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.
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.
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.
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.
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.
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.
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.
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.
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.