This paper revisits the 1986 article "Meta-Analysis in Clinical Trials" by DerSimonian and Laird, which introduced a random-effect model for summarizing treatment efficacy from multiple clinical trials. The method, now known as the "DerSimonian and Laird method," has been widely adopted due to its simplicity and ease of implementation, with over 12,000 citations to date. The authors review the background leading to the original article, describe the random-effects approach, explore its use in various settings, and discuss its popularity in medical and clinical research. They recommend a refinement using a robust variance estimator for testing overall effects and propose repurposing the method for Big Data meta-analysis and Genome Wide Association Studies (GWAS) to study genetic variants in complex diseases. The paper also highlights the method's advantages, such as requiring simple data summaries and being intuitively appealing, while acknowledging its limitations, particularly in hypothesis testing with small sample sizes or high heterogeneity.This paper revisits the 1986 article "Meta-Analysis in Clinical Trials" by DerSimonian and Laird, which introduced a random-effect model for summarizing treatment efficacy from multiple clinical trials. The method, now known as the "DerSimonian and Laird method," has been widely adopted due to its simplicity and ease of implementation, with over 12,000 citations to date. The authors review the background leading to the original article, describe the random-effects approach, explore its use in various settings, and discuss its popularity in medical and clinical research. They recommend a refinement using a robust variance estimator for testing overall effects and propose repurposing the method for Big Data meta-analysis and Genome Wide Association Studies (GWAS) to study genetic variants in complex diseases. The paper also highlights the method's advantages, such as requiring simple data summaries and being intuitively appealing, while acknowledging its limitations, particularly in hypothesis testing with small sample sizes or high heterogeneity.