Individual participant data (IPD) meta-analysis is a method of combining data from individual participants across studies to produce more accurate and reliable results. This approach has several advantages over traditional aggregate data meta-analysis, including the ability to account for individual variability, detect subgroup differences, and assess the impact of patient-level factors. IPD meta-analyses can provide more detailed insights into treatment effects, such as how gender or age influences the effectiveness of a treatment. They also allow for the inclusion of unpublished studies, reducing publication bias, and can incorporate missing or poorly reported outcomes. However, IPD meta-analyses are resource-intensive, requiring significant time and effort to collect and process data from multiple studies. This process can be challenging, as it involves contacting study authors, obtaining data, and ensuring data consistency across studies. Additionally, there may be ethical and confidentiality concerns when using patient-level data. Despite these challenges, IPD meta-analyses are increasingly being used due to their potential to provide more accurate and comprehensive results. The use of IPD in meta-analyses has grown significantly over the past decade, with many studies now using this approach to analyze clinical outcomes. The results of IPD meta-analyses can be more reliable than those based on aggregate data, as they allow for more precise estimation of treatment effects and can detect differences that may be missed in aggregate analyses. For example, an IPD meta-analysis of hypertension trials showed that treatment significantly reduced systolic blood pressure, with results that were more accurate than those obtained from aggregate data. IPD meta-analyses should be conducted with clear objectives and protocols, and the results should be reported in a transparent and detailed manner. This includes describing the methods used, the data sources, and the statistical models applied. The results should also be compared with published findings to ensure consistency and accuracy. Overall, IPD meta-analyses offer a powerful tool for evidence-based medicine, but their implementation requires careful planning, resource allocation, and collaboration among researchers.Individual participant data (IPD) meta-analysis is a method of combining data from individual participants across studies to produce more accurate and reliable results. This approach has several advantages over traditional aggregate data meta-analysis, including the ability to account for individual variability, detect subgroup differences, and assess the impact of patient-level factors. IPD meta-analyses can provide more detailed insights into treatment effects, such as how gender or age influences the effectiveness of a treatment. They also allow for the inclusion of unpublished studies, reducing publication bias, and can incorporate missing or poorly reported outcomes. However, IPD meta-analyses are resource-intensive, requiring significant time and effort to collect and process data from multiple studies. This process can be challenging, as it involves contacting study authors, obtaining data, and ensuring data consistency across studies. Additionally, there may be ethical and confidentiality concerns when using patient-level data. Despite these challenges, IPD meta-analyses are increasingly being used due to their potential to provide more accurate and comprehensive results. The use of IPD in meta-analyses has grown significantly over the past decade, with many studies now using this approach to analyze clinical outcomes. The results of IPD meta-analyses can be more reliable than those based on aggregate data, as they allow for more precise estimation of treatment effects and can detect differences that may be missed in aggregate analyses. For example, an IPD meta-analysis of hypertension trials showed that treatment significantly reduced systolic blood pressure, with results that were more accurate than those obtained from aggregate data. IPD meta-analyses should be conducted with clear objectives and protocols, and the results should be reported in a transparent and detailed manner. This includes describing the methods used, the data sources, and the statistical models applied. The results should also be compared with published findings to ensure consistency and accuracy. Overall, IPD meta-analyses offer a powerful tool for evidence-based medicine, but their implementation requires careful planning, resource allocation, and collaboration among researchers.