Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance (ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter, we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.
Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which two or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal. A consensus-based extension to the consolidated standards of reporting trials (CONSORT) 2010 statement for factorial trials was developed based on the enhancing the quality and transparency of health research (EQUATOR) methodological framework. In the study, we introduced and interpreted the extension of the CONSORT 2010 statement for factorial design in which 16 items were modified and one new item was added and presented an example of a factorial trial in mental health to provide guidance on the reporting of factorial randomized trials.
ObjectiveTo systematically review the efficacy of 10 commonly used intrauterine devices (IUD) by network meta-analysis.MethodsPubMed, The Cochrane Library, EMbase, Web of Science, POPLINE, CNKI, WanFang Data, SinoMed, CMCI, ChiCTR databases and websites were electronically searched to collect randomized clinical trials (RCTs) on efficacy of 10 IUDs from inception to December 31st, 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, network meta-analysis was performed by using WinBUGS 1.4.3 software and Stata 13.0 software.ResultsA total of 67 RCTs involving 59 991 IUD users were included. The results of network meta-analysis indicated that after 1 year of use, GyneFix had a lower pregnancy rate than those of TCu220C, TCu380A, YCu300, OCu165 and MLCu375, and its effectiveness ranked second out of ten IUDs (SUCRA=77.1%). YCu200 had a lower pregnancy rate than that of TCu220C, which ranked third (SUCRA=71.5%). After 2 years of use, GyneFix had a lower pregnancy rate than those of TCu220C, TCu380A and OCu165, which had the highest probability to be the most effective intervention (SUCRA=92.1%). YCu200 had a lower pregnancy rate than those of TCu220C, TCu380A, YCu300, OCu165, GCu200, GammaCu and MLCu375, and its effectiveness ranked second (SUCRA=81.2%).ConclusionsCurrent evidence shows that the risk of pregnancy of GyneFix and YCu200 are lower at 1 and 2 years of use, which suggests they possess superior short-term effectiveness. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.