Treatment of chronic pain is associated with high variability in the response to pharmacological interventions. A mathematical pharmacodynamic model was developed to quantify the magnitude and onset/offset times of effect of a single capsaicin 8% patch application in the treatment of painful diabetic peripheral neuropathy in 91 patients. In addition, a mixture model was applied to objectively match patterns in pain-associated behavior. The model identified four distinct subgroups that responded differently to treatment: 3.3% of patients (subgroup 1) showed worsening of pain; 31% (subgroup 2) showed no change; 32% (subgroup 3) showed a quick reduction in pain that reached a nadir in week 3, followed by a slow return towards baseline (16% ± 6% pain reduction in week 12); 34% (subgroup 4) showed a quick reduction in pain that persisted (70% ± 5% reduction in week 12). The estimate of the response-onset rate constant, obtained for subgroups 1, 3, and 4, was 0.76 ± 0.12 week −1 (median ± SE), indicating that every 0.91 weeks the pain score reduces or increases by 50% relative to the score of the previous week (= t½). The response-offset rate constant could be determined for subgroup 3 only and was 0.09 ± 0.04 week −1 (t½ 7.8 weeks). The analysis allowed separation of a heterogeneous neuropathic pain population into four homogenous subgroups with distinct behaviors in response to treatment with capsaicin. It is argued that this model-based approach may have added value in analyzing longitudinal chronic pain data and allows optimization of treatment algorithms for patients suffering from chronic pain conditions.