Clinical significance in the number of involved lymph nodes in patients that underwent surgery for pathological stage III-N2 non-small cell lung cancer

<p>Abstract</p> <p>Purpose</p> <p>This study investigated whether the number of involved lymph nodes is associated with the prognosis in patients that underwent surgery for pathological stage (p-stage) III/N2 NSCLC.</p> <p>Subjects</p> <p>This study evaluated 121 patients with p-stage III/N2 NSCLC.</p> <p>Results</p> <p>The histological types included 65 adenocarcinomas, 39 squamous cell carcinomas and 17 others. The average number of dissected lymph nodes was 23.8 (range: 6-55). The average number of involved lymph nodes was 5.9 (range: 1-23). The 5-year survival rate of the patients was 51.0% for single lymph node positive, 58.9% for 2 lymph nodes positive, 34.2% for 3 lymph nodes positive, and 30.0% for 4 lymph nodes positive, and 20.4% for more than 5 lymph nodes positive. The patients with either single or 2 lymph nodes positive had a significantly more favorable prognosis than the patients with more than 5 lymph nodes positive. A multivariate analysis revealed that the number of involved lymph nodes was a significant independent prognostic factor.</p> <p>Conclusion</p> <p>Surgery appears to be preferable as a one arm of multimodality therapy in p-stage III/N2 patients with single or 2 involved lymph nodes. The optimal incorporation of surgery into the multimodality approach therefore requires further clinical investigation.</p

Meta-analysis of time-to-event data: a comparison of two-stage methods

Meta-analysis is widely used to synthesise results from randomised trials. When the relevant trials collected time-to-event data, individual participant data are commonly sought from all trials. Meta-analyses of time-to-event data are typically performed using variants of the log-rank test, but modern statistical software allows for the use of maximum likelihood methods such as Cox proportional hazards models or interval-censored logistic regression. In this paper, the different approaches to the analysis of time-to-event data are examined and compared with show that log-rank test approaches are in fact first-order approximations to the maximum likelihood methods and that some methods assume proportional hazards, whereas others assume proportional odds. A simulation study is performed to compare the different methods, which shows that log-rank test approaches give biased estimates when the underlying hazard ratio or odds ratio is far from unity. It also shows that proportional hazards methods give biased results when hazards are not proportional, and proportional odds methods give biased results when odds are not proportional. Maximum likelihood models should, therefore, be preferred to log-rank test based methods for the meta-analysis of time-to-event data and any such meta-analysis should investigate whether proportional hazards or proportional odds assumptions are valid. Copyright © 2011 John Wiley &amp; Sons, Ltd.</p