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.
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.
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.
Dose-response meta-analysis is being increasingly applied in evidence production and clinical decision. The research method, synthesizing certain dose-specific effects across studies with the same target question by a certain types of weighting schedule to get a mean dose-response effect, is to reflect the dose-response relationship between certain exposure and outcome. Currently, the most popular method for dose-response meta-analysis is based on the classical "two-stage approach", with the advantage that it allows fixed- or random-effect model, according to the amount of heterogeneity in the model. There are two types of random-effect model available for dose-response meta-analysis, that is, the generally model and the coefficient-correlation-adjusted model. In this article, we briefly introduce two models and illustrate how they are applied in Stata software, which is expected to provide theoretical foundation for evidence-based practice.
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.
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.
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 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.
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.
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.