Nomogram to Predict the Benefit of Intensive Treatment for Locoregionally Advanced Head and Neck Cancer.

PURPOSE: Previous studies indicate that the benefit of therapy depends on patients' risk for cancer recurrence relative to noncancer mortality (ω ratio). We sought to test the hypothesis that patients with head and neck cancer (HNC) with a higher ω ratio selectively benefit from intensive therapy. EXPERIMENTAL DESIGN: We analyzed 2,688 patients with stage III-IVB HNC undergoing primary radiotherapy (RT) with or without systemic therapy on three phase III trials (RTOG 9003, RTOG 0129, and RTOG 0522). We used generalized competing event regression to stratify patients according to ω ratio and compared the effectiveness of intensive therapy as a function of predicted ω ratio (i.e., ω score). Intensive therapy was defined as treatment on an experimental arm with altered fractionation and/or multiagent concurrent systemic therapy. A nomogram was developed to predict patients' ω score on the basis of tumor, demographic, and health factors. Analysis was by intention to treat. RESULTS: Decreasing age, improved performance status, higher body mass index, node-positive status, P16-negative status, and oral cavity primary predicted a higher ω ratio. Patients with ω score ≥0.80 were more likely to benefit from intensive treatment [5-year overall survival (OS), 70.0% vs. 56.6%; HR of 0.73, 95% confidence interval (CI): 0.57-0.94; P = 0.016] than those with ω score <0.80 (5-year OS, 46.7% vs. 45.3%; HR of 1.02, 95% CI: 0.92-1.14; P = 0.69; P = 0.019 for interaction). In contrast, the effectiveness of intensive therapy did not depend on risk of progression. CONCLUSIONS: Patients with HNC with a higher ω score selectively benefit from intensive treatment. A nomogram was developed to help select patients for intensive therapy.

Generalized Competing Event Models Can Reduce Cost and Duration of Cancer Clinical Trials.

PURPOSE: Generalized competing event (GCE) models improve stratification of patients according to their risk of cancer events relative to competing causes of mortality. The potential impact of such methods on clinical trial power and cost, however, is uncertain. We sought to test the hypothesis that GCE models can reduce estimated clinical trial cost in elderly patients with cancer. METHODS: Patients with nonmetastatic head and neck (n = 9,677), breast (n = 22,929), or prostate cancer (n = 51,713) were sampled from the SEER-Medicare database. Using multivariable Cox proportional hazards models, we compared risk scores for all-cause mortality (ACM) and cancer-specific mortality (CSM) with GCE-based risk scores for each disease. We applied a cost function to estimate the cost and duration of clinical trials with a primary end point of overall survival in each population and in high-risk subpopulations. We conducted sensitivity analyses to examine model uncertainty. RESULTS: For the purpose of enriching subpopulations, GCE models reduced estimated clinical trial cost compared with Cox models of ACM and CSM in all disease sites. The relative cost reductions with GCE models compared with ACM and CSM models, respectively, were -68.4% and -14.4% in prostate cancer, -38.8% and -18.3% in breast cancer, and -17.1% and -4.1% in head and neck cancer. Cost savings in breast and prostate cancers were on the order of millions of dollars. The GCE model also reduced relative clinical trial duration compared with CSM and ACM models for all disease sites. The optimal risk score cutoff for clinical trial enrollment occurred near the top tertile for all disease sites. CONCLUSION: GCE models have significant potential to improve clinical trial efficiency and reduce cost, with a potentially large impact in prostate and breast cancers.

Integrated phase II/III clinical trials in oncology: a case study.

BACKGROUND: Integrated phase II/III trial designs implement the phase II and phase III aspects of oncology studies into a single trial. Despite a body of literature discussing the merits of integrated phase II/III clinical trial designs within the past two decades, implementation of this design has been limited in oncology studies. PURPOSE: We provide a brief discussion of the potential advantages and disadvantages of integrated phase II/III clinical trial designs in oncology and provide an example of the operating characteristics of a Radiation Therapy Oncology Group (RTOG) trial. METHODS: We review the differences among proposed integrated phase II/III designs. Then, we illustrate the use of the design in a brain tumor trial to be conducted by the RTOG and examine the impact of association between endpoints on design performance in terms of type I error, power, study duration, and expected sample size. RESULTS: Although integrated phase II/III designs should not be used in all situations, under appropriate conditions, significant gains can be achieved when using integrated phase II/III designs, including smaller sample size, time and resources savings, and shorter study duration. LIMITATIONS: Data submission without delay and sufficient evaluation of intermediate endpoints are assumed. CONCLUSIONS: Although there are potential benefits in using phase II/III designs, there also may be disadvantages. We recommend running design simulations incorporating theoretical and practical issues before implementing an integrated phase II/III design.