In a systematic review with a meta-analysis, researchers combine the results of each of the individual studies to create a larger sample size (and therefore greater power), ?then re-run the statistics to capture the true magnitude of the effect. The single-effect measure calculated and reported when the results from all the studies are combined is called what?
The Correct Answer and Explanation is :
In a systematic review with a meta-analysis, researchers combine the results of individual studies to create a larger sample size, thereby increasing statistical power. The single-effect measure calculated and reported when the results from all the studies are combined is called the summary statistic.
Explanation:
A meta-analysis is a statistical technique used to synthesize quantitative data from multiple independent studies addressing a common research question. By combining the results, researchers aim to obtain a more precise estimate of the effect size, which enhances the overall statistical power and provides a clearer understanding of the effect under investigation.
The summary statistic in a meta-analysis represents the overall effect size derived from the combined data of all included studies. This measure provides a single value that encapsulates the magnitude and direction of the effect across studies, offering a more robust and generalizable conclusion than individual studies alone.
Common summary statistics include:
- Risk Ratio (RR): The ratio of the probability of an event occurring in the treatment group to the probability of the event in the control group.
- Odds Ratio (OR): The odds of an event occurring in the treatment group compared to the odds in the control group.
- Mean Difference (MD): The difference in means between the treatment and control groups.
- Standardized Mean Difference (SMD): The difference in means divided by the standard deviation, used when studies measure the same outcome but use different instruments or scales.
These summary statistics are calculated by pooling the effect sizes from individual studies, often using statistical methods that account for the size and quality of each study. This approach allows for a more accurate and reliable estimate of the effect, as it integrates data from multiple sources, thereby reducing the impact of random errors and biases present in individual studies.
It’s important to note that while meta-analysis can provide a more precise estimate of the effect size, it also requires careful consideration of the quality and heterogeneity of the included studies. High heterogeneity among studies can indicate variability in study outcomes, which may affect the validity of the summary statistic. Therefore, researchers must assess the consistency of results across studies and consider potential sources of variability to ensure the robustness of the conclusions drawn from the meta-analysis.