Purpose: In order to guide decision-making based on the results of a pivotal oncologic therapeutics trial, we re-analyzed the International Adjuvant Lung Trial (IALT) in the context of prior conflicting evidence. Methods: In contrast to prior data, IALT demonstrated that cisplatin-based adjuvant chemotherapy provides a 4.1% (P<0.03) absolute survival benefit at 5 years for patients with resected stage I–III non–small cell lung cancer (NSCLC). Through a literature review, we identified a 1995 lung cancer therapeutics meta-analysis as the most comprehensive summary of the evidence prior to the initiation of patient accrual to the IALT. The meta-analysis did not show a survival benefit for adjuvant chemotherapy. We constructed a prior probability curve based on the meta-analysis results. Utilizing Bayesian methods, we updated the meta-analysis prior probability curve with the results of the IALT. For a sensitivity analysis, we varied the prior probability of a survival benefit based on: 1) an expert opinion survey of thoracic oncologists and 2) a skeptical curve representing extreme uncertainty and pessimism about a survival benefit. We developed, administered and analyzed the expert opinion survey prior to the publication of the results of the IALT. The skeptical curve was developed mathematically. Results: Our Bayesian analysis confirmed an overall survival benefit for cisplatin-based adjuvant chemotherapy for resected stage I–III NSCLC when the IALT is considered in the context of prior conflicting data. However, the posterior probability survival benefit level is lower than the reported IALT result. The sensitivity analysis suggests that the IALT data support a 3% survival advantage with a probability of 80%, 71%, and 54%, for the meta-analysis, survey, and skeptical prior probability curves, respectively. The posterior probability of a survival decrement was below 1% for each prior probability curve. Subsequent clinical trials confirm these Bayesian findings. Conclusions: By providing a transparent, precise and reproducible quantitative evaluation of a pivotal oncologic trial in the context of prior conflicting evidence, Bayesian methods may aid decision-making for clinicians, patients and researchers.