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51.
ABSTRACT

In clinical trials, selection of appropriate study endpoints is critical for an accurate and reliable evaluation of safety and effectiveness of a test treatment under investigation. In practice, however, there are usually multiple endpoints available for measurement of disease status and/or therapeutic effect of the test treatment under study. For example, in cancer clinical trials, overall survival, response rate, and/or time to disease progression are usually considered as primary clinical endpoints for evaluation of safety and effectiveness of the test treatment under investigation. Once the study endpoints have been selected, sample size required for achieving a desired power is then determined. It, however, should be noted that different study endpoints may result in different sample sizes. In practice, it is usually not clear which study endpoint can best inform the disease status and measure the treatment effect. Moreover, different study endpoints may not translate one another although they may be highly correlated one another. In this article, we intend to develop an innovative endpoint namely therapeutic index based on a utility function to combine and utilize information collected from all study endpoints. Statistical properties and performances of the proposed therapeutic index are evaluated theoretically. A numerical example concerning a cancer clinical trial is given to illustrate the use of the proposed therapeutic index.  相似文献   
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Objective

Comparative survival between neoadjuvant chemotherapy and adjuvant chemotherapy for patients with cT2-4N0-1M0 non–small cell lung cancer has not been extensively studied.

Methods

Patients with cT2-4N0-1M0 non–small cell lung cancer who received platinum-based chemotherapy were retrospectively identified. Exclusion criteria included stage IV disease, induction radiotherapy, and targeted therapy. The primary end point was disease-free survival. Secondary end points were overall survival, chemotherapy tolerance, and ability of Response Evaluation Criteria In Solid Tumors response to predict survival. Survival was estimated using the Kaplan–Meier method, compared using the log-rank test and Cox proportional hazards models, and stratified using matched pairs after propensity score matching.

Results

In total, 330 patients met the inclusion criteria (n = 92/group after propensity-score matching; median follow-up, 42 months). Five-year disease-free survival was 49% (95% confidence interval, 39-61) for neoadjuvant chemotherapy versus 48% (95% confidence interval, 38-61) for adjuvant chemotherapy (P = .70). On multivariable analysis, disease-free survival was not associated with neoadjuvant chemotherapy or adjuvant chemotherapy (hazard ratio, 1.1; 95% confidence interval, 0.64-1.90; P = .737), nor was overall survival (hazard ratio, 1.21; 95% confidence interval, 0.63-2.30; P = .572). The neoadjuvant chemotherapy group was more likely to receive full doses and cycles of chemotherapy (P = .014/0.005) and had fewer grade 3 or greater toxicities (P = .001). Response Evaluation Criteria In Solid Tumors response to neoadjuvant chemotherapy was associated with disease-free survival (P = .035); 15% of patients receiving neoadjuvant chemotherapy (14/92) had a major pathologic response.

Conclusions

Timing of chemotherapy, before or after surgery, is not associated with an improvement in overall or disease-free survival among patients with cT2-4N0-1M0 non–small cell lung cancer who undergo complete surgical resection.  相似文献   
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