Objective To evaluate the methodological quality of clinical trials on traditional Chinese medicine (TCM) nursing in recent six years.Methods Such databases as CNKI, VIP, WanFang Data and CBM were searched for collecting clinical trials on TCM nursing published from January 2006 to September 2011, and domestic primary nursing journals were also searched from January 2010 and September 2011. Methodological quality of included studies was assessed using quality assessment criteria of the Cochrane systematic review guideline. Results A total of 854 clinical trials were retrieved, including 706 (82.7%) randomized controlled trials (RCTs), 108 (12.6%) quasi-randomized controlled trials and 40 (4.7%) non-randomized controlled trials. In the methodological quality analysis, the comparability of baseline was mentioned in 784 trials (91.8%), a total of 498 (58.3%) reported definite diagnosis criteria. 178 (20.8%) reported exclusive criteria. 831 studies (97.3%) applied relevant statistical methods properly. However, only 55 trials (6.4%) mentioned the method of randomization sequence. 10 studies (1.2%) described the method of randomiztion assignment. Blinding was mentioned in 22 studies (2.6%). 98 trials (11.5%) did prospective follow-up. 93 trials (10.9%) had safety description. 20 trials (2.3%) reported lost and with drawl cases, but only 2 conducted intention-to-treat analysis. It was hard to determine whether there was selective reporting bias or not because all the studies did not have protocols. Only 21 studies (2.5%) mentioned the lack of outcome indicators which could be the evidence for existing of bias. By annual analysis, there were 81 trials which conformed to at least 2 low risk criteria. 10 trials (12.3%) was published in 2009, 26 trials (32.1%) published in 2010, and 27 trials published by September 2011, indicated an uptrend. Conclusions According to the Cochrane Collaboration’s tool for assessing risk of bias, the overall quality of clinical trials on TCM nursing is low with defects in different degrees, but it rises gradually over years.
ObjectiveTo systematically reviewed the progress of Zelen’s design and its modifications in clinical research and clarified its methodological elements, advantages, and limitations. MethodsA systematic literature search was conducted for Zelen’s design from databases. The data were extracted. ResultsOne hundred and twenty-four trials were included. The dominant disease in this design was mental disorders, followed by osteoarthrosis diseases, cancer, cardiovascular diseases, and others. Regarding types of consent, more than half of the trials used a double-consent (71, 57.26%), and 42 used a single-consent. Eleven trials used a modified Zelen’s design nested within an observational study. This design used a two-stage informed consent. Stage 1, patients were invited to participate in a cohort study; Stage 2, patients randomized in the experimental group were informed of the allocation result and asked whether they would like to follow the treatment. Five trials used the McNulty-Zelen design, which could be applied in cluster randomized controlled trials and overcome the potential bias of the Hawthorne effect. Intention-to-treat analysis was the main population used in Zelen’s design. ConclusionZelen’s design has a broad application in the foreground in clinical trials. It could also be used to adapt to research needs by combining with various observational studies. Zelen’s design offers unique advantages in reducing recruitment difficulty, improving patient compliance, and minimizing bias. Although the randomization of patients without their prior consent raises potential ethical concerns, these can be addressed through methods such as nested observational studies or supplementary informed consent. In real-world applications of Zelen’s design, it is necessary to design a reasonable informed consent strategy and data statistical analysis method according to the research context. Attention must be paid to the impact of sample size, group shifting and selection of dataset on the results, to improve the interpretability and accuracy of the results.