OBJECTIVES: Patient Reported Outcomes (PROs) are routinely employed in patient-focused drug development programs. However, PRO data are often under-utilized. Better approaches could employ trial data to describe expected patient outcomes. The objective of this work was to present a method for leveraging PRO-based patient profiles to project and thereby anticipate expected patient outcomes.

METHODS: A model-based approach to analyzing PRO scores allows for a more tailored approach to evaluating outcomes of interest. To demonstrate this approach, we analyzed PRO data from a Phase 3 trial of patients with prostate cancer randomized to either Hypofractionated 3D-CRT/MRT (experimental arm) or Conventionally Fractionated 3D-CRT/MRT (comparator arm). The outcome of interest was the difference between treatment arms on the Expanded Prostate cancer Index Composite (EPIC) urinary domain scores, defined on the sum of responses to 12 items. A longitudinal regression model was fit to EPIC scores at 6, 12, 24, and 60 months post-baseline. Patient profile variables (baseline EPIC urinary scores, treatment arm, age, race, clinical T-stage, and Gleason score) were included as predictors. For a particular patient profile of interest, expected EPIC urinary score at 6 months was estimated, as well as associated 95% confidence intervals.

RESULTS: To illustrate the approach, a single profile is presented here. At baseline, a patient receiving the experimental treatment, EPIC urinary score 98.0, age 57, white, clinical T-stage T2, Gleason score 6, could be expected to report an EPIC urinary score of 94.3 (92.06, 96.00) at 6 months. One subject matched this profile and their EPIC score was 93.8 at 6 months, which was 0.53% lower than 94.3, indicating accuracy of proposed procedure. This corresponds to ~1-category decrease on two of the 12 domain items (i.e., a decrease in symptom severity).

CONCLUSIONS: Well-established statistical methods can improve clinician understanding of patient-profile-specific expected outcomes.