How can I adjust for confounding variables in meta analysis?

The meta-regression is sufficient. To get the predicted mean from the meta-regression you don't need to run the residual analysis. Just use the prediction equation from the regression or (perhaps more neatly) mean-centre the covariates age and weight before including them in the model. The intercept for the meta-regression pin the mean-centred model is your predicted adjusted mean effect (adjusted for the covariates). This assumes you want to predict for someone with average weight and age, but you could predict for other values or ... better still graph the effect as a function of covariates.

Puthachad Namwaing

Your approach to adjusting for the two quantitative variables (Age and Weight) in your meta-analysis seems reasonable, and it aligns with a common strategy for addressing heterogeneity by using meta-regression. Let's break down your steps and offer some additional considerations:

Meta-Analysis of Mean Changes:Using the metamean() function from the "meta" package to conduct a meta-analysis of mean changes in Blood Pressure across different subgroups is a valid starting point.

Meta-Regression:Employing the metareg() function to model the relationship between Blood Pressure and the covariates Age and Weight is a standard approach for meta-regression. This allows you to investigate whether these covariates explain some of the observed heterogeneity across studies.

Use of Residuals:Utilizing the residuals from the meta-regression model to perform a second meta-analysis is a common practice. By doing so, you are essentially adjusting for the effects of Age and Weight, which might help reduce the observed heterogeneity.

Assessment of Heterogeneity:Your observation that this approach significantly reduced heterogeneity in your results is promising. It suggests that the inclusion of Age and Weight as covariates in the meta-regression model helped explain some of the variability in the study outcomes.

Validity Check:It's a good practice to check the assumptions of meta-regression, including linearity and homoscedasticity of residuals. Additionally, examine diagnostic plots to ensure that the meta-regression model adequately captures the relationship between the covariates and the effect sizes.

Consideration of Nonlinear Relationships:If the relationship between Age/Weight and the effect sizes is not linear, you might explore incorporating nonlinear terms or transformations (e.g., quadratic terms, log transformations) into your meta-regression model.

Sensitivity Analyses:Perform sensitivity analyses to assess the robustness of your findings. You can explore different model specifications, compare results with and without adjustment, and check the impact of outliers.

Interpretation:When interpreting your results, be cautious about making causal claims. While meta-regression can identify associations, establishing causation requires additional evidence and consideration of potential confounding factors.

Consideration of Other Covariates:Depending on the nature of your data and research question, you might want to explore the inclusion of other covariates that could contribute to heterogeneity. Consider variables such as study design, intervention type, or other relevant characteristics.

Consultation with Statisticians or Methodologists:If possible, seek feedback from statisticians or methodologists with expertise in meta-analysis. They can provide valuable insights into the appropriateness of your statistical methods and offer guidance on potential refinements.

In summary, your approach appears technically sound, and adjusting for covariates through meta-regression is a common strategy in meta-analysis. However, always ensure that you have thoroughly checked the assumptions and limitations of your modeling approach and consider seeking expert advice when needed.

High aspect ratio hexahedral mesh optimization solver?

How to solve the fatal error "the entire source was rejected" in MCNP?

How does a freeze dryer destruct your solid sample?

How to model Type 1 collagen structure for finite element analysis ?

About loading of Doxorobicin on MOF (drug delivery)?

Hey guys, I'm looking for an efficient protocol to transfect patient-derived tumor cells with a high rate of transient transfection?

How can I model a vacuum region between a concentric tube in Ansys fluent?

How can I represent a 2-parameters function in series type?

Can you recommend a protocol for immunohistochemistry of mouse brain sections using anti-MAP2?

What type of plants is better to work in gene regulation control project ?

How to compare two groups with only two measurements?

Enhanced Yellow Fluorescent Protein (Aequorea victoria) DNA sequence?

What kind of fluid could I use for a pitfall trap that also does not invalidate molecular testing?

What's the best way to measure growth rates in House sparrow chicks from day 2 to day 10?

Do we need to write protocol for a second study of a systematic review that has been registered and published?

How to increase a model accuracy ?

Do I need to do linear regression before running mediation analysis using PROCESS MACRO by Hayes ?

Sample dilution for quantitative analysis using ELISA?

Is There Any Feasible Method To Test Fluorescent Compounds Other Than Fluorescence Spectrometers ??

How to calculate average trend in maximum annual NDVI from MODIS?