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Objective To assess the effects and safety of berberine combined with triple therapy on Helicobacter pylori (H. pylori) eradication in adults. Methods PubMed, MEDLINE, EMBASE, Cochrane Library, and Chinese databases including China National Knowledge Infrastructure (CNKI), Wanfang data, Chinese Technology Journal Full-text Database (VIP), and China biomedical literature database (CBM) were searched to obtain the eligible studies published up to October 10, 2017. The primary outcome was eradication rate of H. pylori. The secondary outcome was incidence of adverse effects. Data analysis was conducted by RevMan5.2 and Stata V.9.0 software. Trial sequential analysis (TSA) was performed to assess the risk of random error and the validity of conclusion with TSA program version 0.9 beta. Results The meta-analysis results indicated berberine combined with triple therapy could improve the eradication rates of H. pylori (urea breath test subgroup: RR=1.18, 95%CI=(1.12,1.24), P < 0.00001, biopsy subgroup: RR=1.23, 95%CI=(1.13,1.34), P < 0.00001) and reduce the total occurrence of adverse effects (OR=0.59, 95%CI(0.46, 0.75), P < 0.0001) when compared with only using triple therapy. Besides, the incidence of nausea (OR=0.59, 95%CI(0.41, 0.86), P < 0.05) and diarrhea (OR=0.41, 95%CI(0.24, 0.71) was remarkably lower in experimental group while that of abdominal distention (OR=0.64, 95%CI(0.40,1.04), P > 0.05) and vomiting (OR=0.65, 95%CI(0.37, 1.15), P > 0.05) had no significant change. TSA of H. pylori eradication rates and adverse effects incidence illustrated that the cumulative value of Z-curve went across the conventional boundary value, trial sequential monitoring boundary for benefit, and required information size, suggesting the results were stable. Conclusion Evidence from meta-analysis suggested that berberine combined with triple therapy can be an option for increasing H. pylori eradication rates and reducing overall therapy-related adverse effects incidence, particularly nausea and diarrhea, whereas more randomized controlled trials designed according to CONSORT statement are demanded to support the efficacy in further studies.

Background Predicting allograft survival is vital for efficient transplant success. With dynamic changes in patient conditions, clinical indicators may change longitudinally, and doctors’ judgments may be highly variable. It is necessary to establish a dynamic model to precisely predict the individual risk/survival of new allografts. Methods The follow-up data of 407 patients were obtained from a renal allograft failure study. We introduced a landmarking-based dynamic Cox model that incorporated baseline values (age at transplantation, sex, weight) and longitudinal changes (glomerular filtration rate, proteinuria, hematocrit). Model performance was evaluated using Harrell’s C-index and the Brier score. Results Six predictors were included in our analysis. The Kaplan–Meier estimates of survival at baseline showed an overall 5-year survival rate of 87.2%. The dynamic Cox model showed the individual survival prediction with more accuracy at different time points (for the 5-year survival prediction, the C-index = 0.789 and Brier score = 0.065 for the average of all time points) than the static Cox model at baseline (C-index = 0.558, Brier score = 0.095). Longitudinal covariate prognostic analysis (with time-varying effects) was performed. Conclusions The dynamic Cox model can utilize clinical follow-up data, including longitudinal patient information. Dynamic prediction and prognostic analysis can be used to provide evidence and a reference to better guide clinical decision-making for applying early treatment to patients at high risk.

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions. K E Y W O R D Sinverse probability of censoring weighting, restricted mean survival time, survival analysis, time-dependent covariates 1