Mean and Mean difference are the two key statistical measures used in the statistical analysis. Both are essential for meta-analysis as well. Mean and Mean difference are used for the interpretation of a large set of values into a single number which explains the heterogeneity and variation among the individual values. However, one of a common challenge in meta-analysis is the unavailability of this data (mean and standard deviation).
Q & A Forum
Systematic Review & Meta Analysis
Q: Risk of Bias in Systematic Reviews and Meta-Analyses
Q: Why is assessing the threat of bias important in methodical reviews (SRs) and meta- analyses (MA)?
A: The trustability of conclusions in an SR or MA depends on the quality of the included studies. Studies with a high threat of bias frequently lead to low- certainty conclusions, whereas studies with a low threat of bias give further robust and dependable findings. assessing bias helps determine the strength and weakness of the review’s conclusions.
Q: What are the most common types of bias?
A: The most frequent types of bias include:
- Selection Bias – Occurs when the study population is not representative of the target population or when groups differ totally beyond the intervention.
- Performance Bias – Arises when care or treatment differs totally between study groups, frequently exaggerating the intervention effect.
- Detection Bias – Happens when outgrowth assessment varies totally between study groups.
- Reporting Bias – Occurs when experimenters widely withhold or report certain results.
- Publication Bias – Happens when studies with positive results are more likely to be published than those with negative or inconclusive results.
- Attrition Bias – Arises when differences in population drop-out rates occur between study groups, leading to deficient data.
Q: What tools are available to evaluate the risk of bias?
A: Several rosters live, acclimatized to different study designs
- Randomized Controlled Trials (RCTs) Cochrane threat of Bias 2 (RoB 2) tool.
- Observational Studies: Newcastle-Ottawa Scale (NOS).
- Non-Randomized Intervention Studies: ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions).
- Prognostic Studies: QUIPS (Quality In Prognosis Studies).
- Prediction Model Studies: PROBAST (Prediction model study Risk Of Bias Assessment Tool).
Each tool evaluates study-specific domains to determine an overall risk-of-bias judgment.
Q: How should risk-of-bias assessments be conducted?
A: To ensure accuracy, each study should be assessed independently by at least two reviewers. This minimizes subjectivity and ensures consistency in bias evaluation, like the selection and data extraction process.