Understanding Aggregate Data, FIT-Outcomes

 

The data reported in the data system FIT-Outcomes is divided into active and inactive clients, making it possible to compare the effectiveness of the ongoing (active) cases to the cases that have been terminated (inactive).

The first section of data gives a statistical summary of how many clients, treatment episodes and sessions the clinician or agency has. It also calculates average treatment length and drop-out rate.

The second section of data calculates a number of statistical indices related to the effectiveness of the clinician or the agency.

  • Percentage reaching target calculates the percentage of clients in the green zone (above the predictive trajectory of succesful cases).
  • Effect size calculates the effect of treatment compared to no treatment.
  • Relative effect size compares the effect size of the clinician or agency to the grand mean effectsize in the system. The grand mean effectsize a norm generated from 500.000 cases, representing the mean outcome of treatment for the 500.000 cases. If the relative effect size is positive it means the clinician or agency delivers a treatment more effective than the norm. If the relative effect size is negative it means the clinician or agency delivers a treatment less effective than the norm. A relative effect size of 0 means the clinician or agency delivers a treatment that is average compared to the norm.

4 thoughts on “Understanding Aggregate Data, FIT-Outcomes”

      1. Petr Doležal

        Hi Susanne,

        thanks fo answer. Are there any articles describing how it is done? Can I ask what is the numerical range of it?

        Regards

        Petr

        1. Hi Peter

          We are currently working on an article describing the statistical calculations and predictive algorithms of FIT-Outcomes. So far nothing has been published. If you want to ask more about the statistical calculations you can try sending an email to Scott Miller, who owns the Intellectual Property behind FIT-Outcomes: info@scottdmiller.com

          Kind Regards

          Susanne

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