OBJECTIVES:  Capturing QOL information in orphan indications presents multiple barriers.  Patient recruitment is challenging due to low incidence and severity of the condition.  An additional consideration is the inability to make statistical inference due to low sample sizes.  Outcomes research teams must consider novel methods for optimizing data collection and analysis within these populations. This case study evaluates the methods to address QOL data gaps in RR-DTC, a hard-to-treat, rare cancer. METHODS:  In a real-world observational study of patients with RR-DTC, a mixed-methods quantitative survey and qualitative data collection is being conducted to ascertain direct patient feedback through survey data collection and patient interviews.  Sampling of patients is being done from the target population in a real-world setting. This mixed-methods approach allows for an all-encompassing understanding of the patient’s treatment experience and relevance of qualitative change to quantitative reporting within the clinical trial.  Study documents capture symptoms and impact data cross-sectionally and longitudinally and to allow for the cross-linking of survey results to the clinical data.  RESULTS:  Data will be presented on a sample of 120 patients: 50 currently undergoing systemic therapy with sorafenib, 50 with lenvatinib, and 20 receiving no systemic therapy. QOL endpoints include the Functional Assessment of Cancer Therapy-General (FACT-G), EQ-5D-5L, Symptoms & Toxicities experience, Impressions of Treatment Satisfaction, and Subject Preference.  In addition, data from qualitative interview of 40 patients will be presented to fully conceptualize quality of survival. The methods for patient recruitment and barriers to implementation will be presented as well as mixed methodology for interpreting quantitative survey data using supportive qualitative information. CONCLUSIONS: In absence of phase 3 QOL data in rare indications, a mixed-method approach utilizing real-word observational study can yield important information that supports previously demonstrated survival benefit and help our understanding of treatment impact on quality of survival.