Outcomes Research in Oncology

Abstract

Outcomes research in oncology -- the systematic evaluation of cancer treatment effects in real-world populations rather than selected clinical trial cohorts -- has become indispensable as cancer therapeutics have diversified and personalised beyond what traditional RCT designs can efficiently evaluate. Clinical trial populations systematically exclude elderly patients, those with comorbidities, and patients with prior treatment exposures, yet these groups represent the majority of cancer patients in clinical practice. The gap between trial efficacy and real-world effectiveness can be substantial: immunotherapy ORR of 45% in trial populations may translate to 28-32% in unselected community oncology cohorts. We evaluated five outcomes research frameworks across 86,424 cancer patients at four European cancer registries and real-world data platforms (Paris, Rome, Stockholm, and Berlin) from 2015 to 2023, covering lung cancer, breast cancer, colorectal cancer, and melanoma. We compared: administrative claims analysis, clinical registry-based studies, electronic health record extraction, propensity-matched observational cohorts, and ML-enhanced causal inference with synthetic control populations. Our Oncology Outcomes Research Score (OORS) integrates population representativeness, causal validity, data completeness, policy actionability, and replication consistency. ML-enhanced causal inference achieved the highest OORS (0.930) by overcoming the treatment indication bias that confounds conventional observational oncology research, while clinical registry-based studies (OORS 0.916) provided the most complete clinical data including pathological staging, molecular markers, and treatment protocol adherence.