In evidence-based practice and decision, dose-response meta-analysis has been concerned by many scholars. It can provide unique dose-response relationship between exposure and disease, with a high grade of evidence among observational-study based meta-analysis. Thus, it is important to clearly understand this type of meta-analysis on software implementations. Currently, there are different software for dose-response meta-analysis with various characteristics. In this paper, we will focus on how to conduct dose-response meta-analysis by Stata, R and SAS software, which including a brief introduction, the process of calculation, the graph drawing, the generalization, and some examples of the processes.
ObjectiveTo develop reporting guideline for dose-response meta-analysis (DMA), so as to help Chinese authors to understand DMA better and to promote the reporting quality of DMA conducted by them. MethodPubMed, EMbase, The Cochrane Library, CNKI, and WanFang Data were searched from Jan 1st 2011 to Dec 30th 2015 to collect DMA papers published by Chinese authors. The number of these publications by years, whether and what kind of reporting guideline was used, and whether the DMA method claimed in these publications was correct were analysed. Then we drafted a checklist of items for reporting DMA, and organized a discussion meeting with experts from the fields of DMA, evidence-based medicine, clinical epidemiology, and clinicians to collect suggestions for revising the draft reporting guideline for DMA. ResultsOnly 33.73% of the publications clarified it is a DMA on the title and 48.02% of them reported risk of bias. Almost 38.49% of the publications didn't use any reporting guidelines. Fourteen of them claimed an incorrect use of methodology. We primarily took account for 47 potential items related to DMA based on our literature analysis results and existing reporting guidelines for other types of meta-analyses. After the discussion meeting with 6 experts, we revised the items, and finally the G-Dose checklist with 43 items for reporting DMA was developed. ConclusionThere is a lack of attention on reporting guidelines in Chinese authors and evidence suggests these authors may be at risk of incomplete understanding on reporting guidelines. It is strongly recommended to use reporting guidelines for DMA and other types of meta-analyses in Chinese authors.
Dose-response meta-analysis, as a subset of meta-analysis, plays an important role in dealing with the relationship between exposure level and risk of diseases. Traditional models limited in linear regression between the independent variables and the dependent variable. With the development of methodology and functional model, Nonlinear regression method was applied to dose-response meta-analysis, such as restricted cubic spline regression, quadratic B-spline regression. However, in these methods, the term and order of the independent variables have been assigned that may not suit for any trend distribution and it may lead to over fitting. Flexible fraction polynomial regression is a good method to solve this problem, which modelling a flexible fraction polynomial and choosing the best fitting model by using the likelihood-ratio test for a more accurate evaluation. In this article, we will discuss how to conduct a dose-response meta-analysis by flexible fraction polynomial.
According to the heterogeneity between dose-response data across different studies and the potential nonlinear trend within the dose-response relationship, there are several models for trend estimation from summarized dose-response data, with applications to meta-analysis. However, up to now, there is no guideline of conducting a metaanalysis of dose-response data. After summarizing the previous papers, this paper focuses on how to select the right model for conducting a meta-analysis of dose-response data based on the heterogeneity across different studies, the goodness of fit, and the P value of overall association between exposure and event. Then a preliminary statistical process of conducting a meta-analysis of dose-response data is proposed.
ObjectiveTo systematically evaluate the dose-response relationship between coffee consumption and liver cancer risk. MethodsThe PubMed, Web of Science, Cochrane Library, EMbase, CNKI, VIP, WanFang Data, and CBM databases were searched from inception to December 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using Stata 17.0 software. ResultsFifteen studies (11 cohort studies and 4 case-control studies) involving 557 259 participants were included. The results of meta-analysis showed that coffee consumption was significantly negatively associated with the risk of liver cancer (RR=0.39, 95%CI 0.27 to 0.57, P<0.01). The dose-response meta-analysis showed a non-linear dose-response relationship between coffee consumption and the risk of liver cancer (P<0.01). Compared with people who did not drink coffee, people who drank 1 cup of coffee a day had a 25% lower risk of liver cancer (RR=0.75, 95%CI 0.67 to 0.83), and people who drank 2 cups of coffee a day had a 38% lower risk of liver cancer (RR=0.62, 95%CI 0.56 to 0.70). The risk of liver cancer decreased by 45% (RR=0.55, 95%CI 0.48 to 0.62) for 3 cups of coffee and by 51% (RR=0.49, 95%CI 0.43 to 0.56) for 4 cups of coffee. ConclusionCurrent evidence suggests that there is a nonlinear dose-response relationship between coffee consumption and the risk of liver cancer. These results indicate that habitual coffee consumption is a protective factor for liver cancer. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
Does-response meta-analysis, which has being developed for more than 30 years, is a type of regression function and can be both linear and non-linear model. It plays an important role in investigating the relationship between dependent and independent variable. With its special advantages, dose-response meta-analysis has been widely used in evidence-based practice and decision. Currently there are several models can be used to perform dose-response metaanalysis with various advantages and disadvantages. It is vital to choose best model to perform dose-response metaanalysis in evidence-based practice. In this paper, we briefly introduce and summarize the methodology of dose-response meta-analysis.
ObjectiveTo systematically review the dose-response relationship between body mass index (BMI) and the risk of stroke. MethodsPubMed, EMbase, Web of Science, The Cochrane Library, CBM, VIP, WanFang Data and CNKI databases were electronically searched to collect studies on BMI and the risk of stroke from inception to December 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by using Stata 16.0 software, and the dose-response relationship between BMI and risk of stroke was analyzed by using restricted cubic spline function and generalized least squares estimation (GLST). ResultsA total of 19 studies involving 3 689 589 patients were included. The results of meta-analysis showed that compared with normal BMI, overweight (RR=1.28, 95%CI 1.19 to 1.39, P<0.01) and obesity (RR=1.41, 95%CI 1.15 to 1.72, P<0.01) had a higher risk of stroke. Dose-response meta-analysis suggested that there was no significant non-linear relationship between BMI and stroke risk (nonlinear test P=0.318), and linear trend showed that the risk of stroke increased by 4% for each unit increase in BMI (RR=1.04, 95%CI 1.03 to 1.05, P<0.01). ConclusionCurrent evidence suggests that increased BMI is associated with an increased risk of stroke. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
Dose-response meta-analysis serves an important role in investigating the dose-response relationship between independent variables (e.g. dosage) and disease outcomes. Traditional dose-response meta-analysis model is based on one independent variable to consider its own dose-specific effect on the outcome. However, for drug clinical trials, it generally involves two-dimensions of the treatment, such as dosage and course of treatment. These two-dimensions tend to be associated with each other. When neglecting their correlations, the results may be at risk of bias. Moreover, taking account of the "combined effect” of dosage and time on outcome has more clinical value. Therefore, in this article, based on traditional dose-response meta-analysis model, we propose a three-dimension model for dose-response meta-analysis which considers both the effect of dosage and time, to provide a solution for the above-mentioned problems in a traditional model.
ObjectiveTo systematically review the dose-response relationship between body mass index (BMI) and all-cause mortality in the elderly with frailty.MethodsPubMed, EMbase, Web of Science, CNKI, VIP, WanFang Data, and CBM databases were electronically searched to collect cohort studies on the association of BMI and mortality in frail adults from inception to November 2019. Two reviewers independently screened literature, extracted data and assessed risk bias of included studies; Stata 15.0 software was then used to analyze the dose-response analysis of BMI and mortality by restricted cubic spline function and generalized least squares method.ResultsA total of 4 cohort studies involving 12 861 frail adults were included. Meta-analysis results showed that compared with normal BMI, the frail elderly who were overweight (HR=0.80, 95%CI 0.74 to 0.88, P<0.001) and obese (HR=0.89, 95%CI 0.79 to 1.00, P=0.047) had lower all-cause mortality. The results of dose-response meta-analysis showed that there was a non-linear relationship between BMI and all-cause mortality in the elderly with frailty (P value for nonlinearity was 0.035), for which the elderly with frailty had a BMI nadir of 27.5-31.9 kg/m2. For linear trends, and when BMI was less than 27.5 kg/m2, the risk of all-cause death was reduced by 4% for every 1 kg/m2 increase in BMI (RR=0.96, 95%CI 0.90 to 1.03, P=0.320), when BMI was greater than 27.5 kg/m2, the risk of all-cause death increased by 4% for every 1 kg/m2 increase in BMI (RR=1.04, 95%CI 1.03 to 1.05, P<0.001).ConclusionsThere is a paradox of obesity and a significant nonlinear relationship between BMI and all-cause mortality in the frailty elderly, with the lowest all-cause mortality in the frailty elderly at BMI 27.5-31.9 kg/m2. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusions.
Dose-response meta-analysis, an important tool in investigating the relationship between a certain exposure and risk of disease, has been increasingly applied. Traditionally, the dose-response meta-analysis was only modelled as linearity. However, since the proposal of more powerful function models, which contains both linear, quadratic, cubic or more higher order term within the regression model, the non-linearity model of dose-response relationship is also available. The packages suit for R are available now. In this article, we introduced how to conduct a dose-response meta-analysis using dosresmeta and mvmeta packages in R.