Objective To discuss the levels of evidence provided by different study designs.Methods Websites of N Engl J Med, JAMA, Lancet, and BMJ were accessed to identify research articles (systematic review and meta-analysis included) published in 2009. A standardized data collection form was established using Epidata 3. 1 software to extract the "title", "country of lead author", "clinical problem" (such as treatment, diagnosis, etc. ) and "study design" of eligible studies. Descriptive statistics was conducted with SPSS 13.0. Results Over all, 844 studies were included, among which 35.7% were RCT,9. 4% systematic review and Meta-analysis, and 54. 9% other types of studies. Regarding clinical problems,34. 2%, 19. 7%, 13.7%, 6. 0% and 5. 1% of the included researches addressed the issues of treatment,etiology/risk factors, prevention, disease frequency and prognosis, respectively. The study designs that were most frequently adopted to explore these problems were RCT (70.6%), cohort study (44. 6% ), RCT (68. 1% ), cross-sectional study ( 56. 9% ), and cohort study ( 93.0% ), respectively. Conclusions High-level evidence does not come exclusively from RCT and systematic review, as each type of study may have its unique value in health related research. The clinical problem of interest, the previous work that has been done to approach the same issue, as well as other factors should be taken into account when deciding whether the selected study design is appropriate.

Objective: The burden of chronic disease has been continuously increasing in China since the early 1980s. Besides the worsening of risk factors, the change in diagnostic criteria is very likely an important explanation for the increase in the prevalence of hypertension, hyperlipidemia and diabetes mellitus, three commonest, major chronic conditions that can lead to major vascular events and deaths. This study aims to estimate the contribution of changes in diagnostic criteria to the increase in the prevalence of the three conditions in China. Methods: The data from two representative nation-wide surveys in China in 2002 and 2009, with 145 254 and 8 813 adults included respectively, were used to estimate the prevalence rate of the three conditions and the proportion attributable to the change in diagnostic criteria around year 2000. The new and old cutoff values for hypertension, hyperlipidemia, and hyperglycemia were 140/90 and 160/95 mmHg (1 mmHg=0.133 kPa), 5.7 and 6.2 mmol/L, and 7.0 and 7.8 mmol/L, respectively. The prevalence was standardized according to the distribution of age, sex and rural-urban residence of the 2000 national census of the country so as to compare between the old and new diagnostic criteria and project the situation for the entire country. Results: The standardized prevalence of hypertension, hyperlipidemia, and diabetes mellitus for the entire Chinese adult population in 2002 was 8.21%, 1.71% and 1.43% according to the immediate previous diagnostic criteria, and 19.18%, 3.53% and 2.66% according to the new criteria. In 2009, the prevalence was 11.89%, 9.34% and 4.29% according to the old criteria, and 24.78%, 18.36% and 6.55% according to the new criteria. The total cumulative prevalence of the three conditions was increased by 124% in 2002 and 95% in 2009 as a result of change in diagnostic criteria. Put it differently, the change in diagnostic criteria increased the number of the three conditions from 2002 to 2009 by approximately 359 million and could increase the annual drug costs by some 271 billion RMB if all the conditions are treated. The drug costs alone of treating all the three conditions could consume 56% of the total health budget of the Government in 2010. Conclusion About half of the number of the three conditions is a result of the change in diagnostic criteria. These criteria were adopted from western populations, which are designed to meet the population need and suit healthcare resources available in these countries. It is important for China to consider the resources available and needs and values of the population in addition to the benefits, harms and costs of treatment in determining the cutoff values for defining these conditions for drug interventions.

Modern epidemiology is the art and science of investigating quantitatively regularities or general laws regarding applied healthcare issues.The validity of epidemiological studies is primarily determined by the study design and the precision by the sample size.Large randomized controlled trial (RCT) is thus the most rigorous and most precise epidemiological study design.Due to ethical concerns,RCTs can however be used only to evaluate medical interventions.Rigorousness of study design and sample size required for a study are inversely related to the anticipated size of effect to be evaluated:the smaller the effect,the more rigorous the study design and larger the sample size are required.Thus,large RCTs are necessary and called upon when and only when the effectiveness to be proved is relatively small;large effectiveness can be verified with small or medium-sized RCTs or even observational studies.In the stages of scientific research,large RCTs are confirmatory rather than original investigations on new hypotheses,whereas the value of a study is ultimately determined by the importance and novelty of the research question rather than methodology and the P value.Overemphasis on large RCTs has been causing:1) overemphasis on interventions of small or moderate effect;2) overemphasis on confirmatory studies and on size of study and funding and weakening original creative work;3) increasing the risk of research resources,medical activities,and patients' well-being being hijacked by pharmaceutical companies.

Objective:Differences between randomized controlled trial (RCT) results and real world study (RWS) results may not represent a true efficacy-effectiveness gap because efficacy-effectiveness gap estimates may be biased when RWS and RCT differ significantly in study design or when there is bias in RWS result estimation. Secondly, when there is an efficacy- effectiveness gap, it should not treat every patient the same way but assess the real-world factors influencing the intervention's effectiveness and identify the subgroup likely to achieve the desired effect.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:Ten articles were included to discuss how to use the RCT research protocol as a template to develop the corresponding RWS research protocol. Moreover, based on correctly estimating the efficacy-effectiveness gap, evaluate the intervention effect in the patient subgroup to confirm the subgroup that can achieve the expected benefit-risk ratio to bridge the efficacy-effectiveness gap.Conclusion:Using real-world data to simulate key features of randomized controlled clinical trial study design can improve the authenticity and effectiveness of study results and bridge the efficacy-effectiveness gap.

Objective:Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics.Conclusions:Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.

Biomedical Research ; Epidemiologic Methods ; Evidence-Based Medicine ; Treatment Outcome