Kraus, Elisabeth Barbara and Pargent, Florian and Hilbert, Sven and Augustin, Thomas (2025) Introducing the Treatment Decision Framework (TreaDeF) - a Decision Theoretic Approach to Using Evaluation Study Data to Inform Individual Treatment Decisions. COLLABRA-PSYCHOLOGY, 11 (1): 145043. ISSN 2474-7394,
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We present TreaDeF, a formal, uncertainty-aware diagnostic treatment decision framework. TreaDeF is situated within statistical decision theory, merging traditional informal, human-centered and algorithmic decision making. TreaDeF extends existing approaches from psychometrics and econometrics by incorporating measurement uncertainty. It is a first approach towards individualized, transparent diagnostic decision making by incorporating personal characteristics and individual decision preferences. The framework extends the classical decision theoretic setup by estimating the utility function. While treatment options represent actions and personal characteristics, that determine treatment success, represent states; treatment success predictions for each combination of actions and states represent the utility function. Decision rules, like maximum expected utility or maximin applied to a probability-weighted utility criterion, complete the setup. Our implementation involves psychometric measurement models, non-linear prediction models, and numerically approximated decision rules. State uncertainty is determined by measurement error in Rasch models and utility uncertainty is determined by estimation error of generalized additive regression models. Finally, a numeric optimization method is used to apply maximum expected utility and maximin decision functions. A simulated example of a therapist deciding whether to administer exposition therapy is presented, as well as an extensive real-data example showcasing our implementation to a two-component educational training program for reading ability.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TREATMENT RULES; CHOICES; decision theory; measurement error; treatment decision; maximum expected utility; maximin; algorithmic decision making |
| Subjects: | 300 Social sciences > 370 Education |
| Divisions: | Human Sciences > Institut für Bildungswissenschaft > Professur für Methoden der empirischen Bildungsforschung - Prof. Dr. Sven Hilbert |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 06 May 2026 06:38 |
| Last Modified: | 06 May 2026 06:38 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66962 |
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