OBJECTIVES: To illustrate how claims data can be used to (1) develop outcome scores that predict response to a traditional treatment and (2) estimate the economic impact of individualized assignment to a newer treatment based on the outcome score. An example application is based on two treatments for attention deficit hyperactivity disorder (ADHD): osmotic-release oral system methylphenidate (OROS-MPH) and lisdexamfetamine dimesylate (LDX). METHODS: Adolescents with ADHD initiating OROS-MPH (n=6320) or LDX (n=6394) were selected from the MarketScan claims database. A model was developed for predicting risk of switching/augmentation with OROS-MPH using multiple baseline characteristics. The model was applied to an independent sample to stratify patients by their predicted risk and, within each stratum, risk of switching/augmentation and ADHD-related total costs were compared between OROS-MPH and LDX patients using inverse probability of treatment weighting. RESULTS: The prediction model resulted in substantial stratification, showing risk of switching/augmentation with OROS-MPH ranging from 11.3-42.1%. In the two strata where OROS-MPH had highest risk of switching/augmentation, LDX had significantly lower risk of switching/augmentation than OROS-MPH (by 7.0-8.2%) and lower ADHD-related annual total costs (by $264-$625 per patient). LIMITATIONS: The current study has used the risk of switching/augmentation as a proxy measure for treatment efficacy to establish the prediction model. Future research using a clinical measure for ADHD symptoms is warranted to verify the findings. CONCLUSIONS: Combining multiple patient characteristics into a predicted score for treatment outcomes with a traditional treatment can help identify subgroups of patients who benefit most from a new treatment. In this analysis, ADHD patients with a high predicted score for switching/augmentation with OROS-MPH had a lower rate of switching/augmentation with LDX. Assigning OROS-MPH and LDX treatments based on the predicted scores that are heterogeneous in a patient population may help improve clinical outcomes and the cost-effectiveness of care.
OBJECTIVES: To illustrate how claims data can be used to (1) develop outcome scores that predict response to a traditional treatment and (2) estimate the economic impact of individualized assignment to a newer treatment based on the outcome score. An example application is based on two treatments for attention deficit hyperactivity disorder (ADHD): osmotic-release oral system methylphenidate (OROS-MPH) and lisdexamfetamine dimesylate (LDX). METHODS: Adolescents with ADHD initiating OROS-MPH (n=6320) or LDX (n=6394) were selected from the MarketScan claims database. A model was developed for predicting risk of switching/augmentation with OROS-MPH using multiple baseline characteristics. The model was applied to an independent sample to stratify patients by their predicted risk and, within each stratum, risk of switching/augmentation and ADHD-related total costs were compared between OROS-MPH and LDXpatients using inverse probability of treatment weighting. RESULTS: The prediction model resulted in substantial stratification, showing risk of switching/augmentation with OROS-MPH ranging from 11.3-42.1%. In the two strata where OROS-MPH had highest risk of switching/augmentation, LDX had significantly lower risk of switching/augmentation than OROS-MPH (by 7.0-8.2%) and lower ADHD-related annual total costs (by $264-$625 per patient). LIMITATIONS: The current study has used the risk of switching/augmentation as a proxy measure for treatment efficacy to establish the prediction model. Future research using a clinical measure for ADHD symptoms is warranted to verify the findings. CONCLUSIONS: Combining multiple patient characteristics into a predicted score for treatment outcomes with a traditional treatment can help identify subgroups of patients who benefit most from a new treatment. In this analysis, ADHDpatients with a high predicted score for switching/augmentation with OROS-MPH had a lower rate of switching/augmentation with LDX. Assigning OROS-MPH and LDX treatments based on the predicted scores that are heterogeneous in a patient population may help improve clinical outcomes and the cost-effectiveness of care.