Final answer:
When facing high statistical heterogeneity in a meta-analysis on the effectiveness of mindfulness-based interventions for anxiety in cancer patients, the meta-analyst should investigate sources of heterogeneity, use appropriate models (like the random-effects model), perform subgroup and sensitivity analyses, and consider a qualitative synthesis if necessary.
Step-by-step explanation:
Handling Statistical Heterogeneity in Meta-Analysis
When a meta-analyst finds high levels of statistical heterogeneity in studies examining the effectiveness of mindfulness-based interventions for reducing anxiety in cancer patients, they have several possible courses of action. Firstly, the meta-analyst should investigate the sources of heterogeneity. This involves looking at methodological differences between studies, such as study design, participant characteristics, and intervention types. Another step is to conduct subgroup analyses to explore if specific groups of studies are more homogenous.
Additionally, the meta-analyst might consider using a random-effects model instead of a fixed-effects model to account for variability among studies. Sensitivity analyses can also be conducted to determine how robust the findings are to various inclusion or exclusion criteria. Finally, qualitative synthesis may be necessary when quantitative synthesis is not viable due to high heterogeneity.
Understanding and documenting the level of heterogeneity is crucial as it can affect the interpretation of the meta-analysis results. Therefore, addressing and reporting the heterogeneity is an essential part of the meta-analytic process to ensure accurate and reliable conclusions.