To conduct subgroup analysis during meta-analysis using R programming language, you can follow these steps. First, you need to identify the variables that you want to use for subgroup analysis, such as years of publication, study site, type of specimens, or laboratory method used. Then, you can use the `metafor` package in R, which provides functions for conducting meta-analyses. Use the `rma()` function to fit the meta-analysis model, specifying the outcome variable, the study-level data, and any covariates you want to include. Next, you can use the `addterm()` function to add the subgroup variables as moderators in the model. This allows you to examine whether the effect size varies across different subgroups. You can use the `coef()` function to extract the estimated coefficients for the subgroup variables, and the `confint()` function to obtain confidence intervals. Additionally, you can use the `forest()` function to create a forest plot that displays the effect sizes for each subgroup. Finally, you can interpret the results and draw conclusions about the heterogeneity and subgroup effects. Remember to consider the assumptions and limitations of meta-analysis and subgroup analysis in your interpretation.

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