The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration
By Robert J. DeRubeis, Z. Cohen, Nicholas R Forand, J. Fournier, L. Gelfand, Lorenzo Lorenzo‐Luaces. 2014
446 citations recorded by the source corpus.
Background Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. Results For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01). Conclusions This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.
Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Pharmacology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Experimental and Cognitive Psychology
- Christine Knaevelsrud
- C. Schaeuffele
- Ava Schulz
- Johanna Boettcher
- B. Renneberg
- Przemyslaw Biecek
- Bernd Bischl
- Christoph Molnar
In Management Science and Operations Research
Citations in the corpus
- Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “The goal of the analysis in DeRubeis et al. (2014) is to understand how different subjects respond to different treatments, conditional on their personal covariates.”
- CBT at the Crossroads: The Rise of Transdiagnostic Treatments (2020, background): “Optimally matching patients to treatments according to certain baseline characteristics has the potential to enhance treatment outcomes (Cohen and DeRubeis 2018; DeRubeis et al. 2014).”