9.4.26 Sensitivity Analysis in Meta-analysis

Describes the Role of Sensitivity Analysis in Meta-Analysis

Meta-analysis is a statistical technique that combines results from multiple studies to estimate the overall effect of an intervention or exposure. Sensitivity analysis is a technique used in meta-analysis to examine the robustness of the findings by testing the influence of different assumptions and methodologies on the results.

Sensitivity analysis allows researchers to explore the impact of different factors on the overall effect estimate, such as study quality, publication bias, inclusion/exclusion criteria, and statistical methods used. This helps to identify potential sources of bias and improves the transparency and reliability of the meta-analysis results.

For example, in a meta-analysis of randomized controlled trials evaluating the efficacy of a new drug for treating a particular disease, sensitivity analysis can be used to examine the effect of excluding studies with a high risk of bias or studies with small sample size. This can help to assess the robustness of the findings and identify potential sources of heterogeneity.

Sensitivity analysis can be performed using various statistical techniques, such as leave-one-out analysis, influence analysis, and jackknife resampling. These methods allow researchers to test the influence of individual studies on the overall effect estimate and examine the stability of the findings.

In summary, sensitivity analysis is a crucial component of meta-analysis that helps to assess the robustness and reliability of the findings. It allows researchers to test the impact of different factors on the overall effect estimate and identify potential sources of bias and heterogeneity.

References:

1. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions. Wiley; 2008.