Lung cancer mortality in the Mayo Lung Project: impact of extended follow-up

BACKGROUND: The Mayo Lung Project (MLP) was a randomized, controlled clinical trial of lung cancer screening that was conducted in 9211 male smokers between 1971 and 1983. The intervention arm was offered chest x-ray and sputum cytology every 4 months for 6 years; the usual-care arm was advised at trial entry to receive the same tests annually. No lung cancer mortality benefit was evident at the end of the study. We have extended follow-up through 1996. METHODS: A National Death Index-PLUS search was used to assign vital status and date and cause of death for 6523 participants with unknown information. The median survival for lung cancer patients diagnosed before July 1, 1983, was calculated by use of Kaplan-Meier estimates. Survival curves were compared with the log-rank test. RESULTS: The median follow-up time was 20.5 years. Lung cancer mortality was 4.4 (95% confidence interval [CI] = 3.9-4.9) deaths per 1000 person-years in the intervention arm and 3.9 (95% CI = 3.5-4.4) in the usual-care arm (two-sided P: for difference =.09). For participants diagnosed with lung cancer before July 1, 1983, survival was better in the intervention arm (two-sided P: =.0039). The median survival for patients with resected early-stage disease was 16.0 years in the intervention arm versus 5.0 years in the usual-care arm. CONCLUSIONS: Extended follow-up of MLP participants did not reveal a lung cancer mortality reduction for the intervention arm. Similar mortality but better survival for individuals in the intervention arm indicates that some lesions with limited clinical relevance may have been identified in the intervention arm.     

Development tracks for cancer prevention markers

We provide a general framework for describing various roles for biomarkers in cancer prevention research (early detection, surrogate endpoint, and cohort identification for primary prevention) and the phases in their evaluation.     

Improving the information content of categorical clinical trial endpoints

Because the severity of most diseases can be measured nonuniquely, different medical interventions with different mechanisms of action may be evaluated differently, even in the same patient population. Complicating this further is the fact that even for a given medical intervention, it may not be clear which endpoint, if any, will be most likely to show an intervention effect. For these and other reasons, clinical trials typically involve the evaluation of multiple safety and efficacy endpoints. As information accrues about diseases and patient populations, some endpoints may cease to be useful, but the trend would still likely be toward increasing numbers of potential endpoints. This trend would provide sponsors with increasing numbers of choices for the primary efficacy endpoint. If the endpoint selected as primary is not the optimal one for demonstrating the superiority of the experimental medical intervention, then a safe and effective medical intervention may be mistakenly found to be otherwise. On the other hand, the sponsor may find the endpoint that makes its case and not study other endpoints that would have shown the experimental intervention to be inferior, in some way, to the control intervention. As such, the reliance of medical decisions on narrow primary endpoints can lead to inflation of both the type I and type II error rates. To address these concerns, we propose that all endpoints, especially the primary endpoint, be as informative as possible. This could be accomplished by combining some endpoints into composite endpoints. To avoid losing information in this transformation, we define the concept of information-preserving composite endpoints and provide information concerning when this type of composite endpoint would be most useful. Specifically, we define the concept of joint fusibility of a set of endpoints and note that this property confers upon the derived information-preserving composite endpoint the greatest amenability to statistical analysis. We also point out that using composite endpoints allows sponsors the most discretion in selecting their primary between-group statistical analysis. We illustrate these ideas with examples from a variety of therapeutic areas.