical approval to collect beyond this date.

What is the most appropriate method to define a baseline measurement for each participant and how do I best predict/detect negative trending or abberant behaviour in unseen data?

'Decline' in this case is somewhat difficult to define and in fact it is one of the research questions of this study (i.e. how do you identify decline relative to a baseline clinical assessment using daily measurements) however our intention would be to obtain statistically significant metrics that imply some clinical significance.

I have been using intra-class correlation coefficients and repeated measures ANOVA to examine the stability of each measurement over time. I am fitting a linear regression model (with a time trend) to investigate trends.

I wondered if I could get some other opinions on the soundness of this approach?

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Perhaps you could tell us a bit more to help us think about your question. Do you have any idea what amount of decline is important to detect? If so, what is that amount? Is 60 days a long time period for your purposes or a short amount of time? Are you planning to repeat this study or is this data set all you plan to gather? Thanks. – Joel W. Sep 16 '12 at 2:09
Just edited to better explain the question. – BGreene Sep 20 '12 at 10:19
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Is this restatement correct: You have one measure per day for 60 days and want to predict declines later in the 60 days based on measures earlier in the 60 days. (I do not know what you mean by aberrant measures.) You may want to get change measures, perhaps 59 day-to-day change measures. Then you can see if these change measures predict later (larger) changes that are of interest to you. If variance is large, you might look at changes in week to week averages for each participant. Does that make any sense in light of your specific project? – Joel W. Sep 20 '12 at 13:32
I am measuring each patient using a sensor once per day. There are multiple measures derived from the sensor per patient per day – BGreene Sep 20 '12 at 14:55
How do you define a decline? Is it a change in one measure, a change in an average of several or all the measures, or some clinical judgment? – Joel W. Sep 20 '12 at 17:14
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