AIM: To introduce a novel and flexible model-assisted design for Phase I clinical trials: Bayesian optimal interval (BOIN) design, including the process of implementation, practical implementation, and evaluation of its performance. METHODS: BOIN design decides dose escalation/de-escalation by comparing the observed toxicity rate at the current dose with an escalation boundary and a de-escalation boundary that are optimized to minimize the probability of making incorrect decision of dose assignment. The application of the BOIN design is illustrated using a trial example. RESULTS: BOIN combines the advantages of the algorithm-based methods and model-based methods. It enjoys desirable statistical properties -it is optimal, safe, robust and easy to implement. Simulation study shows that the BOIN substantially outperforms the existing designs with higher accuracy to identify the maximum tolerated dose (MTD). CONCLUSION: BOIN design possesses the similar statistical performance to the much more complicated model-based designs. It is simple to implement, and easy to calibrate to meet the safety requirement mandated by regulatory agents. The BOIN design has been widely used in different types of cancers. It is a novel design that holds great potential to substantially improve phase I trials in China.

Objective To summarize the complications and their incidences following Anterior Subcutaneous Internal Pelvic Fixation(ASIPF).Methods A comprehensive search was conducted of PubMed Library,Cochrane Library,Web of Science,SinoMed,Wanfang Data and China National Knowledge Internet for all articles addressing the postoperative complications of ASIPF published in English and Chinese from January 2009 to November 2018.A proportion Meta-analysis across the studies was performed for the complications after ASIPF (lateral femoral cutaneous nerve irritation,femoral nerve palsy,heterotopic ossification,infection and implant failure) using R software.Results This meta-analysis included 29 clinical studies involving a total of 825 patients.The complications following ASIPF were lateral femoral cutaneous nerve irritation,femoral nerve palsy,heterotopic ossification,infection and implant failure;their incidences were respectively 12％ (95％ CI:from 7％ to 19％),3％ (95％ CI:from 2％ to 4％),30％ (95％ CI:from 22％ to 39％),4％ (95％ CI:from 3％ to 6％) and 4％ (95％ CI:from 3％ to 6％).Conclusions Lateral femoral cutaneous nerve irritation and heterotopic ossification are common complications following minimally invasive internal fixation for anterior pelvic ring injury.High-quality clinical research is needed into the factors leading to the complications and into their preventive countermeasures.

The epidemic spectrum of acetabular fractures is changing with the changing population structure in China which has resulted in an increased proportion of the elderly patients. As acetabular fractures are intra-articular in nature, their management should follow the principles for intra-articular ones. However, it is still a great challenge for orthopedists to choose an appropriate treatment to deal with the fracture because of the pathophysiological changes in the elderly patients and the particular characteristics of acetabular fractures. Technical advances may offer a diversity of treatment options for geriatric acetabular fractures and demands for individualized management are increasing. In order to deepen the knowledge of geriatric acetabular fractures for orthopedists, this review expounds on the current achievements in the management and research concerning the fractures.

Objective To explore the statistical performance and applicable conditions of Bayesian additive regression tree(BART)for estimating average treatment effect in observational studies.Methods The difference of estimates between BART and multivariate regression,propensity score matching,and inverse probability weighting through simulations and actual epidemiological data was compared.Results The results of these simulations showed that under the linear assumption,the performance of BART was close to that of the commonly used methods;when the relationship among variables in the data was complex and non-linear,BART performed markedly better than the others.When the ignorability assumption was not satisfied and there was unobserved confounding,all methods performed worse,but BART was still significantly better than the others and relatively robust.In the actual epidemiological data,this method was used to estimate the average treatment effect of smoking cessation on weight change.Conclusion In most observational studies,outcomes are influenced by multiple factors,making it difficult for researchers to properly specify relationships between variables.It is difficult to identify all these variables or determine the relationship between them.In terms of model fitting and result accuracy,BART is worth recommending.

Due to the limited number of cases, conducting large-scale clinical trials for rare diseases is challenging. This review introduces several small sample statistical designs tailored for rare diseases, including crossover design, n-of-1 design, randomized placebo-phase design, randomized withdrawal design, group sequential design, and adaptive design. It discusses the advantages, disadvantages, and application scenarios of these designs. Additionally, it explores the benefits of Bayes decision-making in clinical trials for rare diseases. The aim is to provide a reference for designing and implementing small sample clinical trials for rare diseases.

AIM: To investigate the application of two-stage estimation (TSE) on adjustment for treatment switch in oncology trials. METHODS: The theory and implementation of TSE method was described, and was applied to adjust the data from a two-arm randomized controlled trial of anti-tumor drugs. The changes of survival curves and hazard ratio of two groups after adjustment for cross-over were evaluated. In addition, the results of two-stage estimation and rank preserving structural failure time model (RPSFT) were compared. RESULTS: After adjustment for cross-over using TSE methods, the results showed that the median survival time of control group was shorter than the original one, and the hazard ratio was lower than the observed value. Moreover, TSE method showed similar results to rank preserving structural failure time model. CONCLUSION: The TSE method is relatively simple to use, reliable and has a good practice property in cross-over analysis of oncology trials. At the same time, it is necessary to pay attention to its application scopes.