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.
Dose-response relationship model has been widely used in epidemiology studies, as well as in evidence-based medicine area. In dose-response meta-analysis, the results are highly depended on the raw data. However, many primary studies did not provide sufficient data and led the difficulties in data analysis. The efficiency and response rate of collecting the raw data from original authors were always low, thus, evaluating and transforming the missing data is very important. In this paper, we summarized several types of missing data, and introduced how to estimate the missing data and transform the effect measure using the existed information.
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.
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.
When investing the relationship between independent and dependent variables in dose-response meta-analysis, the common method is to fit a regression function. A well-established model should take both linear and non-linear relationship into consideration. Traditional linear dose-response meta-analysis model showed poor applicability since it was based on simple linear function. We introduced a piecewise linear function into dose-response meta-analysis model which overcame this problem. In this paper, we will give a detailed discussion on traditional linear and piecewise linear regression model in dose-response meta-analysis.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.
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.
Restricted cubic spline function is an ideal model in trend approximation, which is widely used in doseresponse meta-analysis. The spline function, based on parameter technique, is a smoothly joined piecewise polynomial of each knot, with a cubic polynomial in each sub-interval of the slope which fits well in the non-linear trend by changing the number and (or) the sites of the knots. We have introduced the methodology of linear and non-linear slope model in dose-response meta-analysis in the previous article, and in this one, we will give a more detailed discussion on restricted cubic spline function mainly in the following aspects: model building, parameters pooling and knots selecting.
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 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.