Greetings list!

I know there has been alot of back and forth RE: prediction and lmer. I have a very specific problem at concept level, and would like to know the best approach to reach my goal.

I have developed and validated a patient-specific prognostic model estimating expected "functional independence" in a population of stroke patients.

I wish to extend this model to include "online learning", or conditional prediction, as more information is gathered about the patient.

The model I have developed predicts a (pseudo)continuous measure of functional independence using several baseline factors such as patient age, gender, stroke severity, stroke subtype etc. This generates a fractional polynomial multilevel curve, with confidence intervals generated by MCMC simulations. (see http://rcurve.transform.kcl.ac.uk for prototype interface)

This tool is to be used each week. Therefore at week 2 we have week 1's observed score to include in the estimation. So far I have made this equal the patient-level random effect (i.e. intercept) however this is not the best approach.

I wish to build a model that retains initial predictions, but "adjusts" its subsequent predictions, and confidence of these, based on each additional collected score. Could anyone guide me on how to derive the maths for this? What packages may be suitable? Major pitfalls I may not be seeing?

Many thanks,
Justin


-------------------------------------------------------------------
King's College London
Department of Primary Care and Public Health Sciences
Division of Health and Social Care Research
7th Floor Capital House
42 Weston Street
London SE1 3QD

Tel: 020 7848 6638
Fax: 020 7848 6620


	[[alternative HTML version deleted]]

