The rapid advancement of causal inference is driving a paradigm shift across various disciplines. "Target trial emulation" has emerged as an exceptionally promising framework for observational real-world studies, attracting substantial attention from medical scholars and regulatory agencies worldwide. This article aims to provide an introduction to CERBOT, an online tool that assists in implementing target trial emulation studies, while highlighting the advancements in this domain. Additionally, the article provides an illustrative example to elucidate the operational process of CERBOT. The objectives are to support domestic researchers in conducting target trial emulation studies and enhance the quality of real-world studies in the domestic medical field, as well as improve the medical service level in clinical practice.
Randomized controlled trials are considered as the gold standard for determining the causality, and are usually used to evaluate the efficacy and safety of medical interventions. However, in some cases it is not feasible to conduct a randomized controlled trial. In recent years, a framework called “target trial emulation study” has been formally established to guide the design and analysis of observational studies based on real-world data. This framework provides an effective method for causal inference based on observational studies. In order to facilitate domestic scholars to understand and apply the framework to solve related clinical problems, this article introduces it from the basic concept, framework structure and implementation steps, development status, and prospects.
ObjectiveTo investigate whether there is a causal relationship between the intake of milk or coffee and the risk of non-alcoholic fatty liver disease (NAFLD). MethodsUsing a two-sample Mendelian randomization approach, single nucleotide polymorphisms (SNPs) associated with milk or coffee intake were used as instrumental variables, and genome-wide association study data on NAFLD were used as the outcome event. Inverse-variance weighted (IVW) and MR-Egger methods were employed to investigate the causal effect of milk or coffee intake on the risk of NAFLD. ResultsBoth analyses indicated no causal association between milk or coffee intake and the risk of NAFLD (P>0.05). Sensitivity analysis indicated the robustness of the main findings, with no outliers, heterogeneity, horizontal pleiotropy, or significant influence of individual SNPs. ConclusionThis study does not support a causal relationship between the intake of milk or coffee and the risk of NAFLD.
In recent years, the number of randomized controlled trials using cohorts and routinely collected data (e.g., electronic health records, administrative databases, and health registries) has increased. Such trials can ease the challenges of conducting research and save cost and time. Accordingly, to standardize such trials and increase the transparency and completeness of research reports, an international panel of experts developed the CONSORT-ROUTINE (2021) reporting guideline. The reporting guideline was published in 2021 in the BMJ. To help understand and formally apply the reporting guideline and improve the overall quality of this type of study, the present paper introduced and interpreted the development process and reporting checklist of the CONSORT-ROUTINE.
Statistical graph is an indispensable part of scientific papers. It is helpful to promote the communication, dissemination, and application of academic achievements by presenting research results intuitively and accurately through standardized and beautiful visual graphs. The safety of a medical intervention is the basic premise of its clinical application, and randomized controlled trial (RCT) as an important design to determine the efficacy and safety of medical interventions, it is extremely important to accurately present the information on the safety outcomes of interventions found therein. However, the research found that the reports of RCTs didn’t adequately use visual graphs to present harms data. In order to promote clinical researchers to better use visual graphs to present harms data, international scholars recently published a consensus study in BMJ, which identified and recommended 10 statistical graphs for presenting harms data in RCTs. In order to facilitate domestic scholars to understand and apply the consensus, this article interprets the consensus and recommendations, and it is expected to provide help for improving the quality of harms visualization in domestic papers of RCTs.
In response to the need for health economics modelers to apply more appropriate complex systems models to address complex challenges in public health, an international team of more than 40 experts in the field of complex systems models and economic evaluation has developed and recently published a guideline on the application of complex systems models to the economic evaluation of public health interventions. This paper introduces the development process and main content of the guidelines, which can provide references to facilitate the application of the guidelines by domestic researchers, aiming to ultimately improve the overall quality of public health research and services and improve the health of the population in China.
Objective To analyze the disease burden of liver cancer caused by nonalcoholic steatohepatitis (NASH) among the Chinese population by utilizing the latest global burden of disease (GBD) 2021 data, and conduct a comparative analysis with the global situation, so as to provide references and lessons for the formulation of public health policies and disease management plans in China. Methods GBD 2021 database publicly released in May 2024 was searched and relevant disease burden data of liver cancer caused by NASH in China and globally from 1990 to 2021 was sorted out. Joinpoint regression model was employed to calculate the average annual percentage change (AAPC) of the age-standardized rates of various burden indicators in order to evaluate the changing characteristics of disease epidemiology over time. Meanwhile, Bayesian method was used to predict the number of incidences and deaths of liver cancer caused by NASH in China and globally from 2022 to 2045. Results Compared with 1990, in 2021, the number of incidences, prevalence, deaths, disability-adjusted life years, age-standardized incidence rate and age-standardized prevalence rate of liver cancer caused by NASH in the overall, male and female populations in China and globally all increased. Moreover, in 1990 and 2021, all the burden indicators of the Chinese male population were higher than those of the female population in the corresponding years. The overall trend analysis showed that during the 32 years from 1990 to 2021, the age-standardized incidence rate [AAPC=0.44%, 95%CI (0.35%, 0.53%), P<0.001] and the age-standardized prevalence rate [AAPC=0.92%, 95%CI (0.73%, 1.11%), P<0.001] of liver cancer caused by NASH in the overall population in China both showed a significant upward trend. In addition, in 1990 and 2021, the age-standardized rates of all the burden indicators of the overall population in China were higher than the global levels in the corresponding years. The prediction results of the Bayesian model showed that from 2022 to 2045, the number of incidences and deaths of liver cancer caused by NASH in the overall, male and female populations in China and globally will generally show an upward trend. Conclusions The disease burden of liver cancer caused by NASH in China and globally is generally on the rise. In order to curb the increasing disease burden, it is necessary to formulate relevant public health policies and disease management plans in a timely manner.