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At present, the above small sample statistical inference methods have not been applied to the study of tumor chemosensitive assay in vitro, so it will be very meaningful to use the data of HB to carry out relevant research. Moreover, Lingling Yuan et al. compared the differences between Bayesian interim analysis and classical interim analysis based on group sequential design, and quantitatively assessed the reliability of Bayes results[14]. The research results showed that Type I errors can be well controlled by Bayes interim analysis method, and the influence of several common prior distributions was evaluated, providing professional reference for the application of this method in the field of clinical trials[14]. In addition to group sequential design, adaptive dose finding designs is also a research direction applicable to small sample dose-response analysis[5]. For example, in 2007, Bornkamp et al. showed that inadequate dose selection proposed in phase III confirmatory trials has been one of the major factors in the declining success rate of drug development programs[12]. To address this problem, they suggested improvements in the selection of drug dosing ranges and studies have shown that adaptive dose-ranging trails are most effective.
At present, scholars have carried out a large number of studies in the field of small sample statistical inference, ranging from classical statistical theory (OLS regression, Bootstrap method, Bayes method, etc.) to relatively complex experimental design (group sequential design, adaptive design, multiple comparison and modeling techniques, etc.). The purpose of this study is to use these statistical analysis methods to conduct a reasonable and reliable dose-response study on the data obtained from the tumor chemosensitive assay in vitro of HB and come up with some new ideas. The current goal is to make improvements and innovations in the small sample statistical inference methods, and the future goal is to make contributions to the guidance of clinical chemotherapy drugs.